Extension of Monaural to Stereophonic Sound Based on Deep Neural Networks

Chun CJ, Jeong SH, Park SY, Kim HK. Extension of Monaural to Stereophonic Sound Based on Deep Neural Networks. In: AES Convention 139. Audio Engineering Society; 2015. Paper 9400. Available from: https://aes.org/publications/elibrary-page/?id=17957

@inproceedings{Chun2015_17957,
  author = {Chun, Chan Jun and Jeong, Seok Hee and Park, Su Yeon and Kim, Hong Kook},
  title = {{Extension of Monaural to Stereophonic Sound Based on Deep Neural Networks}},
  booktitle = {AES Convention 139},
  note = {Paper 9400},
  year = {2015},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=17957}
}

TY  - CPAPER
TI  - Extension of Monaural to Stereophonic Sound Based on Deep Neural Networks
AU  - Chun, Chan Jun
AU  - Jeong, Seok Hee
AU  - Park, Su Yeon
AU  - Kim, Hong Kook
T2  - AES Convention 139
M1  - Paper 9400
PY  - 2015
DA  - 2015/10/06
UR  - https://aes.org/publications/elibrary-page/?id=17957
PB  - Audio Engineering Society
LA  - en
AB  - In this paper we propose a method of extending monaural into stereophonic sound based on deep neural networks (DNNs). First, it is assumed that monaural signals are the mid signals for the extended stereo signals. In addition, the residual signals are obtained by performing the linear prediction (LP) analysis. The LP coefficients of monaural signals are converted into the line spectral frequency (LSF) coefficients. After that, the LSF coefficients are taken as the DNN features, and the features of the side signals are estimated from those of the mid signals. The performance of the proposed method is evaluated using a log spectral distortion (LSD) measure and a multiple stimuli with a hidden reference and anchor (MUSHRA) test. It is shown from the performance comparison that the proposed method provides lower LSD and higher MUSHRA score than a conventional method using hidden Markov model (HMM).
ER  -