2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDAUD-18.1
Paper Title TOWARDS LISTENING TO 10 PEOPLE SIMULTANEOUSLY: AN EFFICIENT PERMUTATION INVARIANT TRAINING OF AUDIO SOURCE SEPARATION USING SINKHORN’S ALGORITHM
Authors Hideyuki Tachibana, PKSHA Technology, Japan
SessionAUD-18: Audio and Speech Source Separation 5: Source Separation
LocationGather.Town
Session Time:Thursday, 10 June, 13:00 - 13:45
Presentation Time:Thursday, 10 June, 13:00 - 13:45
Presentation Poster
Topic Audio and Acoustic Signal Processing: [AUD-SEP] Audio and Speech Source Separation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In neural network-based monaural speech separation techniques, it has been recently common to evaluate the loss using the permutation invariant training (PIT) loss. However, the ordinary PIT requires to try all N! permutations between N ground truths and N estimates. Since the factorial complexity explodes very rapidly as N increases, a PIT-based training works only when the number of source signals is small, such as N = 2 or 3. To overcome this limitation, this paper proposes a SinkPIT, a novel variant of the PIT losses, which is much more efficient than the ordinary PIT loss when N is large. The SinkPIT is based on Sinkhorn’s matrix balancing algorithm, which efficiently finds a doubly stochastic matrix which approximates the best permutation in a differentiable manner. The author conducted an experiment to train a neural network model to decompose a single-channel mixture into 10 sources using the SinkPIT, and obtained promising results.