title: A hierarchical approach for speech-instrumental-song classification.
abstract: Audio classification acts as the fundamental step for lots of applications like content based audio retrieval and audio indexing. In this work, we have presented a novel scheme for classifying audio signal into three categories namely, speech, music without voice (instrumental) and music with voice (song). A hierarchical approach has been adopted to classify the signals. At the first stage, signals are categorized as speech and music using audio texture derived from simple features like ZCR and STE. Proposed audio texture captures contextual information and summarizes the frame level features. At the second stage, music is further classified as instrumental/song based on Mel frequency cepstral co-efficient (MFCC). A classifier based on Random Sample and Consensus (RANSAC), capable of handling wide variety of data has been utilized. Experimental result indicates the effectiveness of the proposed scheme.
authors.affiliation: CSE Dept, Institute of Technology and Marine Engg, Diamond Harbour, West Bengal India.
authors.forename: Arijit
authors.initials: A
authors.lastname: Ghosal
authors.affiliation: Indian Statistical Institute, Kolkata, India.
authors.forename: Rudrasis
authors.initials: R
authors.lastname: Chakraborty
authors.affiliation: Department of Information Technology, Jadavpur University, Kolkata, India.
authors.forename: Bibhas Chandra
authors.initials: BC
authors.lastname: Dhara
authors.affiliation: Department of Computer Science & Engineering, Jadavpur University, Kolkata, India.
authors.forename: Sanjoy Kumar
authors.initials: SK
authors.lastname: Saha
keywords: Audio texture; Instrumental/Song classification; Mel frequency cepstral co-efficient; Random sample and consensus; Speech/Music classification