Perceptual Noise-Masking with Music through Deep Spectral Envelope Shaping
This work addresses the issue of noise interference for people listening to music in noisy environments, representing an incremental improvement over existing methods.
The paper tackles the problem of enhancing music's ability to mask ambient noise in noisy environments by reshaping its spectral envelope using a neural network based on a psychoacoustic masking model, resulting in improved state-of-the-art performance as measured by objective metrics.
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.