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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, faster training convergence and lower computation cost by a factor of 0.69 in comparison to the traditional Short-Time Fourier Transformbased (STFT-based) technique.

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