Sound event classification based on Feature Integration, Recursive Feature Elimination and Structured Classification

This paper proposes a novel system for sound event classification based on Feature Integration, Recursive Feature Elimination Support Vector Machine (RFESVM) and Structured Classification. The key points of the proposed method can be summarized as follows: 1) the integration of various feature extraction methods coming from different research communities in one system; 2) the use of feature selection to analyze and select the optimal subset of the integrated features; 3) the adoption of a knowledge-based taxonomic structured classification scheme. Particularly, six groups of features including temporal shape, spectral shape, spectrogram, perceptual cepstral coefficients, harmonic and rhythmic feature sets are investigated in this paper. For the feature selection, the employed RFESVM method enables to select the optimal feature subset taking into account their mutual information. We further develop different feature elimination strategies for RFESVM depending on the requirements of complexity. The RFESVM is combined with a structured classification designed for our task in surveillance and security applications. The proposed method is tested in two realistic environments and the experimental results show good improvements of the classification performance compared to the conventional method.