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• W3133859501 abstract "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." @default.
• W3133859501 created "2021-03-15" @default.
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• W3133859501 date "2021-03-03" @default.
• W3133859501 modified "2023-09-27" @default.
• W3133859501 title "Reverb Conversion of Mixed Vocal Tracks Using an End-to-end Convolutional Deep Neural Network" @default.
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• W3133859501 hasPublicationYear "2021" @default.
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