Pattern analysis based acoustic signal processing: a survey of the state-of-art

Audio signal processing is the most challenging field in the current era for an analysis of an audio signal. Audio signal classification (ASC) comprises of generating appropriate features from a sound and utilizing these features to distinguish the class the sound is most likely to fit. Based on the application's classification domain, the characteristics extraction and classification/clustering algorithms used may be quite diverse. The paper provides the survey of the state-of art for understanding ASC's general research scope, including different types of audio; representation of audio like acoustic, spectrogram; audio feature extraction techniques like physical, perceptual, static, dynamic; audio pattern matching approaches like pattern matching, acoustic phonetic, artificial intelligence; classification, and clustering techniques. The aim of this state-of-art paper is to produce a summary and guidelines for using the broadly used methods, to identify the challenges as well as future research directions of acoustic signal processing.

Automated summary

Audio signal processing is a challenging field in the current era, where audio signal classification aims to generate appropriate features from sound and distinguish the class of sound, with diverse representation methods and feature extraction techniques. The state-of-art paper provides a summary and guidelines for understanding ASC's research scope, including different audio types, representation methods, feature extraction techniques, pattern matching approaches, classification, and clustering techniques.