Title :
Music Identification Using Embedded HMM
Author :
Kai Shen ; Gao, Sheng ; Chai, Peiqi ; Sun, Qibin
Author_Institution :
Inst. for Infocomm Res., Heng Mui Keng
fDate :
Oct. 30 2005-Nov. 2 2005
Abstract :
In this paper, we propose a new method for music identification based on embedded hidden Markov model (EHMM). Differing from conventional HMM, the EHMM estimates the emission probability of its external HMM from the second, state specific HMM, which is referred as internal HMM. EHMM clusters the feature blocks with its external HMM and describes spectral and the temporal structures of each feature block with its internal HMM. Our analysis and experimental results show that the proposed method for music identification achieves higher accuracy and lower complexity than previous approaches
Keywords :
audio signal processing; hidden Markov models; music; pattern recognition; probability; embedded HMM; embedded hidden Markov model; emission probability estimation; feature blocks; music identification; spectral structures; temporal structures; Audio databases; Copyright protection; Embedded computing; Event detection; Hidden Markov models; Libraries; Robustness; Spatial databases; State estimation; Sun; EHMM; Music Identification; Temporal Information;
Conference_Titel :
Multimedia Signal Processing, 2005 IEEE 7th Workshop on
Conference_Location :
Shanghai
Print_ISBN :
0-7803-9288-4
Electronic_ISBN :
0-7803-9289-2
DOI :
10.1109/MMSP.2005.248550