Multi-channel dictionary learning speech enhancement based on power spectrum

Tongzheng Ni; Junfeng Wei; Jiarong Wu; Lanfang Zhang; Weidong Tang

doi:10.1117/12.2680516

31 May 2023 Multi-channel dictionary learning speech enhancement based on power spectrum

Tongzheng Ni, Junfeng Wei, Jiarong Wu, Lanfang Zhang, Weidong Tang

Proceedings Volume 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023); 127043B (2023) https://doi.org/10.1117/12.2680516
Event: 8th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2023), 2023, Hangzhou, China

Abstract

Algorithms that model and estimate noise based on statistical properties, such as spectral subtraction, can estimate the distribution of stationary noise, but their performance degrades when suppressing non-stationary noise. Dictionary learning and sparse representation algorithms have made great achievements in solving non-stationary noise suppression. However, the multi-channel speech enhancement algorithm based on dictionary learning needs to manually estimate the parameters of spectrum reduction threshold in practice. In order to obtain optimized noise reduction results, the adaptive estimation of spectrum reduction threshold is of great significance. According to the power spectrum of the signal, the algorithm of spectral subtraction threshold is defined and the spectral subtraction threshold is used to optimize and enhance the quality of speech. The experimental comparison shows that the spectral reduction threshold calculated based on the power spectrum is closer to the optimal result compared with the fixed threshold. In the -10dB noise environment, the multichannel dictionary learning algorithm based on improved power spectrum improves the segmental signal-to-noise ratio by 1-2dB compared with spectral subtraction and non-negative matrix decomposition, and improves the perceived speech quality assessment and short-term intelligibility by an average of 2.3 and 0.11 points respectively. The experimental results show that the multi-channel dictionary learning algorithm based on the improved power spectrum can effectively remove additive noise under both unsteady and steady state noise conditions.

Citation Download Citation

Tongzheng Ni, Junfeng Wei, Jiarong Wu, Lanfang Zhang, and Weidong Tang "Multi-channel dictionary learning speech enhancement based on power spectrum", Proc. SPIE 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023), 127043B (31 May 2023); https://doi.org/10.1117/12.2680516

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KEYWORDS

Signal to noise ratio

Associative arrays

Interference (communication)

Background noise

Mathematical optimization

Fourier transforms

Detection and tracking algorithms

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