End-To-End Music Remastering System Using Self-Supervised And Adversarial Training

Junghyun, Koo,; Paik, Seungryeol; Lee, Kyogu

doi:10.1109/icassp43922.2022.9746389

Cited by 3 publications

(1 citation statement)

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“…For the encoder, we compare three popular architectures commonly used in the Music Information Retrieval field: MEE the Music Effects Encoder proposed by [19]. It consists of a cascade of residual 1D convolutional layers, TE a Timbre Encoder inspired by [20].…”

Section: Analysis Network F Amentioning

confidence: 99%

Blind Estimation of Audio Effects Using an Auto-Encoder Approach and Differentiable Digital Signal Processing

Peladeau,

Peeters

2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Blind Estimation of Audio Effects (BE-AFX) aims at estimating the audio effects (AFXs) applied to an original, unprocessed audio sample solely based on the processed audio sample. To train such a system traditional approaches optimize a loss between ground truth and estimated AFX parameters. This involves knowing the exact implementation of the AFXs used for the process. In this work, we propose an alternative solution that eliminates the requirement for knowing this implementation. Instead, we introduce an auto-encoder approach, which optimizes an audio quality metric. We explore, suggest, and compare various implementations of commonly used mastering AFXs, using differential signal processing or neural approximations. Our findings demonstrate that our auto-encoder approach yields superior estimates of the audio quality produced by a chain of AFXs, compared to the traditional parameter-based approach, even if the latter provides a more accurate parameter estimation.

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Section: Analysis Network F Amentioning

confidence: 99%