Audio Signal Classification: History and Current Techniques

Audio signal classification (ASC) consists of extracting relevant features from a sound, and of using these features to identify into which of a set of classes the sound is most likely to fit. The feature extraction and grouping algorithms used can be quite diverse depending on the classification domain of the application. This paper presents background necessary to understand the general research domain of ASC, including signal processing, spectral analysis, psychoacoustics and auditory scene analysis. Also presented are the basic elements of classification systems. Perceptual and physical features are discussed, as well as clustering algorithms and analysis duration. Neural nets and hidden Markov models are discussed as they relate to ASC. These techniques are presented with an overview of the current state of the ASC research literature.