Supervised monaural speech enhancement using two-level complementary joint sparse representations

Fu, Jiafei; Zhang, Long; Ye, Zhongfu

doi:10.1016/j.apacoust.2017.11.005

Cited by 7 publications

(1 citation statement)

References 26 publications

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“…However, due to the fact that some speech components of the noisy signal were still projected on the interference noise subdictionary, the speech enhancement performance is not optimal. Fu et al proposed a two-level complementary joint sparse representation method to enhance single-channel speech [21]. To suppress noise source confusion, a twolevel joint sparse representation was constructed using the relationship among speech, noise, noisy signals, and the discriminative property of joint dictionary to estimate a less distorted speech signal.…”

Section: Introductionmentioning

confidence: 99%

Single-channel speech enhancement based on joint constrained dictionary learning

Sun

et al. 2021

J AUDIO SPEECH MUSIC PROC.

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To improve the performance of speech enhancement in a complex noise environment, a joint constrained dictionary learning method for single-channel speech enhancement is proposed, which solves the “cross projection” problem of signals in the joint dictionary. In the method, the new optimization function not only constrains the sparse representation of the noisy signal in the joint dictionary, and controls the projection error of the speech signal and noise signal on the corresponding sub-dictionary, but also minimizes the cross projection error and the correlation between the sub-dictionaries. In addition, the adjustment factors are introduced to balance the weight of constraint terms to obtain the joint dictionary more discriminatively. When the method is applied to the single-channel speech enhancement, speech components of the noisy signal can be more projected onto the clean speech sub-dictionary of the joint dictionary without being affected by the noise sub-dictionary, which makes the quality and intelligibility of the enhanced speech higher. The experimental results verify that our algorithm has better performance than the speech enhancement algorithm based on discriminative dictionary learning under white noise and colored noise environments in time domain waveform, spectrogram, global signal-to-noise ratio, subjective evaluation of speech quality, and logarithmic spectrum distance.

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Section: Introductionmentioning

confidence: 99%

Single-channel speech enhancement based on joint constrained dictionary learning

Sun

et al. 2021

J AUDIO SPEECH MUSIC PROC.

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Dual transform based joint learning single channel speech separation using generative joint dictionary learning

Hossain

Mahmud

Islam

et al. 2022

Multimed Tools Appl

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FLGCNN: A novel fully convolutional neural network for end-to-end monaural speech enhancement with utterance-based objective functions

Zhu

2020

Applied Acoustics

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Supervised monaural speech enhancement using two-level complementary joint sparse representations

Cited by 7 publications

References 26 publications

Single-channel speech enhancement based on joint constrained dictionary learning

Single-channel speech enhancement based on joint constrained dictionary learning

Dual transform based joint learning single channel speech separation using generative joint dictionary learning

FLGCNN: A novel fully convolutional neural network for end-to-end monaural speech enhancement with utterance-based objective functions

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