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Conference Paper
Audio Coding Based on Spectral Recovery by Convolutional Neural Network
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- Authors
- Seong-Hyeon Shin, Seung Kwon Beack, Taejin Lee, Hochong Park
- Issue Date
- 2019-05
- Citation
- International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2019, pp.725-729
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- This study proposes a new method of audio coding based on spectral recovery, which can enhance the performance of transform audio coding. An encoder represents spectral information of an input in a time-frequency domain and transmits only a portion of it so that the remaining spectral information can be recovered based on the transmitted information. A decoder recovers the magnitudes of missing spectral information using a convolutional neural network. The signs of missing spectral information are either transmitted or randomly assigned, according to their importance. By combining transmission and recovery of spectral information, the proposed method can enhance the coding performance, compared with conventional transform coding. The subjective performance evaluation shows that, for mono coding at 39.4 kbps, the proposed method provides higher sound quality than the USAC, by an average MUSHRA score of 8.5.
- KSP Keywords
- Audio coding, Coding performance, Convolution neural network(CNN), Performance evaluation, Spectral information, frequency domain(FD), new method, sound quality, time frequency(T-F), time-frequency domain, transform coding