SNR-Progressive Model With Harmonic Compensation for Low-SNR Speech Enhancement

Despite significant progress made in the last decade, deep neural network (DNN) based speech enhancement (SE) still faces the challenge of notable degradation in the quality of recovered speech under low signal-to-noise ratio (SNR) conditions. In this letter, we propose an SNR-progressive speech enh...

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Bibliographic Details
Published inIEEE signal processing letters Vol. 32; pp. 476 - 480
Main Authors Hou, Zhongshu, Lei, Tong, Hu, Qinwen, Cao, Zhanzhong, Lu, Jing
Format Journal Article
LanguageEnglish
Published New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
Compensation
Estimation
Filtering
Harmonic analysis
Low-SNR speech enhancement
neural network
Noise measurement
Pitch estimation
Power harmonic filters
Signal quality
Signal to noise ratio
SNR-progressive learning
Spectrogram
Speech enhancement
Speech processing
Training
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Summary:Despite significant progress made in the last decade, deep neural network (DNN) based speech enhancement (SE) still faces the challenge of notable degradation in the quality of recovered speech under low signal-to-noise ratio (SNR) conditions. In this letter, we propose an SNR-progressive speech enhancement model with harmonic compensation for low-SNR SE. Reliable pitch estimation is obtained from the intermediate output, which has the benefit of retaining more speech components than the coarse estimate while possessing a significantly higher SNR than the input noisy speech. An effective harmonic compensation mechanism is introduced for better harmonic recovery. Extensive experiments demonstrate the advantage of our proposed model.
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ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2024.3484288