|
KMID : 1022420180100030019
|
|
Phonetics and Speech Sciences
2018 Volume.10 No. 3 p.19 ~ p.29
|
|
|
Performance of music section detection in broadcast drama contents using independent component analysis and deep neural network
|
|
Heo Woon-Haeng
Jang Byeong-Yong
Jo Hyeon-Ho
Kim Jung-Hyun
Kwon Oh-Wook
|
|
|
Abstract
|
|
|
|
We propose to use independent component analysis (ICA) and deep neural network (DNN) to detect music sections in broadcast drama contents. Drama contents mainly comprise silence, noise, speech, music, and mixed (speech+music) sections. The silence section is detected by signal activity detection. To detect the music section, we train noise, speech, music, and mixed models with DNN. In computer experiments, we used the MUSAN corpus for training the acoustic model, and conducted an experiment using 3 hours' worth of Korean drama contents. As the mixed section includes music signals, it was regarded as a music section. The segmentation error rate (SER) of music section detection was observed to be 19.0%. In addition, when stereo mixed signals were separated into music signals using ICA, the SER was reduced to 11.8%.
|
|
|
KEYWORD
|
|
|
independent component analysis , deep neural network , segmentation error rate
|
|
|
FullTexts / Linksout information
|
|
|
|
|
|
Listed journal information
|
|
|
|