Layered nonnegative matrix factorization for speech separation

Hsu, Chung Chien; Chien, Jen‐Tzung; Chi, Tai-Shih

doi:10.21437/interspeech.2015-217

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…We executed our proposed LCNMF algorithm by comparing both TNMF and NMF. For LNMF and LCNMF, we both chose a two-layer structure, LNMF with more layers did not significantly improve the separation results [18]. The number of speech basis vectors K1 and K2 of the first and second layers was set as 120 and 30 respectively.…”

Section: Experimental Data and Parametersmentioning

confidence: 99%

“…Roux et al proposed a Deep NMF model, which unfolds the NMF iterations and unties the NMF parameters for optimal separation performance [17]. Layered NMF (LNMF) stacks multiple layers of standard NMF blocks, and then combine sparse parts-based representation learned by each single-layer to interpret the speech data differently [18]. The experimental results showed that the hierarchical NMF model could significantly enhance speech separation performance [17][18].…”

Section: Introductionmentioning

confidence: 99%

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Layered Convolutive Nonnegative Matrix Factorization for Speech Separation

Wang

Huang

et al. 2022

J. Phys.: Conf. Ser.

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Nonnegative matrix factorization (NMF) has attracted significant attention for its good performance in single-channel speech separation. The improved algorithms of NMF have become research hotspots. Layered NMF (LNMF), an improved algorithm, can express the source signal more accurately for its multilayer structure. However, LNMF sometimes performs poorly because it ignores the short-term correlation of speech signals. Based on LNMF and the advantages of Convolutive NMF (CNMF), we proposed a Layered Convolutive NMF(LCNMF) algorithm for single-channel speech separation. The LCNMF corporates the multilayer structure into the NMF and expands the convolution of the top-level NMF model. During the training, NMF is used to learn the non-top-level basis matrices, and CNMF is used to learn the top-level basis matrix, then combined with each single-layer of basis matrix. During the prediction, CNMF is used to separate mixed signals. The results on the dataset MIK-1K showed that LCNMF outperformed NMF and LNMF for separating the mixture of single-channel speech signals. LCNMF improved by 0.019, 1.049dB, 1.305dB, and 0.851dB on average compared with NMF, and improved by 0.007, 0.172dB, 0.090dB, and 0.366dB on average compared with LNMF in sort-term objective intelligibility (STOI), Source to Distortion Ratio (SDR), Source to Interference Ratio (SIR) and Source to Artifacts Ratio (SAR)

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Section: Experimental Data and Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Layered Convolutive Nonnegative Matrix Factorization for Speech Separation

Wang

Huang

et al. 2022

J. Phys.: Conf. Ser.

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show abstract

“…Hsu et al [109] apply deep MF on the spectrogram matrix of a set of spoken sentences to extract several layers of frequential basis features, and is better able to separate the speakers in a mixture than a simple one-layer NMF. Thakur et al [110] used deep AA to extract sources based on the spectrograms of bioacoustics signals, with the dictionaries learnt at the first layers corresponding to archetypes on the convex hull of the data while deeper atoms being more in the center of the data.…”

Section: Audio Processingmentioning

confidence: 99%

Deep matrix factorizations

De Handschutter,

Gillis,

Siebert

2020

Preprint

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Constrained low-rank matrix approximations have been known for decades as powerful linear dimensionality reduction techniques to be able to extract the information contained in large data sets in a relevant way. However, such low-rank approaches are unable to mine complex, interleaved features that underlie hierarchical semantics. Recently, deep matrix factorization (deep MF) was introduced to deal with the extraction of several layers of features and has been shown to reach outstanding performances on unsupervised tasks. Deep MF was motivated by the success of deep learning, as it is conceptually close to some neural networks paradigms. In this paper, we present the main models, algorithms, and applications of deep MF through a comprehensive literature review. We also discuss theoretical questions and perspectives of research.

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