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

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.