A singer identification technique for content-based classification of MP3 music objects
Proceedings of the eleventh international conference on Information and knowledge management
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Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
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ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Audio classification in speech and music: a comparison between a statistical and a neural approach
EURASIP Journal on Applied Signal Processing
Perceptual audio hashing functions
EURASIP Journal on Applied Signal Processing
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EURASIP Journal on Applied Signal Processing
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MMACTE'05 Proceedings of the 7th WSEAS International Conference on Mathematical Methods and Computational Techniques In Electrical Engineering
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ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A decision-tree-based algorithm for speech/music classification and segmentation
EURASIP Journal on Audio, Speech, and Music Processing
A combination of data mining method with decision trees building for Speech/Music discrimination
Computer Speech and Language
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International Journal of Wireless and Mobile Computing
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Audio data model for multi-criteria query formulation and retrieval
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Digital video scenes identification using audiovisual features
WebMedia '09 Proceedings of the XV Brazilian Symposium on Multimedia and the Web
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Dictionary learning based sparse coefficients for audio classification with max and average pooling
Digital Signal Processing
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Personal and Ubiquitous Computing
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A hierarchical system for audio classification and retrieval based on audio content analysis is presented in this paper. The system consists of three stages. The first stage is called the coarse-level audio classification and segmentation, where audio recordings are classified and segmented into speech, music, several types of environmental sounds, and silence, based on morphological and statistical analysis of temporal curves of short-time features of audio signals. In the second stage, environmental sounds are further classified into finer classes such as applause, rain, bird sound, etc. This fine-level classification is based on time-frequency analysis of audio signals and use of the hidden Markov model (HMM) for classification. In the third stage, the query-by-example audio retrieval is implemented where similar sounds can be found according to an input sample audio. It is shown that the proposed system has achieved an accuracy higher than 90% for coarse-level audio classification. Examples of audio fine classification and audio retrieval are also provided.