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Cepstrum Enhancement-Based Nonnegative Matrix Factorization for Blind Source Separation
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DOI：10.1109/LSP.2026.3675984.png)
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Abstract
En 中文
Nonnegative matrix factorization (NMF) has proven to be a powerful source spectrogram representation in many audio blind source separation (BSS) methods. However, NMF-based methods may still suffer from the well-known permutation problem especially when the number of NMF basis vectors is large. This letter presents a cepstrum enhancement-based NMF approach that effectively exploits the harmonic structure of audio signals. In oracle NMF-based methods, the source variances are estimated by directly applying NMF to the spectrograms of the separated signals. In the presented approach, however, the spectrogram of each separated source is firstly smoothed using a cepstrum thresholding method, and then NMF is applied to the enhanced spectrogram for source variance estimation. The cepstrum thresholding enhances the harmonic structure of the source signals while removing the interfering components from other sources, which helps alleviate the permutation problem. Also, the proposed cepstrum enhancement method is decoupled from the specific source model and can be easily incorporated into other NMF-based BSS methods as a plug-and-play module. Experimental results validate the effectiveness of the proposed method.
Keywords:
Blind source separation
non-negative matrix factorization
cepstral thresholding
permutation problem
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3.9
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