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. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.