Various exemplary aspects are directed to acoustic context recognition apparatuses and methods involving isolating and identifying context(s) of an acoustic environment. In one exemplary embodiment, source audio is converted into audio spectrograms, each spectrogram indicative of a period of time. The series of spectrograms are analyzed to identify audio patterns, over a period of time, which are indicative of an environmental context of the source audio. In many embodiments of the present disclosure, acoustic context recognition also includes comparing the identified audio patterns to known environmental contexts.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving an audio signal spectrogram, having adjacent pixels, indicative of an audio input; constructing a plurality of local binary patterns (LBP) based on a comparison of the adjacent pixels of the spectrogram; creating, for each of a plurality of blocks of the spectrogram, a respective LBP histogram that is based on a number of times different LBPs occur in a corresponding block of the plurality of blocks; identifying clusters of LBP histograms from the respective LBP histograms; generating, for each of the clusters, a code word representing a corresponding cluster; creating a codebook histogram based upon correspondence between the LBP histograms and the code words representing the clusters; and classifying, using a machine learning model, the codebook histogram to identify context for the received audio signal spectrogram. 2. The method of claim 1, wherein the respective LBP histogram, for each of the plurality of blocks of the spectrogram, is indicative of an acoustic context of the audio input over a period of time. 3. The method of claim 1, wherein the audio signal spectrogram is a linear spectrum representation of the audio input. 4. The method of claim 1, wherein the plurality of LBPs are indicative of pixel values, threshold transitions, and respective pixel locations in each of the plurality of blocks of the spectrogram. 5. The method of claim 1, wherein creation of the codebook histogram further includes: determining a cosine distance between the LBP histograms and the code words using the cosine distance between the LBP histograms and the code words, and assigning the LBP histograms to code words in the codebook histogram based upon the cosine distance. 6. The method of claim 1, further comprising the step of interpolating, for each of the LBP histograms, by using pixel values outside a respective block for each of the LBP histograms. 7. The method of claim 1, wherein each respective LBP histogram is a histogram of a relationship between each of the plurality of blocks of the spectrogram as defined by a plurality of uniform configurations and a non-uniform configuration. 8. The method of claim 1, wherein the plurality of blocks of the spectrogram are defined by blocks within 0 Hz-900 Hz frequency-band, blocks within 900 Hz-2000 Hz frequency-band, and blocks within 2000 Hz-8000 Hz frequency-band. 9. The method of claim 1, wherein the LBP is defined by an algorithm: LBP P , R = ∑ i = 0 P - 1  f  ( g i - μ )  2 P ,  f  ( x ) = { 1 , x ≥ μ 0 , x < μ , wherein gi is a neighboring pixel's value, μ is a mean over a particular block, P is a number of pixels in the particular block, R is a radius of a neighborhood and wherein coordinates of gi are R cos(2πi/P), sin(2πi/P). 10. A method comprising the steps of: receiving an audio signal spectrogram indicative of an audio input; constructing a plurality of local binary patterns (LBP) based on a comparison of adjacent pixels of the spectrogram; creating, for each of a plurality of blocks of the spectrogram, a respective LBP histogram that is based on a number of times different LBPs occur in a corresponding block of the plurality of blocks; identifying clusters of LBP histograms from the respective LBP histograms for each of the plurality of blocks of the spectrogram; generating, based upon the clusters, a codebook containing a plurality of code words; mapping the LBP histograms to the code words of the codebook to create a codebook histogram; and training a machine learning algorithm using the LBP histograms as features therefore. 11. The method of claim 10, further comprising interpolating, for each of the LBP histograms, using pixel values outside a respective block for each of the LBP histograms. 12. The method of claim 10, wherein each respective LBP histogram maps each of the plurality of blocks of the spectrogram into a plurality of uniform configurations and a non-uniform configuration. 13. The method of claim 10, wherein the plurality of blocks of the spectrogram are defined by blocks within 0 Hz-900 Hz frequency-band, blocks within 900 Hz-2000 Hz frequency-band, and blocks within 2000 Hz-8000 Hz frequency-band. 14. The method of claim 10, wherein the machine learning algorithm is within a support vector machine. 15. The method of claim 10, wherein the LBP is defined by an algorithm: LBP P , R = ∑ i = 0 P - 1  f  ( g i - μ )  2 P ,  f  ( x ) = { 1 , x ≥ μ 0 , x < μ , where gi is a neighboring pixel's value, μ is a mean over a particular block, P is a number of pixels in the particular block, R is a radius of a neighborhood and wherein the coordinates of gi are R cos(2πi/P), sin(2πi/P).