Perceptual Noise-Masking with Music through Deep Spectral Envelope Shaping

• URL: http://arxiv.org/abs/2502.17527v1
• Date: Mon, 24 Feb 2025 07:58:10 GMT
• Title: Perceptual Noise-Masking with Music through Deep Spectral Envelope Shaping
• Authors: Clémentine Berger, Roland Badeau, Slim Essid,
• Abstract summary: People often listen to music in noisy environments, seeking to isolate themselves from ambient sounds.<n>We propose a neural network based on a psychoacoustic masking model to enhance the music's ability to mask ambient noise.<n>We evaluate our approach on simulated data replicating a user's experience of listening to music with headphones in a noisy environment.
• Score: 8.560397278656646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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