Content-based retrieval for music collections
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing
Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing
Computer and Robot Vision
The Scientific Evaluation of Music Information Retrieval Systems: Foundations and Future
Computer Music Journal
Psychoacoustics: Facts and Models
Psychoacoustics: Facts and Models
Real-time discrimination of broadcast speech/music
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
Musical instrument recognition using cepstral coefficients and temporal features
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Speech/music discrimination for multimedia applications
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
Audio classification from time-frequency texture
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Speech/Music Classification Using Occurrence Pattern of ZCR and STE
IITA '09 Proceedings of the 2009 Third International Symposium on Intelligent Information Technology Application - Volume 03
Automatic Music Genre Classification Using Bass Lines
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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Music classification is a fundamental step in any music retrieval system. As the first step for this, we have proposed a scheme for discriminating music signal with voice (song) and without voice (instrumental). The task is important as song-instrument discrimination is of immense importance in the context of a multi-lingual country like India. Moreover, it enables the subsequent classification of instrumentals based on the type of instrument. Spectrogram image of an audio signal shows the significance of different frequency components over the time scale. It has been observed that spectrogram image of an instrumental signal shows more stable peaks persisting over time and it is not so for a song. It has motivated us to look for spectrogram image based features. Contextual features have been computed based on the occurrence pattern of the most significant frequency over the time scale and overall texture pattern revealed by the time-frequency distribution of signal intensity. RANSAC has been used to classify the signals. Experimental result indicates the effectiveness of the proposed scheme.