okay okay no i do not should i start maybe by presenting the project little bit okay okay so historically it was last year professor delambeaux in his c s c w classes asked the student to design a table okay and all the students and guillaume wa was a student of this class was very excited to design such a table okay so he decided to to make more research on this topic so the general topic is about interactive table disappearing computer what is the other one okay na in the general fi field of collaborative computer supported collaborative learning okay so the idea is we have different people collaborating in a pedagogical purpose and we know that when we put a computer in front of them it does not help focusing on collaboration yeah so if we make the computing functionalities disappearing on on the in the furniture in the wall or other things like that we may more efficiently support collaboration than the the learning so i arrived in june something like that a couple of month ago okay guillaume and michael are 2 students that are working 44 months to their master project yeah okay and they start working on 2 different sub projects so guillaume is working on the noise sensitive table it is what we are discussing about today okay so the idea is okay w we have in the lab a notion about root mirroring the idea is if we can provide a feedback to people working together it may help them self regulating their activity their collaboration okay so could be normative or not in our own case we want to have a table that would be accessible by by students at 0 p f l we are ok okay at 0 p f l the learning center will be built in a couple of years so the idea we we will have plenty of rooms like in libraries or meeting rooms where student will arrive and work so if we have this table that is accesi accessible to students if they arrive if we provide a feedback of who is talking instantly the at one moment or through time or the turn taking maybe they will just by seeing an explicit feedback of that they will take profit of it and so do you know which way you give the feedback light or i saw something like that yes okay for for the noise sensitive table project we decided to to take the noise as input okay and give a feedback with leds on the table so this is one of the the possibilities that were offered to us but we decided to do to do it this way the idea is to have peripher peripherical information we do not want people to focus on what is going on what on the feedback so we do not want histogram or very precise information but we will have leds that will be distant there will be 6 centimeters between each led with a blurring glass okay the idea is to have more a kind of art artistic feedback to have a kind of general feeling of what is going on something about okay dance well i guess it is just a restriction we took so so we do not the m amount of money we have to spend otherwise we could have we could imagine to have a whole table of of s well of l e d s yeah and then y you can really start to to some regions of the table so yeah maybe more complex yeah yeah so th yes the the 1st the 1st idea of the project was to have the whole table covered by leds but yeah but for the prototype of this is not this is not the the way we implement it now for practical reasons to the border basically some distance but in fact we have a there was th yes or we kept one paper sheet th there was the the restriction that if we cover the le the table with leds some will not be visible because you have yes okay 0 okay obviously yeah so if there is information displayed under the the sheets the it will not be very useful okay so we decided to have it yes 30 centimeter at each side okay so the 1st shape of the table the shape of the table obviously is one of the factor on that will change the collaboration so but we do not want to start with too many parameters so we start with a table that is approximatively the tab of the table where we are now yeah and we will have a square shape like this that will be here and a 2nd one that would be there yeah so we will have something like this okay like this that will be the our 1st real prototype right okay and the so this l e d modules are being designed now we should have them in a couple of weeks weeks and we will be able to start evaluating the prototypes about the c s c w class that will start at the end at the beginning of november so the student will be asked to design a table so to find the shape of the table to get who the and put that on the on table legs okay and we will so at the beginning we wanted to put this l e d modules in the tables but we will not make it because for solidity reason of the table you know it is not very strong wood so we will rather beam the l e d on the table so we come back to a more richer display b but we will restrict it to a small to have the same display 0 let us led lights that will be distant 6 centimeters between leds okay this is for prac practical reason and also a matter of time so we will simulate our but do not yeah because do not you think with a beam also you have less problems with the sheets does yes but th there are 0 other problems like yes the the information display on the sheets or you do not want to do that yeah so we do not know if it is okay and we have yeah confusing yeah yes and we have the feeling that to have the information embedded in the table make it part of the table true it i do not know we will we we will try we do not really know what will happen with this prototype okay no it is a choice yeah we are very excited about that okay okay maybe before going on on this subject we can talk talk a little bit on yeah i can just explain my project it is completely different from this one well it is also about tables but the idea is if the several students come to a table with their laptops yeah they should be able to to share information easily okay and in fact w what we we are doing now we project another screen on on the table yep and different people are are are able to use their their mouse of their laptop to go to the to the project screen there and the project screen is just another windows machine that is running okay yeah so you just have another f computer more or less you can use but you can use it with your your own mouse and keyboard but physically it is just something flat on the table again yeah exactly huh but in contrary to to having al another keyboard and mouse that only one person can use okay now the different persons can use the the screen at the same time with the with the mouse okay so based on the fact that most of the p f l student have their own laptop the idea is the similar idea is to okay yeah people are coming on a should be able to very easily use some resources offered to them okay so so the idea is to help them socializing organizing their own work and not in a classroom setting but in a social place where they could gather okay yeah so it is different but still yeah it is same i will s still the same subjects it is it is collaborative work okay but different directions it has nothing to do with audio processing and l e d okay yeah so okay okay so maybe we can focus a little bit more on the what we have done about the noise sensitive table yeah tell you about what we are interested in on the so we start so guillaume started by developing the application that get the audio input so we want it to have a modular architecture to be able to improve each layer separately okay so the audio input is quite basic no we u we use sound cards to to get the signals and to detect to on to ha and we have a threshold to detect when someone is talking at least when the noise is at is bigger that specific levels okay and then we get this information about people talking not talking and to say okay this one is talking for x time or has been talking x percent of the 5 last minutes or 2 people are talking together so this is more semantic layer we call that interaction models and so when different conditions are trigger are detected we trigger on the interaction event so very simple one could be one person is talking yeah yeah so it is the easy one and there is a lot of programs now about this implementation is it is not it is not obvious it is still in progress still in progress still in progress anyway i think the the details of the implementation is not that important almost done it is more important that 1st we want to to have some events like 2 people are talking or 11 started and another started and well right more or less broke into the speech of the 1st person and so on okay and then you can start to visualize these different situations so the last layer is about this visualization right w we will we are thinking about the visual grammar meaning that when we have an interaction event that is fired we will associate a way of visuali visualizing it so for instance if someone is talking we can just have a light that will center on the point that is close to him that could be an instant feedback and if he has been talking maybe 70% of the last 5 minutes as me or too long it it may be reflected as the intensity or size of a s a cycle 0 just kidding okay center on the same point yeah and then if there is turn taking we can show waves of light going from me to you or from other people or using the center part of the table to show some something more about the more general dynamic yeah right well that is i guess it is 1st prototype i you see we start with people 2 people talk or and do not and do not they afterwards one could imagine that you for example you can detect that someone is a bit angry or is yelling yeah and wi with different colors with with different colors on on table you could display this this give some feedback or laughing ah so you ha you you might have multiple colors but or yeah yes yep yes that is sorry did not get that in we can change inte so we have 8 by 82 times leds each led is actually 3 leds th m g b led yeah and we can change the intensity yeah alright so it is already quite a rich feedback okay once we get the hardware yeah yes and just a last thing so this table is 2 different things it is one object that will g provide a feedback to the terminal users people collaborating around the table right but it also a tool for us as a researcher to test both the interaction event we want to detect and to test the visualization so we will be able to edit some interaction rules and the visualization grammar okay and we will be able to process to post process some conversation just to see what happens and to have these models on feedback working when people are using the table okay so i think it was no it is quite clear so i guess you want to know what you could do with an array maybe yes yes yes everything from multi switch administration of audio and how you can analyze it what information you can get out of speech and so on yes so yeah i have one or or 2 question i do not know if you want to to begin with the the explanation about the the release maybe we could summarize the question we were thinking about or yeah yeah yeah it is good idea 0 okay just give some questions so i have actually 2 questions that are just practical question abo about my my implementation now i so basically what we want we want to know when a is talking b is talking and no right basic information we need so and the 1st information is about the the noise level n not the noise level the sound level because what you get from from the microphones is a curve and a way the way it is implemented now to detect yes you have a i have packets for let us say this is this is not fixed but i have packets for about alpha 2nd so it is 2nd okay and on these packets i want to know the the sound level the average sound level so the way i do it now is to to take each top of the curves bottoms and just making leverage this yeah but i do not know if it is it is not really the best way to do it but i do not know if you mean the top of the wave form yes okay probably it is it is f be a good idea if you have s 2 with with source like books or web page center give a lot of information how it is audio programming and so on works cetera yeah maybe you you know some at least a good book that is dealing with this stuff 0 maybe we can go through the different questions and then you can organize your own okay so this is this the the 1st question okay i would just add something we are not interested so much in sound level but in the level of the sound that corresponds to voice yes but i think this is 2 different problems the filtering must be must be done before okay it was just a and after that if you have a cleaned curve and you can just apply functionally okay go on and the 2nd question was about filtering and f f t yeah what you can get from it okay and i would add one more general questions it is about because what we can record is sound origins so in po so no sonic points okay yeah but what we are interested in is people yeah so the question is if someone is moving i am talking here i stop talking i am going here i am talking how to detect that it is the same people if we want to integrate information to say these people has spoken 70% of the time in the 5 last minutes okay obviously we need to to know that it is the same people so right now our practical solution is to have one microphone close to every place where people are supposed to sit so we can infer that if a sound is coming in this area it is the same so kind of trick but for the beginning it is enough 0 yes i will just add something is there a way to to from one from one stream to to build 2 streams that correspond to the the voice of one people and the voice of the 2nd yeah yeah yeah okay the separation yes and is possible to do this in real time yeah that is and a last question that is related t our last if i not find another one about people if someone is leaving or if someone maybe it would be something ev quite complex even for you if someone is staying here but is quiet or leaving how can we detect that someone is here but quiet okay well you need a camera i know obviously but how to integrate the information i mean will you have a camera no no and it it this is not not not yet maybe later on but maybe you can detect breathing 0 the wait and the sit yeah yeah that you could do or on the table there should be well yeah but what happens if 0 someone is standing then or putting his bag on the okay but it was just to it is a question okay i am not sure we will talk too much about that today a sound question sound related question okay not directly but it is related well so in a couple of days we will get this the fire wire box okay so we will we have chosen one that has 8 line up so we will be able to have line in sorry line in so we will be able to plug up to 8 microphones with jack plugs to exhale air with the possibility of extending with 8 more x l r microphone with another box okay so you i think guillaume said that is optic something like that or yes yeah the the communication between the the extension and is of the okay so you you need a different you need a microphone with a device inside right no no no in fact that is it is another thing this optic ho how does it work no no no okay well m we have so we ha we have the computer we have the the main box with the 8 line ins and the fire wire port okay and it is fire wire and there is an extension for this this this device connected to it with optic fiber yeah it is optic to have 8 more x l r if we need it ye okay but we are not sure to to buy it now but we can extend it actually it is not really an extension to the 1st box just an indu industry standard protocol between these 2 that is it is another fire wire or not fire wire but well well there is a special protocol to to deliver audio signals and its path through 0 okay but concretely you would use that to get higher quality signals yes y that is all well actually just to have the the icsla e possibly to to branch icsla yes yes well yeah we are not sure about what kind of microphone we should use ah okay okay so we wanted to that is a 1st thing so we wanted to have open possibilities and secondly maybe we will use this box you know to get data yeah i do not know if in another experiment maybe in another project if we want to have comments of different people around the table or 0 elsewhere yeah getting having the possibility of using x l r microphone could be could be neat so everything is open be because for now i am just sampling the sound that 8000 so just something very very rel i could even go to lower okay but this is just to get the the s the noise the sound level and how but if i want to record the conversation with a processing after th you mean you meant 8 kilo sampling frequency yes to to have a better sound quality perhaps it is better to have a x array yeah yeah something like that we use 16 so that is 8 kilo band width 16 kilo sampling frequency yes it it is not bad it is quite decent okay f before answering the questions th there is also a point of precision of localization if you use an array so if you use higher frequencies you can get more precise localization sure sure but maybe you do not need it but what is the the gain in terms of position that i can not answer directly centimeters or more it depends on your set up you know you have to test basically okay m what you get is the real position or just the angle at which the the sound comes y you get the angle the most precise one is the asymmet in the horizontal plane then you also get elevation but it is not very precise it is more of an indication and radius is very bad okay if you use a planar geometry like this one yes sure yeah you can use multiple ones and do some triangulation yes because there there is just 4 microphones that are 8 88 these are only screws okay yes microphones are on bottom okay but there are in in a plan yeah yeah okay because at at t p f n there is a lab that is has done almost the same thing but they have 8 microphone that are at yes the 8 the 8 cor corner of a cube yes a cube okay yeah then you might get better direction in elevation but i am not sure it is really relevant no we are not very interested in no the elevation no no so but what is could be relevant it is not almost it is not always the case with the the if people move forward yeah yes the the position the the radius of the ah the radius yes it could be th then the r the problem is not the geometry it is the i you need more than one basically yes okay with one you will only get direction more than one microphone array yes so because because i the the distance between the the microphone is too is too low to huh okay well so you say this one already works to to tell the the direction direction yeah would it also work if you placed the microphones at at the border of the table you see now you you have it quite central yeah and what happens if if you put them here on around the table would it work as well or it is a different option yeah we have not tried that because we are not going to have the special kind of table you just did not try or more this is not a good example but we have like a box just bring the box and put it yeah so it we could not consider that yeah it is clear this but in your case it could be interesting yeah unless people put something on the microphone yes sure but they will not have nice light lighting on in front of them they would maybe careful yeah yes but i think the the most relevant part is the the other the hardware that synchronize the microphones and that no right but that i am i think after the meeting we can go and talk to olivier about that but okay yes i am not the person for hardware no so maybe w you could present a different work you have done yeah and i do not know how they are yeah i will t i will try to present i guess it corresponds to your questions so you told us it was different pieces more than one integrated device yeah so the the lowest layer would be the 1st 2 questions you asked about how to detect activity currently you are thresholding the t maximum of your wave form right yes not maximum the the average on the on the packets ah ah 0 okay and you are looking for a peak yes the the i take the the b i g when i go up to the the curve i am on the at the local maximum yeah yeah yeah i take it then the next local minimum and taking this this range doing average on it okay what would be the d difference between detecting the average or detecting the maximum yes difference what what would be the difference what is the difference difference is is that if i just it is a kind of filtering already yeah yes yeah and your 2nd question was about f f t what can be done with f f t yes so the approach we are taking here is to answer both questions the same time we use f f t to do detection we do not use the wave form the raw wave form okay so a you have you have 15 implementations we split we split the signal in small frames like 16 millisecond yes 60 okay 16 okay 16 each frame is taking 32 millisecond of signal just to give an idea and there is a one frame every 16 millisecond and they overlap of 50% again huh okay so it is 32 millisecond frames taken each yeah each 16 millisecond well th these are details it is just to give an idea an idea so something like that so why are you overlapping well it is the the c i have a rough idea but what is the main interest because usually when you do f f t y if i am getting into details but you you apply a window to avoid effect so the beginning and the end it it i it is not crappy yeah and it is kind of crappy it is just not very much representative represented because you apply a window which has this shape having window precise okay okay so you use the 1st and last point to detect what is between so they are very yeah no i am saying when you take your signal you take one frame you apply the window and the window is simply coefficients you give and you give higher coefficient to the middle than to the extreme sure so if you do not do overlap y you will lose information at the beginning and the end you lose information okay okay okay okay but you do not have to do overlap i mean it is yes and then i just because we are using an open s an open source framework to do this there is something i do not know if you know i know it calls sphinx that is doing s sorry c m u no is it from c m u yes yeah doing voice recognition yeah yeah and i just use it for the for the for the microphone and the acquisition right so there is already an f f t module implemented in it yeah yeah i did not test it yeah but f f t is only a tool what we do release a phase domain analysis yeah yes sure we only use the phase between the microphones y yes for the for the the position yeah but also for detection also for detection yeah we have a way so i do not think i should get into details but basically the beginning is your signal which you slice into frame you do f f t on each mic and the end is a number of frequency beams which are used by each person to explain roughly so you are s so when you speak speech is wide band so the more active you are the more band width you use so you will get a large value of band width for people who speak and for the others it will random it will be a small value corresponding to breath or a little noise or just background noise kay and so kay yes sure okay but you are talking about the microphone array here yeah cause one problem if you use energy based methods which is probably more the thing you have been using so far yes is that it is not quite related to location no so you can have for one person you can have a high energy signal which is difficult to locate and visa versa strong background noise i think in your case you are quite interested in the location so i would advise to use more phase domain methods so phase well that is something with f f t s once you have the 50 for each frequency you have the magnitude and the phase huh huh and the phase yes so you can compare the phase of the microphones okay and this is directly linked to the direction of the person so the magnitude you do not really use or and for this sometimes we use it but not it is not the 1st thing we use i know it is a bit counterintuitive but maybe i will go t to the board well i did a speech processing course and i am not sure if they a they also learns that that okay yeah okay we more used the face than the magnitude so i guess the r right for a single channel it is not very meaningful but here it is relative phase between the microphones so i will just try to s summarize huh y you have to detect an event on one microphone and know when it is happened on on the other one no that is what i do not do no okay this would be valid for like speech recognition with a single channel that would be a very good idea but if you choose to use multiple channels let us say you have 4 of them with f f t you can just as i said l look at the phase between the microphones at a given frequency so the phase the time between audio signals for for this microphone on this microph it is linked to this value yeah okay basically the time you are mentioning is this value and this is the frequency and this is an angle in radians okay so f f t gives you a measure of these values this i can point to a paper i do not think this is the right place to explain everything but will give you if you look at your table what we have developed here is an approach where th you divide the space in sectors for example 10 sectors around the table and in each sector you will get the value it is related to the number of microphones around no no it is application dependent for example well this is not and on this one you could have a large value on on on those ones small values these are number frequencies which we estimate with several steps from these measures so to conclude what we are doing is we estimate how much of the frequency spectrum you are occupying when you speak or i am occupying in this direction when i speak okay and it turns out that this is quite good to do detection and localization at the same time bec because you know in a sector space there is this much activity okay this is a measure of the activity 0 okay yeah yeah more recently i have worked on prolongating this with a more precise direction evaluation to know where in the sector the person is so that that is not much work once you have done this this is can be this can be done quite quickly and what happen if 2 person are talking at the same time this it just get 2 sectors that are higher than others no then you yeah y well i i use a value but then you will get 2 large values yeah so and do you get something about the distance of the guy that is talking or just a direction sounds no just direction yeah okay th there there is a distance but it is not very very effective well f yeah i it is not i it is an inherent problem to the geometry you use as as you mentioned it is you can for example if you use another array you can intersect lines of direction okay and from from this kind of if information you can let us say have a finger print of the user to know if this is the same user speaking so maybe well not sure because the the i w i would not trust it too much classically what you do is you extract the signal of the person okay one interesting thing is that these numbers are not arbitrary things they represent the number frequencies where a given person is dominant but when you have 2 of them which might be interesting for separation you know already when you have done this processing w which part of the frequencies of the spectrum belongs to this person and to that person which and then it is easy to separate the signals you mean that each person will have a specific frequency because i the way he is talking or his localization yeah range no it is more than both it is a kind of speaking styles both no it is it is it is more an instantaneous this is still instantaneous this is for one time frame yes okay if you look at all your frequencies for example 0 to 4 kilohertz you can split it and say all these parts belong to person one here and all the other parts belong to the other person so this is this is a bit arbitrary because perhaps person 2 will have some frequencies in the range of that is correct but then you can look statistically it wi it will be negligible because the r of of the difference level okay and if you reconstruct the stream from this this range you will get something that is yeah it is you can listen to it yeah okay and then you can do some maybe higher level analysis where you get the pitch of the person or i do not really get your last diagram yes this so it is at one given moment still yeah yeah okay so you can say that f this frequency belongs more to this person and this one more to this one and this is exactly but some frequency may maybe are not used neither by guy the guy one or the guy 2 yeah then it is random and that is why you get these values which are random okay maybe they are using the same frequency yeah so you need to s yeah but this is very rare okay and when it happens one is always masking the other in practice okay okay so and can you detect if someone is laughing or angry d there is kind of signature even if we can not really trust it s 100% of the time yeah no no but i have i have seen quite a few papers i i have n not done it myself i am really on the lowest level but once you have done this as i said you can separate the signals and and do processing on it do do processing okay so you can get pitch rate of speech that is quite easy pitch pitch the timbre en francais it is pitch i will show you this afterwards li okay so for different person this might be quite different at least for male females and rate of speech like if somebody is talking very in a very energetic manner it might be quite fast or just energy also and you can you can this way you can filter perhaps some range that are just very low frequencies for perhaps a noise or one yeah also that might be an issue with people are bringing laptops the yes the fan they will b yeah they might be detected as another another source so you would have to classify the sources as human or machine okay but again yeah i think once you see the spectrum if it is just pure and stable now if you do not really detect the pitch ch can say that no persons also yeah if if there is no pitch so yes but but then this this is very interesting because this way you can detect if someone is talking yeah if someone has an some noise with perhaps putting his his cap on the on the table yeah perhaps if there is a laptop and in one position so all will go and be detected there and you can classif classify and this is yeah yes so w what you are mentioning is a pain for us she very good but for you it might be cause we are only interesting in getting the speech but for you it might be quite quite good yeah okay yes so i have to avoid filtering and smoothing cause as soon as you do that you exclude some of the information yes so it is better to keep it for the latest stage so on top of this there is another part which is more linked to the tracking all this was for one time frame so and the the length of the time frame has an inference of on which parameters yeah you can play with it in speech it is stationary over 1020 millisecond and we make a stationary assumption a local stationary t assumption which allows you to use f f t and blah blah blah okay now in spite of that i know some people use much longer windows which is not a problem but th there i let us say if you use a 12nd window it is a bit too much i think the in fact the the frame lengths will be very very i it is not really meaningful well if you do this yes like some very small words like yes they might be 200 millisecond somehow okay or 300 millisecond they they will not be visible on the on the wavefo okay yeah they might be blurred with silence yes so yes you you might have to play with it just to save processing time like use slightly longer frames but the the the the long the longest frame you would use would be 100 yeah at m at most yeah at most okay simply because i have used 100 as a minimum length of a speech word okay yes yeah so now assuming you have done this for each time i if this is your detail or i should use a different if this is your asymmet at each time you get a direction what is a excuse me so asymmet is your direction in horizontal plane huh it is an angle okay like north south east west okay so where are you represent the phase and yeah no no of this is really different thanks okay i am just saying that once you have done this for a given time frame you can have a direction of the person at least in terms of sector huh huh okay and it is also possible to give a more precise direction quite quickly so this was kind of the lowest layer now this is a layer just above it which might interest you if you repeat that over time you will see patterns for example at 2 different locations kay so you can yeah 2 different person will speak okay so you can cluster those enter when user is and you will see that if it is long enough it is some significant event this can be done quite cheaply yeah yes and before i forg forget it i have one question yeah you say you do the processing after afterwards yeah and what is the the cost yes the the length a cost yeah th 11 or bit longer well i use matlab so it is not perfect okay it is like 34 yes but matlab is very very effective for matrices and yeah but not everything is simple linear okay especially this part it is definitely not linear because you are looking at a maximum energy in a frequency kind of yes the most expensive part is here with matlab i have 34 times real time okay you can do sub optimal processing and for example here i am considering all possible pairs of microphones which is 28 and the processing is directly r proportional to that yes relative yes so you can save on that but then you lose a little bit on precision also i i am using all the small frames with 50% overlap yes i do not think you really need absolutely to do that and even if you use 4 microphones to get not good it is not that good in terms of direction okay yeah i would be careful with that okay like 56 is decent 6 i would say yeah and 8 is is good 8 yeah i get down now to 12 degrees root mean square error so you might not need that there i yes but that is what i mean might not be yeah yes 12 degree is very very small for for our application because we want we really want to detect if th this is the same person that is speaking or not yeah yeah if there is an error of let us say 1015 degrees and but it is capable of seeing it as 2 different person yeah there is not problems with the yeah yeah so it could be sufficient to yeah define enough sectors and okay and the number of sectors is is not dependent of the number of microphones no no so it is arbitrary i use 20 degree sectors cause i had to choose a value okay but it is up to you well otherwise it could also just rely on the frequency band separation and detect that way the different persons and th then you do not really know where around the table these yeah yeah but if there is 22 person the same sector i see well d depends with how you do it but if if you place the the microphones n yeah so that is another way yeah it it is almost like having a lapel yeah that way you can separate where where is the the voice loud with or the loudest yeah i think it is not necessarily a bad idea actually well it is quite different thing and you do not really get a name for it but well if you are able to calibrate your microphones the ones who are closer to the person will get more energy so you can compare that it is possible at the end it it depends a bit what you want so if you are yeah i have experience with that on the side i have done some single channel work which can maybe help cause it seems to me that you are not definite on the geometry right no geometry is not we have not thought a lot about it yeah m this part is quite specific to a microphone array where like they are concentrated in some place like in the middle of the table yes because if we are distributing the microphone all around the table then we come back to the energy solution yeah yeah is it what you say yeah but you can kind of come back to that also with the energy dissolution or with f f t too f f t also f f t okay yeah yeah but th i think what is very important here is the synchronization of the microphones r right yeah yeah right it has to be as good as possible okay i am speaking about it because we wanted to do this table in a mid tech way let us say a cheap way yeah to have not very expensive tables so i do not know if what i have seen w on our website was that the this installation was a bit expensive yeah but i am trying to to to to see what is very important and what is not for true but there are many many things yeah we can also consider directional microphones yes you know no so you can their pattern is not the same depending on the direction where the person is the difference between the so for example if there was a directional microphone pointing there you would get most of my energy but less from you et cetera sure yeah you can also do microphone arrays with directional microphones yes sh i think you have to to might not be relevant but what would be the difference you will not compare each mic with all the other one but just comparing this one with the more or less it is it depends on what you want okay it sh i guess it is completely other approach i mean i do not know okay but what are so yeah so what are this mic for instance the electret mic they are 0 yeah omni directional yeah omni directional electret m i guess so okay these are these are some kinds of and this one it is the same yeah exactly the same yeah yes okay expensive yeah yeah yeah ca can you say a number expensive quite expensive because because of the quality of the the yeah they are high quality mics and they are plug in the on the x l r or i think we can leave this question for olivier yes okay yes okay for the our we will we we will and when you were talking about comparing energy in the case where a microphone would be distributed all around the table you are comparing energy and the time delay yeah no no y you would assume because time delay is is too short to be compared or can be neglected you would neglect it the time delay is is very small it it is only used for getting the a direction when you have a microphone array okay but in that case it is not relevant you would assume that they are roughly synchronized not necessarily very precisely and compare the energy levels but i have no experience with that okay okay yeah now i do not know you do not want to use ah it is almost okay let us have a break yes it is time cool
i am gonnae okay okay 0 i do not should i start maybe by presenting the project a little bit okay so historically it was last year professor dilanboor in his csw class asked the student to design a table and all the students and guillaume was a student of this class i was very excited to design such a table so he decided to make more research on this topic so the general topic is about interactive table disappearing computer what is the other one so in the general field of collaborative computer supported collaborative learning so the idea is we have different people collaborating in a pedagogical purpose and we know that when we put a computer in front of them it does not help focusing on collaboration so if we make the computing functionalities disappearing in furniture in the wall or other things like that we may more efficiently support the collaboration than the learning so i arrived in june something like that a couple of months ago guillaume and mikhail and 2 students that are working for 4 months to the master project and they start working on 2 different sub projects so guillaume is working on the noise sensitive table it is what we are discussing about today so the idea is okay we have in the lab a notion of about group mirroring the idea is if we can provide a feedback to people working together it may help them self regulating their activity their collaboration so it could be normative or not in our case we want to have a table that would be accessible by students at epfl we are at epfl the learning learning center will be built in a couple of years so the idea we will have plenty of rooms like in libraries or meeting rooms where students will arrive and work so if we have this table that is accessible to students if they arrive if we provide a feedback of who is talking instant at one moment or through time or what is the turn taking maybe they will just by seeing an explicit feedback of that they will take profit of it so do you know which way you give the feedback light or i saw or something like that for the nois sensitive table project we decided to take the noise as input and give a feedback with leds on the table so this is one of the possibilities that were offered to us but we decided to do it this way the idea is to have peripheral information we do not want people to focus on what is going on the feedback so we do not want histogram or very precise information but we will have leads that will be distant there will be 6 centimeters between each lead it is a blurring glass the idea is to have more a kind of art artistic feedback to have a kind of general feeling of what is going on something about more ok yes well i guess it is just a restriction we took so we do not depress the amount of money we have to spend otherwise we could imagine to have a whole table of leds and then you can really start to to allume some wichens on the table maybe more complex yeah yeah the 1st idea of the project was to have the whole table covered by nets but yeah but for the prototype of practical reasons this is not the way we implement it now to the border basically some distance in fact we have kept one paper sheet yes there was the restriction that if we cover the table with leds some will not be visible because you have obviously yeah yes so if there is information displayed under the sheets it will not be very useful so we decided to have it yes 30 centimeters okay so the 1st shape of the table the shape of the table obviously is one of the factor that will change the collaboration but we do not want to start with too many parameters so we start with a table that is approximately the table of the table where we are now and we will have a square shape like this that will be here and the 2nd one that would be there so we will have something like this okay like this that will be our 1st real prototype right okay and so this led modules are being designed now we should have them in a couple of weeks and we will be able to start evaluating prototypes about the cscw class that will start at the beginning of november so the student will be asked to design a table so to find the shape of the table of the table to cut hood and put that on table legs and we will so at the beginning we wanted to put this led modules in the tables but we will not make it because from solidity reason of the table it is not very strong wood so we will rather beam the led on the table so we come back to a more richer display but we will restrict it to small to have the same display led lights that will be distant 6 centimeters between lights so we will simulate our this is for practical reason and also a matter of time because yeah not you think with a beam also you have less problems with the sheets yes but there are other problems like the information display on the sheet so we do not know if it is confusing yeah and we have the feeling that to have the information embedded in the table make it part of the table i do not know we will try we do not really know what will happen with this prototype we are very excited about that maybe before going on the subject we can talk a little bit of yeah i can just explain my project it is completely a different form this one well it is also about tables but the idea is if several students come to a table with their laptops they should be able to share information easily okay and in fact what we are doing now we project another screen on the table yeah and different people are able to use their mouse of their laptop to go to the project screen there okay and the project screen is just another windows machine that is running so you just have another computer more or less you can use but you can use it with your own mouse and keyboard but physically it is just something flat on the table again yeah but in contrary to having another keyboard and mouse only one person can use now the different persons can use the screen at the same time with the mouse okay so based on the fact that most of the upfl students have their own laptop the idea is the similar idea is to okay people are coming and should be able to very easily use some resources offered to them so the idea is to help them socializing organizing their own work and not in a classroom setting but in a social place where they could gather okay yeah so it is different but still yeah still the same subject it is called a work but different directions it has nothing to do with audio process audio processing and leds okay okay so maybe we can focus a little bit more on what we have done about the noise sensitive table yeah tell you about what we are interested in and so we start so guillome started by developing the application that get the audio input so we wanted to have a modular architecture to be able to improve each layer separately so the audio input is quite basic now we use sound counts to get the signals and to detect and we have a threshold to detect when someone is talking at least when the noise is bigger than a specific level then we get this information about people talking not talking to say okay this one is talking for x time or has been talking x percent of the 5 last minutes or 2 people are talking together so this is more semantic layer we call that interaction models and so when different conditions are triggered we trigger and detected we trigger and interaction event so a very simple one could be one person is talking so it is a easy one and there is a lot of problems now about this implementation it is still in progress it is not obvious still in progress anyway i think the details of the implementation is not that important it is more important that 1st you want to have some events like 2 people are talking or one one started and another is the important and others started and more or less broke into the speech of the 1st person and so on and then you can start to visualize these different situations so the last layer is about this visualization we are thinking about visual grammar meaning that when we have an interaction event that is fired we will associate a way of visualizing it so for instance if someone is talking we can just have a light that will center on a point that is close to him that could be an instant feedback and if he has been talking maybe 70% of the last 5 minutes as me it may be reflected as the intensity of size of a cycle okay center on the same point and then if there is turn taking we can show waves of light going from me to you or from other people or using the center part of the table to show something more about the more general dynamic i guess it is the 1st prototype you see we start with people do people talk or do not they afterwards one could imagine that you for example you can detect that someone is a bit angry or is yelling and with different colors with different colors on the table you could display this give some feedback you might have multiple colors yes sorry i did not get that we can change so we have 8 x 8 2 times lengths each led is actually 3 leds lgb lad and we can change the intensity so it is already quite a rich feedback once we get the hardware yes and just the last thing so this table is 2 different things it is one object that will provide feedback to the terminal users people collaborating around the table but it is also a tool for us as a way for us as a researcher to test both the interaction events we want to detect and to test the visualization so we will be able to edit some interaction rules and visualization grammar and we will be able to process some process some conversation just to see what happens and to have these models and feedback working when people are using the demo okay so i think it is quite clear so i guess you want to know what you could do with an array maybe yes yes yes i guess everything from what is the situation of audio and how you can analyze it what information you can get out of speech and so on i have one or 2 questions i do not know if you want to begin with the explanation about the surveys maybe we could summarize the question we were thinking about yeah it is good idea okay just give some questions so i have actually 2 questions that are just practical questions about my about my implementation now so basically what we want we want to know when a is talking b is talking and the basic information we need so the 1st information is about the noise level not the noise level the sound level because what you get from the microphones is a curve and the way the way it is implemented now to detect yes you have i have packets for let us say this is not fixed but i have packets for about half a 2nd so let us say a 2nd and on this packet i want to know the sound level the average sound level so the way i do it now is to take each top of the curves bottoms and just making an average of this but i do not know if it is not really the best way to do it but i do not know if you mean the top of the waveform yes okay probably it would be a good idea if you have to say with resources like books or web pages that give a lot of information how this audio programming and so on works that yeah maybe you know some at least a good book that is dealing with stuff maybe we can go through the different questions and then you can organize your so this is this is the 1st question i would just add something we are not interested so much in sound level but in the level of the sound that corresponds to voice yes but i think this is 2 different problems the filtering must be done before okay and after that if you have a clean curve you can just apply it functioning and the 2nd question was about filtering in fft what you can get from it okay and i would add one more it is about because what we can record is sound origins sonar sonic points but what we are interested in is people so the question is if someone is moving i am talking here i stop talking i am going here i am talking how to detect that it is the same people if we want to integrate information to say these people has spoken 70% of the time in the 5 last minutes obviously we need to know that it is the same people so right now our practical solution is to have one a microphone close to every place where people are supposed to sit so we can infer that if a son is coming in this area it is the same it is a kind of trick for the beginning it is enough yes i will just add something is there a way to to from one stream to to be 2 to be 2 streams that correspond to the voice of friend people and the voice of the 2nd yeah clean of the separation yes and it is possible to do this in real time yeah yeah and the last question that is related to last if i do not find another one about people if someone is living or if someone maybe it would be something quite complex even for you if someone is staying here but is quiet or living how can we detect that someone is here but quiet well you need a camera i know obviously how to integrate the information i mean will you have a camera no not yet maybe later or later maybe you can detect breathing or the weight on the seat yeah that you could do or on the table they should be well yeah but what happens if someone is standing then 0 putting his bag under okay but it was just 2 it is a question i am not sure we will talk too much about that today i will just if it is a sound question sound related question okay not directly but it is related wow so in a couple of days we will get this the fire wire box okay so we have chosen one that has 8 line up so we will be able to line in so we will be able to plug up to 8 microphones with a jack plugs to excel air with the possibility of extending with 8 more xcelair microphone so you i think guillaume said that is optic something like that yes the communication between the extension so you need a different you need a microphone with a device inside right no no in fact that how does it no no no it is not a thing this optic so we have the computer we have the main box with the 8 line in the fire wire port and there is an extension for this device it is connected to it with optic fiber to have 8 more xlr if we need it okay we are not sure to buy it now but we can extend it actually it is not really an extension to the 1st box it is just an industry standard protocol between these 2 it is another fire wire well not firewire but well there is a special protocol to deliver audio signals and but concreetly you would use that to get higher quality signals yes that is all yes just to have the xlar we are not sure about what kind of microphone we should use ah okay so we wanted to that is a 1st thing so we wanted to have open possibilities and secondly maybe we will use this box to get data i do not know if in another experiment maybe in another project if we want to have comments of different people around the table or elsewhere getting having the possibility of using xlr microphone could be neat so everything is open because for now i am just sampling the sound that 8000 so just something very very i could even go to lower but this is just to get the noise the sound level but if i want to record the conversation with a processing you might add kilo sampling frequency yes to have a better sound quality and perhaps it is better to have an xera yeah something like that we use 16 so that is 8 k bandwidth 16 kilo sampling frequency it is not bad yeah it is quite decent okay before answering the questions there there is also a point of precision of localization if you use an array so if you use higher frequencies you can get more precise localization sure but maybe you do not need it what is the gain in terms of position that i can not answer directly centimaltors or more it depends on your setup you know you have to test basically what you get is the real position or just the angle at which the sound comes comes you get the angle the most precise one is the azimuth in the horizontal plane then you also get elevation but it is not very precise it is more of an indication and radius is very bad okay if you use a planar geometry like this one yes sure yeah you can use multiple ones and do some triangulation yes because there is just 4 microphones that are 8 8 these are only screws okay but they are in a plan yeah yeah because at epfl there is a lab that has done almost the same thing but they have 8 microphones that are at yes the 8 the 8 call a corner of a cube okay yeah then you might get a better direction in elevation but i am not sure it is really relevant no we are not the elevation no but it could be relevant it is not always the case but the if people move forward yes the position the radius of the ah the radius yes it could be the problem is not the geometry it is the you need more than one basically yes okay with one you will only get direction more than one microphone array yes because the distance between the microphone is too is too low to well so you say this one already works to tell the direction would it also work if you placed the microphones at the border of the table you see now you have it quite essential and what happens if you put them here around the table would it work as well it is a different option yeah you just did not try or we have not tried that because we are not going towards a special kind of table more this is not a good example but we have like a box you just bring the box and put it yeah so we could not consider that but in your case it could be interesting yeah unless people put something on the microphone yes sure but they will not have nice light lighting on in front of them yes but i think the most driven part is the other hardware that synchronize the microphones and the the microphones and that right but that i am i think after the meeting we can go and talk to olivier about that yes i am not the person for hardware so maybe you could present a different work yeah you have done and i do not know how they are yeah i will try to present i guess it corresponds to your questions so you told us it was different pieces yeah the one integrated device so the the lowest layer would be the 1st 2 you asked about how to detect activity currently you are thriss holding the maximum of your waveform right not maximum the average on the packets okay and you are looking for a peak yes i take the when i go up to the curve i am at a local maximum i take it then the next local minimum and taking this range what would be the difference between the average or detecting the maximum yes what would be the difference what is the difference the difference is that if i just it is a kind of filtering already yes and your 2nd question was about fft what can be done with fft done with fft yes so the approach we are attacking here is to answer both questions at the same time we use fft to do detection we do not use the waveform the raw waveform so your fft implementation is now we split the signal in small frames like 16 milliseconds 16 16 each frame is taking 32 milliseconds of signal just to give an id there is one frame every 16 milliseconds and they overlap of 50% okay so it is 32 milliseconds frames taken each each 16 milliseconds well these are details it is just to give an id an id an id yeah something like that so why are you overlapping well it is i have a rough id but because usually when you do fft you are getting into details but you apply a window to avoid aliasing effect so the beginning and the end the end is kind of crappy it is not crappy it is just not very much represented because you apply okay which has this shape having window so you use the 1st and last point to detect what is between no i am saying when you take your signal you take one frame you apply the window and the window is simply coefficients you give and you give higher coefficient to the middle than to the extreme so if you do not do overlap you lose information you will lose information at the beginning and the end at the end okay okay okay but you do not have to do overlap i mean it is yes just because we are using an open source framework to do this there is something i not know if you know it it calls sphinx that is doing sorry is it from cmu yes yeah doing voice recognition and i just use it for the framework for the microphone and the acquisition right so there is already an fft module implemented in it i did not test it but fft is only a tool yes sure what we do really is phase domain analysis we only use the face between the microphones yes for the position you see yeah but also for detection also for detection yeah we have a way so i do not think i should get into details but basically the beginning is your signal which you slice into frame you do fft on each mic and the end is a number of frequency beans which are used by each person to explain roughly so you are so when you speak speech is wide band so the more active you are the more bandwidth you use so you will get a large value of bandwidth for people who speak and for the others it will be random it will be a small value corresponding to breath or on little noise or just background noise okay okay yes sure okay but you are talking about the microphone array here yeah because one problem if you use energy based methods which is probably more the things you have been using so far is that it is not quite related to location no so you can have a strong background noise for one person you can have a a high energy signal which is difficult to locate and vice versa i think in your case you are quite interested in the location so i would advise to use more phase domain methods so phase that is something with ffts once you have the fft for each frequency you have the magnitude and the phase so you can compare the phase of the microphones and this is directly linked to the direction of the person so the magnitude you do not really use sometimes we use it but it is not the 1st thing we use i know it is a bit counterintuitive but maybe i will go to the book well i did a speech for saying cause and i am not sure if they also we also learned that we more use the face than the magnitude right for a single channel it is not very meaningful but here it is a relative phase between the microphones so i will just try to you have to detect an event on one microphone and know when it happened on the other one no that is what i do not do okay this would be valid for speech recognition like speech recognition with a single channel that would be a very good idea but if you choose to use multiple channels let us say you have 4 of them with fft you can just as i said look at the phase between the microphones at a given frequency so the phase the time between audio signals this microphone it is linked to this value yeah okay basically the time you are mentioning is this value and this is the frequency and this is an angle in radiance okay so fft gives you a measure of these values this i can point to a paper i do not think this is the right place to explain everything but we will give you if you look at your table what we have developed here is an approach where you divide the space in sectors for example 10 sectors around the table and in each sector you will get a value it is related to the number of microphones or all no it is application dependent for example this is not on this one you could have a large value on those ones small values these are number of frequencies which we estimate with several steps from these measures so to conclude what we are doing is we estimate how much of the frequency spectrum you are occupying when you speak or i am occupying in this direction when i speak and it turns out that this is quite good to do detection and localization at the same time because you know in a sector of space there is this much activity activity okay this is a measure of the activity yeah yeah more recently i have worked on prolongating this with a more precise direction evaluation to know where in the sector the person is so that is not much work once you have done this this can be done quite quickly what happens if 2 persons are talking at the same time we just get 2 sectors that are higher than a thing well i use a value but then you will get 2 large values and do you get something about the distance of the guy that is talking or just the direction no just direction there is a distance but it is not very effective well yeah it is not a it is an in herent problem to the geometry you use you can for example if you use another array you can intersect lines of direction okay and from this kind of information you can let us say have a fingerprint of the user to know if this is the same user that is speaking speaking maybe because the i would not trust it too much classically what you do is you extract the signal of the person one interesting thing is that these numbers are not arbitrary things they represent the number of frequencies where a given person is dominant so when you have 2 of them which might be interesting for separation you know already when you have done this processing which part of the frequency of the spectrum belongs to this person and to that person and then it is easy to separate the signals you mean that each person will have a specific frequency because the way it is talking or is localization no it is small that it is a kind of speaking style it is more an instantaneous this is still instantaneous this is for one time frame if you look at all your frequencies for example 0 to 4 khz you can split it and say all this parts belong to person one here and all the other parts belong to the other parts belong to the other belong to the other person this is a bit arbitrary because perhaps person 2 will have some frequencies in the range of that is correct but then you can look statistically it will be negligible okay because of the difference level and if you reconstruct the stream from this range you will get something that is yeah you can listen to it okay and then you can do some maybe higher level analysis where you get the pitch of the person i do not really get your last diagram yes so it is at one given moment still yeah yeah okay so you can say that this frequency belongs more to this person and this one more to this one exactly but some frequency may maybe are not used neither by the guy one or the guy 2 then it is random and that is why you get these values which are random and maybe they are using the same frequency yeah so you need to yeah but this is very rare okay and when it happens one is always masking the other in practice okay so and can you detect if someone is laughing or angry or is there a kind of signature even if we can not really trust it no no but i have seen quite a few papers i have not done it myself i am really on the lowest level but once you have done this as i said you can separate the signals and and do processing okay so you can get pitch right of speech that is quite easy pitch i will show you this after once timbre in french right okay so for different persons it might be quite different at least for male females and the rate of speech like if somebody is talking very in a very energetic manner it might be quite fast or just energy or so you can this way you can filter perhaps filter perhaps some range that are just very low frequencies for a noise yeah also that might be an issue if people are bringing laptops the fan they will yeah they might be detected as another source so you would have to classify the sources as human or machine okay but again yeah i think once you see the spectrum if it is just pure and stable yeah if you do not really detect the pitch you can say that also yeah it is not it is not so yes but this is very interesting because this way you can detect if someone is talking if someone has some noise with perhaps putting his cup and the table yeah perhaps if there is a laptop and one position so all will go and be detected there and you can classify in this season yes yes so what you are mentioning is a pain for us but for you it might be because we are only interesting in getting a speech but for you it might be quite good so i have tried to avoid filtering and smoothing because as soon as you do you do that you exclude some of the information yes so it is better to keep it for the latest stage so on top of this there is another part which is more linked to the tracking all this was for one time frame so and the length of the time frame is an influence on which parameters yeah you can play with it in speech it is stationary over 10 20 milliseconds and we make a stationary assumption a local stationarity assumption which allows you to use fft and blah blah blah okay now in spite of that i know some people use much longer windows which is not a problem but let us say if you use a 12nd window it is a bit too much i think in fact the frame lengths will be very ecially meaningful well if you do this yes like some very small words like yes they might be 200 milliseconds or 300 milliseconds they will not be visible on the yeah they might be blurred with silence yes so yes you might have to play with it just to save processing time like you slightly longer frames but the longest frame you would lose would be 100 yeah at most yeah simply because i have used 100 as a minimum length of speech word okay yes now assuming you have done this for each time if this is your data or i should use a different if this is your azimut at each time you get a direction what is azimuth so azimuth is your direction in horizontal plane it is an angle like north south east west so where you represent the phase yeah no no this is really different i am just saying that once you have done this for a given time frame you can have a direction of the person at least in terms of sector and it is also possible to give a more possible to give a more precise direction quite quickly so this was kind of the lowest layer now this is a layer just above it which might interest you if you repeat that over time you will see patterns for example at 2 different locations okay so you can 2 different persons will speak so you can cluster those into a one user yes and you will see that if it is long enough it is some significant event this can be done quite cheaply yes before i forget it i have one question you say you do the processing after afterwards and what is the cost the cost yes the length or a bit longer well i use matlab so it is not perfect it is like 3 4 but matlab is very effective for matrices and yeah but not everything is simple linear especially this part it is definitely not linear because you are looking at the maximum energy in a frequency kind of the most expensive part is here with matlab i have 34 times real time okay you can do sub optimal processing and for example here i am considering all possible pairs of microphones which is 28 and the processing is directly relative proportional to that so you can save on that but then you lose a little bit on precision also i am using all these small frames with 50% overlap i do not think you really need absolutely to do that and even if you use the 4 microphones it gets it is not that good in terms of good in terms of direction okay i would be careful with that like 5 6 is decent 6 i would say yeah and 8 is good 8 yeah i get down now to one 2 degrees root mean square error so there is yes you might not need that that is what i mean i mean might not be yes one 2 degrees is very very small for our application because we really want to detect if this is the same person that is speaking or not if there is an error of let us say 10 15 degrees but it is capable of saying it is 2 different person there is not problems about it so it could be sufficient to you define enough sectors and okay and the number of sectors is not dependent of the number of microphones no so it is arbitrary i use 20 degree sectors because i had to choose the value but it is up to you otherwise you could also just rely on the frequency band separation detect that way they are different persons and then you do not really know where around the table is but there is 22 persons in the same sector you see well it depends with how you do it but if you place the microphones so that is another way yeah it is almost like having a lapel yeah that way you can separate where where is the voice louder it is not necessarily a bad idea actually it is quite different thing and you do not really get an ankle but well if you are able to calibrate your microphones the ones who are closer to the person will get more energy so you can compare that is possible it depends a bit what you want if you are yeah i have experience with that on the side i have done some single channel work which can maybe help because it seems to me that you are not definite on the geometry right no geometry is not we have not thought about it this part is quite specific to a microphone array where they are concentrated in some place like in the middle of the table yes because if we are distributing the microphone all around the table then we come back to the energy solution is it what you see yeah but you can kind of come back to that also with the energy solution or with fft 2 fft also yeah but i think what is very important here is the synchronization of the microphones right yeah yeah it has to be as well as possible i am speaking about it because we want it to do this table in a mid tech way let us say a cheap way yeah to have not very expensive tables so i do not know if what i have seen on your website was that this installation was a bit expensive yeah but i am trying to there are many many things to see what is very important and what is not you can also consider directional microphones yes you know no the difference between the their pattern is not the same depending on the direction where the person is for example if there was a directional microphone pointing there you would get most of my energy but less from you etc you can also do microphone arrays with directional microphones yes i think you have to might not be relevant but what would be the difference you will not compare each mic with all the other one but just comparing this one with the more it is it depends on what you want i mean yes what are the other approach so yeah so what are this mic for instance there are electric mic but only yeah only directional yeah electric okay i guess yeah these are some kinds of it is the same yeah exactly the same yeah expensive yeah yeah expensive can you say can you say in a normal yeah there are high quality mics and they are plugged on the xlr or i think we can leave this question for olivier yes okay okay for the hardware and when you were talking about comparing energy in the case where microphone would be distributed all around the table you are comparing energy and the time delay no no you would because the time delay is too short to be compared or can be neglected you would neglect it the time delay is very small it is only used for getting a direction when you have a microphone array but in that case it is not relevant you would assume that they are roughly synchronized not necessarily very precisely and compare the energy levels but i have no experience with that okay okay no i do not know you do not want to use ah it is almost let us have a break it is time cool okay no well it is interesting i think i can not even more we can not even when we have got to put it up until the debut we have no more to pose a question now because if we are not on the video it is really i had to reticely my of physics and all that in