Reverb Conversion of Mixed Vocal Tracks Using an End-to-end Convolutional Deep Neural Network
This addresses the challenge for music producers and engineers in reproducing reverb effects, offering a novel automated solution, though it is incremental as it applies deep learning to a specific audio processing task.
The paper tackles the problem of converting musical reverb between mixed vocal tracks, proposing an end-to-end convolutional deep neural network that can apply reverb from a reference track to a source track or perform de-reverberation, achieving a preferred rate of 64.8% in perceptual evaluation.
Reverb plays a critical role in music production, where it provides listeners with spatial realization, timbre, and texture of the music. Yet, it is challenging to reproduce the musical reverb of a reference music track even by skilled engineers. In response, we propose an end-to-end system capable of switching the musical reverb factor of two different mixed vocal tracks. This method enables us to apply the reverb of the reference track to the source track to which the effect is desired. Further, our model can perform de-reverberation when the reference track is used as a dry vocal source. The proposed model is trained in combination with an adversarial objective, which makes it possible to handle high-resolution audio samples. The perceptual evaluation confirmed that the proposed model can convert the reverb factor with the preferred rate of 64.8%. To the best of our knowledge, this is the first attempt to apply deep neural networks to converting music reverb of vocal tracks.