A system and method for constructing training dictionaries with multichannel information. An exemplary method takes into account the effect of the acoustic path while training multichannel acoustic data. A method that uses different time-frequency resolutions in machine learning training is also presented.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for improving the separation of audio sources comprising: obtaining first data from a training source signal in a sensor; transforming the first data to the time-frequency domain using a first window length and obtaining a first representation; transforming the first data to the time-frequency domain using a second window length and obtaining a second representation; determining elements of a training dictionary using one or more signal processing algorithms from the first and second representations; wherein the first and second window lengths are different; storing the training dictionary elements; using the training dictionary elements to process second data obtained by the sensor; and audibly outputting a signal related to the processed second data. 2. The method of claim 1, wherein the first and second representations are mapped to a time-frequency domain with common frequency resolution before determining the training dictionary elements. 3. The method of claim 1, where the source signal is single channel or binaural or multichannel audio signal. 4. The method of claim 1, where the signal processing algorithms are one or more of non-negative matrix factorization, non-negative tensor factorization, independent component analysis, principal component analysis, singular value decomposition, dependent component analysis, low-complexity coding and decoding, stationary subspace analysis, common spatial pattern, empirical mode decomposition, tensor decomposition, canonical polyadic decomposition, higher-order singular value decomposition, and tucker decomposition. 5. The method of claim 1, where the training dictionary is used for source separation. 6. The method of claim 1, where the representations can be obtained with any one or more of a short-time Fourier transform (STFT), a wavelet transform, a polyphase filterbank, a multi rate filterbank, a quadrature mirror filterbank, a warped filterbank, an auditory-inspired filterbank, and a tree-structured array of filterbanks. 7. The method of claim 1, where the data are captured in live or studio music events from one or more microphones. 8. A method for improving the separation of audio sources comprising: capturing one or more sound sources from two or more microphones and creating a first set of two or more time-domain signals; storing the first set of time-domain signals; removing silence from the first set of time domain signals; transforming the first set of time domain signals via a time-frequency transform and creating two or more representations; stacking the representations and creating a new representation; extracting training dictionary elements using one or more signal processing algorithms from the new representation; storing the training dictionary elements; using the training dictionary elements to process a second set of two or more time-domain signals obtained by the two or more microphones; and audibly outputting the processed second set of time domain signals. 9. The method of claim 8, where the time-domain signals are single channel or binaural or multichannel audio signals. 10. The method of claim 8, where the signal processing algorithms are one or more of non-negative matrix factorization, non-negative tensor factorization, independent component analysis, principal component analysis, singular value decomposition, dependent component analysis, low-complexity coding and decoding, stationary subspace analysis, common spatial pattern, empirical mode decomposition, tensor decomposition, canonical polyadic decomposition, higher-order singular value decomposition, and tucker decomposition. 11. The method of claim 8, where the training dictionary is used for source separation 12. The method of claim 8, where sound sources are captured in live or studio music events. 13. A system that improves the separation of audio sources comprising: two or more microphones that capture one or more sound sources; a transform that creates a first set of two or more time-domain signals; memory adapted to store the first set of time-domain signals; a processor adapted to remove silence from the first set of time domain signals; a transformer that transforms the first set of time domain signals via a time-frequency transform and creates two or more representations; one or more signal processing algorithms that stack the representations to create a new representation and extract training dictionary elements from the new representation; storage that stores the training dictionary elements, the training dictionary elements used to process a second set of two or more time-domain signals obtained by the two or more microphones; and at least one speaker that audibly outputs the processed second set of time domain signals. 14. The system of claim 13, where the time-domain signals are single channel or binaural or multichannel audio signals. 15. The system of claim 13, where the signal processing algorithms are one or more of non-negative matrix factorization, non-negative tensor factorization, independent component analysis, principal component analysis, singular value decomposition, dependent component analysis, low-complexity coding and decoding, stationary subspace analysis, common spatial pattern, empirical mode decomposition, tensor decomposition, canonical polyadic decomposition, higher-order singular value decomposition, and tucker decomposition. 16. The system of claim 13, where the training dictionary is used for source separation 17. The system of claim 13, where sound sources are captured in live or studio music events. 18. The system of claim 13, wherein the output processed second set of time domain signals have improved separation. 19. The system of claim 13, wherein the representations can be obtained with any one or more of a short-time Fourier transform (STFT), a wavelet transform, a polyphase filterbank, a multi rate filterbank, a quadrature mirror filterbank, a warped filterbank, an auditory-inspired filterbank, and a tree-structured array of filterbanks. 20. The system of claim 13, wherein an effect of an acoustic path is accounted for by the signal processing algorithms.