Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Computer and Robot Vision
Content-Based Classification, Search, and Retrieval of Audio
IEEE MultiMedia
Construction and Evaluation of a Robust Multifeature Speech/Music Discriminator
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
The Journal of Machine Learning Research
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Content-based audio classification and retrieval by support vector machines
IEEE Transactions on Neural Networks
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With the rapid growth in audio data volume, research in the area of content-based audio retrieval has gained impetus in the last decade. Audio classification serves as the fundamental step towards it. Accuracy in classifying data relies on the strength of the features and on the efficacy of classification scheme. In this work, we have focused on the features only. We have restricted ourselves further in the time domain based low level features. Zero crossing rate (ZCR) and shot time energy (STE) are the most widely used features in this category. We have tried to develop the features reflecting the quasi-periodic pattern of the signal by studying the occurrence pattern of ZCR and STE. Co-occurrence matrix for ZCR and STE are formed and features are computed from that to parameterize the signal. For classification, simple k-means clustering is followed and experimental result indicates that proposed features perform better than the traditional feature derived from ZCR and STE.