1. SINGLE CHANNEL SPEECH MUSIC SEPARATION USING NONNEGATIVE MATRIXFACTORIZATION AND SPECTRAL MASKS Jain-De,Lee Emad M. GraisHakan Erdogan 17 th International Conference on Digital Signal Processing,2011

2. Outline  INTRODUCTION  NON-NEGATIVE MATRIX FACTORIZATION  SIGNAL SEPARATION AND MASKING  EXPERIMENTS AND DISCUSSION  CONCLUSION

3. Introduction  There are two main stages of this work – Training stage – Separation stage  Using NMF with different types of masks to improve the separation process – The separation process faster – NMF with fewer iterations

4. Introduction  Problem formulation – The observe a signal x(t),which is the mixture of two sources s(t) and m(t) – Assume the sources have the same phase angle as the mixed Where (t, f) be the STFT of x(t) X = S + MX = S + M

5. Non-negative Matrix Factorization  Non-negative matrix factorization algorithm  Minimization problem  Different cost functions C of NMF – Euclidean distance – KL divergence subject to elements of B,W ≧ 0

6. Non-negative Matrix Factorization  Euclidean distance cost function  KL divergence cost function  Multiplicative Update Algorithm

7. Non-negative Matrix Factorization  The magnitude spectrogram S and M are calculated by NMF  Larger number of basis vectors – Lower approximation error – Redundant set of basis – Require more computation time

8. Signal Separation and Masking  The NMF is used decompose the magnitude spectrogram matrix X  The initial spectrograms estimates for speech and music signals are respectively calculated as follows Where W S and W M are submatrices in matrix W

9. Signal Separation and Masking  Use the initial estimated spectrograms and to build a mask as follows  Source signals reconstruction Where 1 is a matrix of ones is element-wise multiplication

10. Signal Separation and Masking  Two specific values of p correspond to special masks – Wiener filter(soft mask) – Hard mask

11. Signal Separation and Masking The value of the mask versus the linear ratio for different values of p

12. Experiments and Discussion  Simulation – 16kHz sampling rate – Speech Training speech data-540 short utterances Testing speech data-20 utterances – Music 38 pieces for training 1 piece for testing – Hamming window-512 point – FFT size-512 point

13. Experiments and Discussion  Performance measurement of the separation

14. Experiments and Discussion

17. Conclusion  The family of masks have a parameter to control the saturation level  The proposed algorithm gives better results and facilitates to speed up the separation process