Extraction of acoustic features based on auditory spike code and its application to music genre classification

A new method of extracting acoustic features based on auditory spike code is proposed. An auditory spike code represents the acoustic activities created by the signal, similar to sound encoding of the human auditory system. In the proposed method, an auditory spike code of the signal is computed using a 64-band Gammatone filterbank as the kernel functions. Then, for each spectral band, the sum and non-zero counts of the auditory spike code are determined, and the features corresponding to the population and occurrence rate of the acoustic activities for each band are computed. In addition, the distribution of the acoustic activities on a time axis is analysed based on the histogram of time intervals between the adjacent acoustic activities, and the features for expressing temporal properties of the signal are extracted. The reconstruction accuracy of the auditory spike code is also measured as the features. Different from most conventional features obtained by complex statistical modelling or learning, the features by the proposed method can directly show specific acoustic characteristics contained in the signal. These features are applied to a music genre classification, and it is confirmed that they provide a performance comparable to state-of-the-art features.