Acoustic event classification using Cauchy Non-negative matrix factorization and fuzzy rule-based classifier

Identification of presence of target acoustic sound or event from a single channel mixture is a challenging task of automatic sound recognition system. In presence of background noise, the detection and classification of target acoustic event becomes more difficult. Various methods have been proposed that extract features from spectrogram of sound and then the extracted features are used with traditional non negative matrix factorization for separation of overlapping sound. In his paper, we propose an approach to separate and classify single channel acoustic events. The method combines Common Fate Transformation and Cauchy Non-negative Matrix Factorization for feature extraction and finally fuzzy rule-based classifier is developed for classification. The proposed method, when applied to real data, gave high true positive rate. The method also gave better results in terms of true positive rate when compared to widely used support vector machine using the same real data. Moreover, the proposed approach is fast and can be used for the efficient separation of acoustic events from overlapping sounds.