Perceptual Noise-Masking with Music through Deep Spectral Envelope Shaping

TL;DR

This paper introduces a neural network that reshapes music's spectral envelope to improve noise masking in noisy environments, enhancing listening experience while preserving audio quality.

Contribution

A novel psychoacoustic masking model using deep spectral envelope shaping to enhance noise masking in music through neural network training.

Findings

Improved noise masking performance over existing methods.

Effective preservation of original music quality.

Validated on simulated noisy listening scenarios.

Abstract

People often listen to music in noisy environments, seeking to isolate themselves from ambient sounds. Indeed, a music signal can mask some of the noise's frequency components due to the effect of simultaneous masking. In this article, we propose a neural network based on a psychoacoustic masking model, designed to enhance the music's ability to mask ambient noise by reshaping its spectral envelope with predicted filter frequency responses. The model is trained with a perceptual loss function that balances two constraints: effectively masking the noise while preserving the original music mix and the user's chosen listening level. We evaluate our approach on simulated data replicating a user's experience of listening to music with headphones in a noisy environment. The results, based on defined objective metrics, demonstrate that our system improves the state of the art.