2020/01/01 by Kai Zhen, Mi Suk Lee, Jongmo Sung +2 · 30 citations
Computer Science · Engineering · Mathematics · #Advanced Adaptive Filtering Techniques #Auditory masking #Codec #Coding (social sciences) #Computer hardware #Computer science #Digital Filter Design and Implementation #Image and Signal Denoising Methods #Masking (illustration) #Mathematics #Perception #Psychoacoustics #Sound quality #Speech coding #Speech recognition #cs.LG #cs.SD #eess.AS
paper · pdf · doi:10.1109/lsp.2020.3039765
published in IEEE Signal Processing Letters 27, 2159-2163 (Institute of Electrical and Electronics Engineers)
openalex publication_date 2020/01/01 · openalex created_date 2020/12/07 · arxiv created 2020/12/31 · arxiv updated 2021/01/05 · openalex updated_date 2026/08/05
Conventional audio coding technologies commonly leverage human perception of sound, or psychoacoustics, to reduce the bitrate while preserving the perceptual quality of the decoded audio signals. For neural audio codecs, however, the objective nature of the loss function usually leads to suboptimal sound quality as well as high run-time complexity due to the large model size. In this work, we present a psychoacoustic calibration scheme to re-define the loss functions of neural audio coding systems so that it can decode signals more perceptually similar to the reference, yet with a much lower model complexity. The proposed loss function incorporates the global masking threshold, allowing the reconstruction error that corresponds to inaudible artifacts. Experimental results show that the proposed model outperforms the baseline neural codec twice as large and consuming 23.4% more bits per second. With the proposed method, a lightweight neural codec, with only 0.9 million parameters, performs near-transparent audio coding comparable with the commercial MPEG-1 Audio Layer III codec at 112 kbps.