Blind recovery of cardiac and respiratory sounds using non-negative matrix factorization & time-frequency masking

The Short-time Fourier transform (STFT) is a popular time-frequency representation in many source separation problems. In this work, the sampled and discretized version of Discrete Gabor Transform (DGT) is proposed to replace STFT within the single-channel source separation problem of the Non-negative Matrix Factorization (NMF) framework. The result shows that NMF-DGT is better than NMF-STFT according to Signal-to-Interference Ratio (SIR), Signal-to-Artifact Ratio (SAR), and Signal-to-Distortion Ratio (SDR). In the supervised scheme, NMF-DGT has a SIR of 18.60 dB compared to 16.24 dB in NMF-STFT, SAR of 13.77 dB to 13.69 dB, and SDR of 12.45 dB to 11.16 dB. In the unsupervised scheme, NMF-DGT has a SIR of 0.40 dB compared to 0.27 dB by NMF-STFT, SAR of -10.21 dB to -10.36 dB, and SDR of -15.01 dB to -15.23 dB.

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Time-frequency Spectra Recognition Based on Sparse Non-negative Matrix Factorization and Support Vector Machine

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2009.01272 ◽

2009 ◽

Vol 35 (10) ◽

pp. 1272-1277 ◽

Cited By ~ 1

Author(s):

Lei CAI ◽

Yong-Sheng ZHU

Keyword(s):

Support Vector Machine ◽

Matrix Factorization ◽

Support Vector ◽

Frequency Spectra ◽

Time Frequency ◽

Non Negative Matrix Factorization

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A Novel Signal Separation Method Based on Improved Sparse Non-Negative Matrix Factorization

Entropy ◽

10.3390/e21050445 ◽

2019 ◽

Vol 21 (5) ◽

pp. 445

Author(s):

Huaqing Wang ◽

Mengyang Wang ◽

Junlin Li ◽

Liuyang Song ◽

Yansong Hao

Keyword(s):

Frequency Distribution ◽

Matrix Factorization ◽

Vibration Signal ◽

Separation Method ◽

Signal Separation ◽

Reference Vector ◽

Time Frequency ◽

Fault Features ◽

Time Frequency Distribution ◽

Non Negative Matrix Factorization

In order to separate and extract compound fault features of a vibration signal from a single channel, a novel signal separation method is proposed based on improved sparse non-negative matrix factorization (SNMF). In view of the traditional SNMF failure to perform well in the underdetermined blind source separation, a constraint reference vector is introduced in the SNMF algorithm, which can be generated by the pulse method. The square wave sequences are constructed as the constraint reference vector. The output separated signal is constrained by the vector, and the vector will update according to the feedback of the separated signal. The redundancy of the mixture signal will be reduced during the constantly updating of the vector. The time–frequency distribution is firstly applied to capture the local fault features of the vibration signal. Then the high dimension feature matrix of time–frequency distribution is factorized to select local fault features with the improved SNMF method. Meanwhile, the compound fault features can be separated and extracted automatically by using the sparse property of the improved SNMF method. Finally, envelope analysis is used to identify the feature of the output separated signal and realize compound faults diagnosis. The simulation and test results show that the proposed method can effectively solve the separation of compound faults for rotating machinery, which can reduce the dimension and improve the efficiency of algorithm. It is also confirmed that the feature extraction and separation capability of proposed method is superior to the traditional SNMF algorithm.

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