Abdullah, S;
Zamani, M;
Demosthenous, A;
(2021)
A discrete wavelet transform-based voice activity detection and noise classification with sub-band selection.
In:
2021 IEEE International Symposium on Circuits and Systems (ISCAS).
IEEE: Daegu, Korea.
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Abstract
A real-time discrete wavelet transform-based adaptive voice activity detector and sub-band selection for feature extraction are proposed for noise classification, which can be used in a speech processing pipeline. The voice activity detection and sub-band selection rely on wavelet energy features and the feature extraction process involves the extraction of mel-frequency cepstral coefficients from selected wavelet sub-bands and mean absolute values of all sub-bands. The method combined with a feedforward neural network with two hidden layers could be added to speech enhancement systems and deployed in hearing devices such as cochlear implants. In comparison to the conventional short-time Fourier transform-based technique, it has higher F1 scores and classification accuracies (with a mean of 0.916 and 90.1%, respectively) across five different noise types (babble, factory, pink, Volvo (car) and white noise), a significantly smaller feature set with 21 features, reduced memory requirement, faster training convergence and about half the computational cost.
| Type: | Proceedings paper |
|---|---|
| Title: | A discrete wavelet transform-based voice activity detection and noise classification with sub-band selection |
| Event: | 2021 IEEE International Symposium on Circuits and Systems (ISCAS) |
| ISBN-13: | 9781728192017 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ISCAS51556.2021.9401647 |
| Publisher version: | https://doi.org/10.1109/ISCAS51556.2021.9401647 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Discrete wavelet transform, mel-frequency cepstral coefficients, multilayer perceptron, noise classification, sub-band selection, voice activity detection |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10131676 |
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