Perceptual Noise-Masking with Music through Deep Spectral Envelope Shaping
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915171240247296 |
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| author | Berger, Clémentine Badeau, Roland Essid, Slim |
| author_facet | Berger, Clémentine Badeau, Roland Essid, Slim |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_17527 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Perceptual Noise-Masking with Music through Deep Spectral Envelope Shaping Berger, Clémentine Badeau, Roland Essid, Slim Sound Artificial Intelligence Audio and Speech Processing Signal Processing 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. |
| title | Perceptual Noise-Masking with Music through Deep Spectral Envelope Shaping |
| topic | Sound Artificial Intelligence Audio and Speech Processing Signal Processing |
| url | https://arxiv.org/abs/2502.17527 |