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학술대회 Audio Coding Based on Spectral Recovery by Convolutional Neural Network
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신성현, 백승권, 이태진, 박호종
International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2019, pp.725-729
19HR2500, [통합과제] 초실감 테라미디어를 위한 AV부호화 및 LF미디어 원천기술 개발, 최진수
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 제안 키워드
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