UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Acoustic Noise Classification Using Selective Discrete Wavelet Transform-Based Mel-Frequency Cepstral Coefficient

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. Green open access

[img]
Preview
Text
UKSIM-submitted-ACCEPTED-CORRECTED_V2.pdf - Published version

Download (414kB) | Preview

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 > 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
Downloads since deposit
21Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item