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
Estimation of musical sound separation algorithm effectiveness employing neural networks
Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
Separation of harmonic sound sources using sinusoidal modeling
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Blind separation of disjoint orthogonal signals: demixing N sources from 2 mixtures
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
Analysis of Sound Features for Music Timbre Recognition
MUE '07 Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering
Maximum Likelihood Study for Sound Pattern Separation and Recognition
MUE '07 Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering
EURASIP Journal on Applied Signal Processing
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Automatic indexing of music instruments for multi-timbre sounds is challenging, especially when partials from different sources are overlapping with each other. Temporal features, which have been successfully applied in monophonic sound timbre identification, failed to isolate music instrument in multi-timbre objects, since the detection of the start and end position of each music segment unit is very difficult. Spectral features of MPEG7 and other popular features provide economic computation but contain limited information about timbre. Being compared to the spectral features, spectrum signature features have less information loss; therefore may identify sound sources in multitimbre music objects with higher accuracy. However, the high dimensionality of spectrum signature feature set requires intensive computing and causes estimation efficiency problem. To overcome these problems, the authors developed a new multi-resolution system with an iterative spectrum band matching device to provide fast and accurate recognition.