Time-frequency masking for convolutive and noisy mixtures

About Multichannel Speech Signal Extraction and Separation Techniques

“…Where ܵ and ‫ݔ‬ are the source and mixture signals respectively, h ୨୧ is a P-point Room Impulse Response (RIR) from source i to microphone j, P is the number of paths between each source-microphone pair and ߬ is the delay of the p th path from source j to microphone i [9][10][11][12][13][14]. This model is the most natural mixing model, encountered in live recordings called echoic mixtures.…”

“…The separating matrix functions as a linear spatial filter or Beamformer that attenuates the interfering signals. ICA can then be applied to separate the convolutive mixtures either in the time domain [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22], in the transform domain [6-7-23], or their hybrid [12][13].…”

“…Sparseness of a signal means that only a small number of the source components differ significantly from zero. One popular approach to sparsity-based separation is T-F masking [13]. This approach is a special case of non-linear time-varying filtering that estimates the desired source from a mixture signal by applying a T-F mask.…”

“…ICA can then be applied to separate the convolutive mixtures either in the time domain [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22], in the transform domain [6-7-23], or their hybrid [12][13].…”

“…One popular approach to sparsity-based separation is T-F masking [13]. This approach is a special case of non-linear time-varying filtering that estimates the desired source from a mixture signal by applying a T-F mask.…”