Single channel speech music separation using nonnegative matrixfactorization and spectral masks
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SINGLE CHANNEL SPEECH MUSIC SEPARATION USING NONNEGATIVE MATRIXFACTORIZATION AND SPECTRAL MASKS. Emad M. Grais. Hakan Erdogan. 17 th International Conference on Digital Signal Processing,2011. Jain-De,Lee. Outline. INTRODUCTION NON-NEGATIVE MATRIX FACTORIZATION

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Single channel speech music separation using nonnegative matrixfactorization and spectral masks

SINGLE CHANNEL SPEECH MUSIC SEPARATION USING NONNEGATIVE MATRIXFACTORIZATION AND SPECTRAL MASKS

Emad M. Grais

Hakan Erdogan

17th International Conference on Digital Signal Processing,2011

Jain-De,Lee


Outline
Outline

  • INTRODUCTION

  • NON-NEGATIVE MATRIX FACTORIZATION

  • SIGNAL SEPARATION AND MASKING

  • EXPERIMENTS AND DISCUSSION

  • CONCLUSION


Introduction
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


Introduction1
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+M


Non negative matrix factorization
Non-negative Matrix Factorization

  • Non-negative matrix factorizationalgorithm

  • Minimization problem

  • Different cost functionsCof NMF

    • Euclidean distance

    • KL divergence

subject to elements ofB,W≧0


Non negative matrix factorization1
Non-negative Matrix Factorization

  • Euclidean distance cost function

    • KL divergence cost function

    • Multiplicative Update Algorithm


Non negative matrix factorization2
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


Signal separation and masking
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 WS and WM are submatrices in matrix W


Signal separation and masking1
Signal Separation and Masking

  • Use the initial estimated spectrograms and to build a mask as follows

  • Source signals reconstruction

Where1 is a matrix of ones

is element-wisemultiplication


Signal separation and masking2
Signal Separation and Masking

  • Two specific values of p correspond to special masks

    • Wiener filter(soft mask)

    • Hard mask


Signal separation and masking3
Signal Separation and Masking

The value of the mask versus the linear ratio for different values of p


Experiments and discussion
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


Experiments and discussion1
Experiments and Discussion

  • Performance measurement of the separation





Conclusion
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


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