DocumentCode :
3449214
Title :
Recognition of music types
Author :
Soltau, Hagen ; Schultz, Tanja ; Westphal, Martin ; Waibel, Alex
Author_Institution :
Karlsruhe Univ., Germany
Volume :
2
fYear :
1998
fDate :
12-15 May 1998
Firstpage :
1137
Abstract :
This paper describes a music type recognition system that can be used to index and search in multimedia databases. A new approach to temporal structure modeling is supposed. The so called ETM-NN (explicit time modelling with neural network) method uses abstraction of acoustical events to the hidden units of a neural network. This new set of abstract features representing temporal structures, can be then learned via a traditional neural networks to discriminate between different types of music. The experiments show that this method outperforms HMMs significantly
Keywords :
acoustic signal processing; audio signals; feature extraction; hidden Markov models; learning (artificial intelligence); multimedia computing; music; neural nets; ETM-NN method; HMM; abstract features; acoustical events abstraction; experiments; explicit time modelling with neural network; hidden units; multimedia database indexing; multimedia database searching; music type recognition system; neural network; temporal structure modeling; temporal structures; Cepstral analysis; Hidden Markov models; Indexes; Interactive systems; Laboratories; Multimedia databases; Multiple signal classification; Music; Neural networks; Speech;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on
Conference_Location :
Seattle, WA
ISSN :
1520-6149
Print_ISBN :
0-7803-4428-6
Type :
conf
DOI :
10.1109/ICASSP.1998.675470
Filename :
675470
Link To Document :
بازگشت