Method for Restoring Spectral Components in Denoised Speech Signals

• US 20120143604A1
• Filed: 12/07/2010
• Published: 06/07/2012
• Est. Priority Date: 12/07/2010
• Status: Abandoned Application

• Current Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Inventors: Singh, Rita
• Application Number:
  US12/962,036
• Publication Number:
  US 20120143604A1
• Time in Patent Office: Days
• US Class Current: 704/226
• CPC Class Codes: G10L 21/0208 Noise filtering
• CPC Class Codes: G10L 21/0272 Voice signal separating
• CPC Class Codes: G10L 21/038 using band spreading techniques

Abstract

Spectral components attenuated in a test denoised speech signal as a result of denoising a test speech signal are restored by representing a training undistorted speech signal as a composition of training undistorted bases, and representing a training denoised speech signal as a composition of training distorted bases. The test denoised signal decomposed as a composition of the training distorted bases. The undistorted test speech signal is then estimated as the composition of the training undistorted bases that is identical to the composition of training distorted bases.

Claims

1. A method for restoring spectral components attenuated in a test denoised speech signal as a result of denoising a test speech signal, comprising:

representing a training undistorted speech signal as a composition of training undistorted bases;

representing a training denoised speech signal as a composition of training distorted bases;

decomposing the test denoised signal as a composition of the training distorted bases;

estimating the undistorted test speech signal as the composition of the training undistorted bases that is identical to the composition of training distorted bases.

2. The method of claim 1, wherein a process for producing the test denoised speech signal is unknown, and further comprising:

modeling the process by an ideal lossless denoising function to produce a denoised signal that is hypothetically lossless, and passing the denoised signal through a distortion function that attenuates the spectral components.

4. The method of claim 1, wherein all the bases are additive, and each bases is associated with a weight.

5. The method of claim 2, wherein the distortion function transforms any basis independently of any other bases.

6. The method of claim 1, further comprising:

representing all speech signals as magnitude spectrograms that are obtained by determining magnitudes of short-time Fourier transforms (STFTs) of the speech signals.

7. The method of claim 1, wherein the training undistorted bases and the training distorted bases are determined by a joint analysis of magnitude spectrograms of training data, wherein the training data comprise pairs of recordings, where each pair includes a clean speech signal, and an artificially corrupted version of the clean speech signal that has been corrupted by adding of noise and then denoising the corrupted version.

8. The method of claim 7, wherein samples of the clean speech signal, and the artificially corrupted and denoised version of the clean speech signal are time aligned.

9. The method of claim 8, wherein the undistorted training bases and the distorted training bases are determined by joint analysis of the pairs of recordings.

10. The method of claim 1, wherein the training undistorted bases and the training distorted bases are determined using an example-based model, and wherein the training undistorted bases and the training distorted bases are randomly selected from among magnitude spectral vectors for the training undistorted bases and the training distorted bases.

11. The method of claim 4, wherein the weights are non-negative.

12. The method of claim 4 where the weights are determined by non-negative matrix factorization (NMF).

13. The method of claim 1, further comprising:

expanding a bandwidth of the test undistorted speech signal.

14. The method of claim 7 or 13, wherein the training undistorted bases are obtained from a full-bandwidth clean speech signal and the training distorted bases are obtained from a reduced-bandwidth, artificially noise-corrupted, and denoised speech signal.

15. The method of claims 1, wherein the estimated test undistorted speech signal is obtained by combining the training undistorted bases using weights determined by non-negative matrix factorization (NMF).

16. The method of claim 1, wherein final magnitude spectra composing estimated magnitude short-time Fourier transforms (STFTs) of the test undistorted speech signal is obtained by applying using a Wiener filter formulation to an estimated undistorted spectra.

17. The method of claim 16, where the estimated test undistorted speech signal is obtained by and combining the inverted estimated magnitude STFTs with a phase obtained from the STFT of the test denoised speech signal and inverting the resulting complex STFT.

18. The methods of claim 16, wherein frequency components greater than 4 k HZ of the STFT of the estimated test undistorted speech signal are obtained directly from the combination of the training undistorted bases.

19. The method of claim 17 or 18, wherein a phase for the frequency components greater than 4 kHz of the STFT is obtained by replicating phase of low-frequency components less than 4 k HZ of the STFT of the estimated test undistorted speech signal.