Analysis and classification of acoustic scenes with wavelet transform-based mel-scaled features

Analysis of audio from real-life environments and their categorization into different acoustic scenes can make context-aware devices and applications more efficient. Unlike speech, such signals have overlapping frequency content while spanning a much larger audible frequency range. Also, they are less structured than speech/music signals. Wavelet transform has good time-frequency localization ability owing to its variable-length basis functions. Consequently, it facilitates the extraction of more characteristic information from environmental audio. This paper attempts to classify acoustic scenes by a novel use of wavelet-based mel-scaled features. The design of the proposed framework is based on the experiments conducted on two datasets which have same scene classes but differ with regard to sample length and amount of data (in hours). It outperformed two benchmark systems, one based on mel-frequency cepstral coefficients and Gaussian mixture models and the other based on log mel-band energies and multi-layer perceptron. We also present an investigation on the use of different train and test sample duration for acoustic scene classification.