Non-negative matrix factorization regularized by recurrent neural networks for audio processing

Patent metadata

Field	Value
Publication number	US-9721202-B2
Application number	US-201414186832-A
Country	US
Kind code	B2
Filing date	Feb 21, 2014
Priority date	Feb 21, 2014
Publication date	Aug 1, 2017
Grant date	Aug 1, 2017

Abstract

Sound processing techniques using recurrent neural networks are described. In one or more implementations, temporal dependencies are captured in sound data that are modeled through use of a recurrent neural network (RNN). The captured temporal dependencies are employed as part of feature extraction performed using nonnegative matrix factorization (NMF). One or more sound processing techniques are performed on the sound data based at least in part on the feature extraction.

First claim

What is claimed is:

1. A method implemented by at least one computing device, the method comprising:
capturing, by the at least one computing device, temporal dependencies in sound data modeled through use of a recurrent neural network (RNN);
extracting, by the at least one computing device, features from the sound data based on the captured temporal dependencies as a negative log-likelihood term of a nonnegative matrix factorization (NMF) cost using nonnegative matrix factorization (NMF); and
performing, by the at least one computing device, one or more sound processing techniques on the sound data based at least in part on the extracted features.

2. A method as described in claim 1 , wherein the recurrent neural network models the temporal dependencies in a temporal sequence of frames in the sound data.

3. A method as described in claim 2 , wherein the frames are configured as part of a magnitude spectrogram.

4. A method as described in claim 2 , wherein the frames are configured as vectors in an activity matrix.

5. A method as described in claim 2 , wherein the recurrent neural network captures long-term temporal dependencies and event co-occurrence in the sound data.

6. A method as described in claim 5 , wherein the long-term temporal dependencies describe a plurality of frames in the temporal sequence that includes a frame, a preceding frame, and at least one other frame.

7. A method as described in claim 1 , wherein the RNN is employed as part of one or more time-dependent restricted Boltzmann machines (RBM) to describe multimodal conditional densities in the sound data.

8. A method as described in claim 1 , wherein the recurrent neural network (RNN) is configured to capture the temporal dependencies by discovering an approximate factorization of an input matrix that describes an observed magnitude spectrogram of the sound data having time and frequency dimensions.

9. A method as described in claim 1 , wherein the NMF is configured to utilize a Cosine distance as a cost distance in nonnegative matrix factorization to generate a likelihood that a respective sound source generated a respective portion of the sound data.

10. A method as described in claim 1 , wherein the temporal information obtained using the recurrent neural network (RNN) is used as part of nonnegative matrix factorization (NMF) to predict plausibility of decomposition of sound data as part of the feature extraction.

11. A method as described in claim 10 , wherein the predicted plausibility of decomposition of the sound data is predicted as a density of activity matrices used as part of nonnegative matrix factorization (NMF).

12. A system comprising:
at least one computing device having a processor and memory configured to perform operations comprising:
capturing temporal dependencies in sound data modeled through use of a recurrent neural network (RNN);
extracting features from the sound data based on the captured temporal dependencies as a negative log-likelihood term of a nonnegative matrix factorization (NMF) cost using nonnegative matrix factorization (NMF); and
performing one or more sound processing techniques on the sound data based at least in part on the extracted features.

13. A system as described in claim 12 , wherein the recurrent neural network captures long-term temporal dependencies and event co-occurrence in the sound data.

14. A system as described in claim 13 , wherein the long-term temporal dependencies describe a plurality of frames in the temporal sequence that includes the frame, the preceding frame, and the at least one other frame.

15. A system as described in claim 12 , wherein the recurrent neural network (RNN) is configured to capture the temporal dependencies by discovering an approximate factorization of an input matrix that describes an observed magnitude spectrogram of the sound data having time and frequency dimensions.

16. A system as described in claim 12 , wherein the NMF is configured to utilize a Cosine distance as a cost distance in nonnegative matrix factorization to generate a likelihood that a respective sound source generated a respective portion of the sound data.

17. A system comprising:
means for capturing temporal dependencies in sound data modeled through use of a recurrent neural network (RNN);
means for extracting features from the sound data based on the captured temporal dependencies as a negative log-likelihood term of a nonnegative matrix factorization (NMF) cost using nonnegative matrix factorization (NMF); and
means for performing one or more sound processing techniques on the sound data based at least in part on the extracted features.

18. A system as described in claim 17 , wherein the recurrent neural network models the temporal dependencies in a temporal sequence of frames in the sound data.

19. A system as described in claim 18 , wherein the frames are configured as part of a magnitude spectrogram.

20. A system as described in claim 18 , wherein the frames are configured as vectors in an activity matrix.

21. A system as described in claim 18 , wherein the recurrent neural network captures long-term temporal dependencies and event co-occurrence in the sound data.

Assignees

• Adobe Systems Inc

Inventors

• Boulanger-Lewandowski Nicolas Maurice
• Mysore Gautham J
• Hoffman Matthew Douglas

Classifications

• G10L21/0272

  Voice signal separating · CPC title

• G10L21/0308

  characterised by the type of parameter measurement, e.g. correlation techniques, zero crossing techniques or predictive techniques · CPC title

• G06N3/044 Primary

  Recurrent networks, e.g. Hopfield networks · CPC title

• G10L21/028

  using properties of sound source · CPC title

• G06N3/09

  Supervised learning · CPC title