Efficient Audio Enhancement with a Differentiable Psychoacoustic Loss

Abstract

Audio enhancement consists of improving the perceived quality of audio signals. Initially, with the aim of addressing bandwidth extension, this work proposes \(AEROMamba_{P}\), an efficient variant of the AERO super-resolution architecture where attention and LSTM layers are replaced by the Mamba state-space model, and which incorporates a newly developed differentiable perceptual loss derived from the Perceptual Audio Quality Measure (PAQM). During training, the architecture requires approximately 2-4x less GPU memory than the baseline; during inference, it achieves a 14x speedup while using only one-fifth of the GPU memory. When upsampling both a piano dataset and MUSDB18 from 11.025 kHz to 44.1 kHz, subjective listening tests show that \(AEROMamba_{P}\) outperforms AERO by 15% in perceived quality scores. Next, to handle the enhancement of audio signals that have been highly compressed by lossy coding, it is further proposed \(AEROMamba_{P\={S}}\), which applies the same framework but replaces STFT reconstruction losses with the PAQM loss, specifically to enhance MP3 encoded audio at 32 kbps. In listening evaluations, \(AEROMamba_{P\={S}}\) achieves 52% higher quality rating than \(AEROMamba_{P}\) when restoring compressed audio. These results demonstrate that PAQM-driven training coupled with lightweight state-space modeling yields high perceptual quality and computational efficiency in both band-limited and compressed audio scenarios.