Model Order Selection for Non-negative Matrix Factorization with Application to Speech Enhancement
نویسندگان
چکیده
This report deals with the application of non-negative matrix factorization (NMF) in speech processing. A Bayesian NMF is used to find the optimal number of basis vectors for the speech signal. The result is validated by performing a speech enhancement task for a set of different number of basis vectors. The algorithm performance is measured with the Source to Distortion Ratio (SDR) that represents the overall quality of speech. The results show that for medium input SNRs, 60 basis vectors for each speaker are sufficient to model the speech spectrogram. NMF produced better SDR results than a recently developed version of Spectral Subtraction algorithm. The window length was found to have a great effect on the results, but zero padding did not influence the results.
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