Journal of Theoretical and Applied Information Technology
Audio mining is a technique by which the core of an audio signal can be automatically searched and analyzed. This paper addresses feature extraction from audio and audio similarity measures and proposes an algorithm to mine any type of audio data. This Hybrid Algorithm for Audio Mining (HAAM) consists of two different phases. The first phase named training phase, takes training audio data as input and then sonogram, spectrum histogram, periodicity histogram and fluctuation pattern for the audio data are calculated. Then the features are extracted using Mel Frequency Cepstral Coefficient (MFCC) and the essential features are reduced from these for further processing.