Binti Abdullah, S;
Zamani, M;
Demosthenous, A;
(2020)
Acoustic Noise Classification Using Selective Discrete Wavelet Transform-Based Mel-Frequency Cepstral Coefficient.
In:
International Journal of Simulation Systems, Science & Technology Special Issue: Conference Procedings UKSim2020, 25 to 27 March 2020.
(pp. 6.1-6.6).
United Kingdom Simulation Society: Cambridge, UK.
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Abstract
A feature extraction method through wavelet mel-frequency cepstral coefficients (MFCCs) is proposed for acoustic noise classification. The method combined with a wavelet sub-band selection technique and a feedforward neural network with two hidden layers, is a promising solution for a compact acoustic noise classification system that could be added to speech enhancement systems and deployed in hearing devices such as cochlear implants. The technique leads to higher classification accuracies (with a mean of 95.25%) across three SNR values, a significantly smaller feature set with 16 features, a reduced memory requirement, and faster training convergence, with a trade-off of slightly higher computational complexity by a factor of 1.89 in comparison to the traditional short-time Fourier transform-based (STFT-based) technique.
| Type: | Proceedings paper |
|---|---|
| Title: | Acoustic Noise Classification Using Selective Discrete Wavelet Transform-Based Mel-Frequency Cepstral Coefficient |
| Event: | UKSim-AMSS 22nd International Conference on Modelling & Simulation (UKSim2020) |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.5013/IJSSST.a.21.02.06 |
| Publisher version: | https://ijssst.info/Vol-21/No-2/paper6.pdf |
| 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: | Acoustic noise classification, neural network, dimensionality reduction, mel-frequency cepstral coefficients, discrete wavelet transform, sub-band selection. |
| 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/10095071 |
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