Matching pursuit based robust acoustic event classification for surveillance systems. (January 2017) - Nguyen, Quang; Choi, JongSuk
Highlights: Acoustic event classification using matching pursuit and random forest. Event based feature extraction using atom time and frequency information. Superior in classification of human scream from other classes. Graphical abstract: Abstract: The ability to automatically recognize acoustic events in real world conditions is an important application of the surveillance systems. This paper presents an acoustic event classification (AEC) method which uses the Matching Pursuit to extract the important Gabor atoms from input audio signals. Rather than extracting features in short-time frames, we apply the matching pursuit to the whole duration of an acoustic event. Information from atoms, such as time, frequency, and amplitude are used to construct time-frequency features. These features capture both spectral and temporal information of the sound event, which is analogous to the spectrogram representation. Experiments were performed on an 8-class database including human scream and gunshot. Under noisy and mismatched conditions, the proposed classification method achieves F1-score of 0.814, which is superior to state-of-the-art methods.