No abstract

We address the problem of separating two audio sources from a single channel mixture recording. A novel method called Multi Layered Random Forest (MLRF) that learns a binary mask for both the sources is presented. Random Forest (RF) classifiers are trained for each frequency band of a source spectrogram. A specialized set of linear transformations are applied to a local time-frequency (T-F) neighborhood of the mixture that captures relevant local statistics. A sampling method is presented that efficiently samples T-F training bins in each frequency band. We draw equal numbers of dominant (more power) training samples from the two sources for RF classifiers that estimate the Ideal Binary Mask (IBM). An estimated IBM in a given layer is used to train a RF classifier in the next higher layer of the MLRF hierarchy. On average, MLRF performs better than deep Recurrent Neural Networks (RNNs) and Non-Negative Sparse Coding (NNSC) in signal-to-noise ratio (SNR) of reconstructed audio, overall T-F bin classification accuracy, as well as PESQ and STOI scores. Additionally, we demonstrate the ability of the MLRF to correctly reconstruct T-F bins of the target even when the latter has lower power in that frequency band. ABSTRACTWe address the problem of separating two audio sources from a single channel mixture recording. A novel method called Multi Layered Random Forest (MLRF) that learns a binary mask for both the sources is presented. Random Forest (RF) classifiers are trained for each frequency band of a source spectrogram. A specialized set of linear transformations are applied to a local time-frequency (T-F) neighborhood of the mixture that captures relevant local statistics. A sampling method is presented that efficiently samples T-F training bins in each frequency band. We draw equal numbers of dominant (more power) training samples from the two sources for RF classifiers that estimate the Ideal Binary Mask (IBM). An estimated IBM in a given layer is used to train a RF classifier in the next higher layer of the MLRF hierarchy. On average, MLRF performs better than deep Recurrent Neural Networks (RNNs) and Non-Negative Sparse Coding (NNSC) in signalto-noise ratio (SNR) of reconstructed audio, overall T-F bin classification accuracy, as well as PESQ and STOI scores. Additionally, we demonstrate the ability of the MLRF to correctly reconstruct T-F bins of the target even when the latter has lower power in that frequency band.