Bernardo V. Miranda | Searcharxiv

arXiv2026

Exploring Representation Autoencoders for Text-to-Audio Generation

Representation autoencoders (RAE), created by pairing a frozen pretrained self-supervised learning (SSL) encoder with a trained decoder, have recently demonstrated state-of-the-art performance on image generation. Their use in the audio domain, however, remains limited: text-to-audio (TTA) models are typically trained in the latent space of dedicated autoencoders where the encoder and the decoder are trained jointly with reconstruction objectives. This paper presents a study on audio representation autoencoders (AuRA) and their applicability to general audio TTA. We train decoders for two pretrained SSL audio encoders and evaluate their reconstruction quality against autoencoders used by recent TTA systems. After obtaining decoders for the SSL representations, we also train rectified flow models in the SSL and baseline latent spaces to isolate the autoencoder's effect on generation. We find that generation in SSL spaces achieves comparable or superior performance to generation in the baseline spaces, despite differences in reconstruction performance. These results highlight that reconstruction quality alone is not a sufficient proxy for generative performance.

cs.SD↗

arXiv2026

Efficient Audio Enhancement with a Differentiable Psychoacoustic Loss

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 \(AEROMamba_{PS}\), 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, \(AEROMamba_{PS}\) 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.

eess.AS↗