Psychoacoustic Calibration of Loss Functions for Efficient End-to-End Neural Audio Coding
This work provides a method for improving the perceptual quality and efficiency of neural audio codecs, which is significant for developers and users of audio compression technologies.
This paper addresses the issue of suboptimal sound quality and high complexity in neural audio codecs by introducing a psychoacoustic calibration scheme for loss functions. The proposed method allows a lightweight neural codec (0.9 million parameters) to achieve near-transparent audio coding comparable to commercial MPEG-1 Audio Layer III at 112 kbps, outperforming a baseline neural codec twice its size and consuming 23.4% more bits per second.
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.