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International Journal of Speech Technology
Erschienen in: International Journal of Speech Technology 4/2023

18.11.2023

Psychoacoustic model-driven spectral subtraction for monaural speech enhancement

verfasst von: Navneet Upadhyay

Erschienen in: International Journal of Speech Technology | Ausgabe 4/2023

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Abstract

In this paper, we investigate a psychoacoustic model-driven spectral subtraction framework for enhancement of noisy speech. In the proposed framework, the noisy speech spectrum is separated into six distinct and unevenly frequency-spaced subbands as per the psychoacoustic model of the human hearing system, and spectral over-subtraction is applied independently in each subband. The noise in each subband is estimated using an adaptive noise estimator that does not require a speech pause tracker. To compute and update the noise, the noisy speech power is adaptively smoothed using a smoothing factor controlled by a posterior SNR. The performance of the proposed framework is evaluated using SNR, segmental SNR (SegSNR), and PESQ scores for a variety of non-stationary and stationary noise environments at varying SNR levels. The experimental results show that the proposed framework outperforms various up-to-date speech enhancement technologies on three extensively used  objective metrics assessments and speech spectrograms.
Vorheriger Artikel Optimized cross-corpus speech emotion recognition framework based on normalized 1D convolutional neural network with data augmentation and feature selection
Nächster Artikel Identification of Parkinson’s disease from speech signal using machine learning approach

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Metadaten
Titel
Psychoacoustic model-driven spectral subtraction for monaural speech enhancement
verfasst von
Navneet Upadhyay
Publikationsdatum
18.11.2023
Verlag
Springer US
Erschienen in
International Journal of Speech Technology / Ausgabe 4/2023
Print ISSN: 1381-2416
Elektronische ISSN: 1572-8110
DOI
https://doi.org/10.1007/s10772-023-10062-9

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