A new joint noise reduction and echo suppression system based on FBSS and automatic voice activity detector

Henni, Rahima; Djebari, Mustapha; Djendi, Mohamed

doi:10.1016/j.apacoust.2020.107444

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…If it is in the training phase, the calculated separation matrix is compared with the actual separation matrix of the data block labels to obtain its loss. If the loss exceeds the threshold [15], the weights in the hidden layer are reversely adjusted based on the loss, i.e., ω and v , and the separation matrix is recalculated until the loss converges and stabilizes in the range of the set threshold [16]. If in the use phase, the separation matrix is obtained by calculating according to equation (3).…”

Section: Ica-based Blind Source Separation Algorithm Improved By a Co...mentioning

confidence: 99%

Research on Music Signal Processing Based on a Blind Source Separation Algorithm

Zhao¹,

Tuo²,

Guo³

et al. 2022

AETiC

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The isolation of mixed music signals is beneficial to the extraction and identification of music signal features and to enhance music signal quality. This paper briefly introduced the mathematical model for separating blind source from mixed music signals and the traditional Independent Component Analysis (ICA) algorithm. The separation algorithm was optimized by the complex neural network. The traditional and optimized ICA algorithms were simulated in MATLAB software. It was found that the time-domain waveform of the signal isolated by the improved ICA-based separation algorithm was closer to the source signal. The similarity coefficient matrix, signal-to-interference ratio, performance index, and iteration time of the improved ICA-based algorithm was 62.3, 0.0011, and 0.87 s, respectively, which were all superior to the traditional ICA algorithm. The novelty of this paper is setting the initial iterative matrix of the ICA algorithm with the complex neural network.

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Section: Ica-based Blind Source Separation Algorithm Improved By a Co...mentioning

confidence: 99%

Research on Music Signal Processing Based on a Blind Source Separation Algorithm

Zhao¹,

Tuo²,

Guo³

et al. 2022

AETiC

View full text Add to dashboard Cite

show abstract

A new deep learning forward BSS (D-FBSS) algorithm for acoustic noise reduction and speech enhancement

Aliouat,

Djendi

2025

Applied Acoustics

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Hybrid voice activity detection system based on LSTM and auditory speech features

Korkmaz

Boyacı²

2023

Biomedical Signal Processing and Control

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A new joint noise reduction and echo suppression system based on FBSS and automatic voice activity detector

Cited by 3 publications

References 21 publications

Research on Music Signal Processing Based on a Blind Source Separation Algorithm

Research on Music Signal Processing Based on a Blind Source Separation Algorithm

A new deep learning forward BSS (D-FBSS) algorithm for acoustic noise reduction and speech enhancement

Hybrid voice activity detection system based on LSTM and auditory speech features

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