Search Results for author: Giorgio Fabbro

Found 6 papers, 3 papers with code

Automatic music mixing with deep learning and out-of-domain data

1 code implementation24 Aug 2022 Marco A. Martínez-Ramírez, Wei-Hsiang Liao, Giorgio Fabbro, Stefan Uhlich, Chihiro Nagashima, Yuki Mitsufuji

Music mixing traditionally involves recording instruments in the form of clean, individual tracks and blending them into a final mixture using audio effects and expert knowledge (e. g., a mixing engineer).

Distortion Audio Effects: Learning How to Recover the Clean Signal

no code implementations3 Feb 2022 Johannes Imort, Giorgio Fabbro, Marco A. Martínez Ramírez, Stefan Uhlich, Yuichiro Koyama, Yuki Mitsufuji

Given the recent advances in music source separation and automatic mixing, removing audio effects in music tracks is a meaningful step toward developing an automated remixing system.

Music Source Separation

Music Demixing Challenge 2021

1 code implementation31 Aug 2021 Yuki Mitsufuji, Giorgio Fabbro, Stefan Uhlich, Fabian-Robert Stöter, Alexandre Défossez, Minseok Kim, Woosung Choi, Chin-Yun Yu, Kin-Wai Cheuk

The main differences compared with the past challenges are 1) the competition is designed to more easily allow machine learning practitioners from other disciplines to participate, 2) evaluation is done on a hidden test set created by music professionals dedicated exclusively to the challenge to assure the transparency of the challenge, i. e., the test set is not accessible from anyone except the challenge organizers, and 3) the dataset provides a wider range of music genres and involved a greater number of mixing engineers.

Music Source Separation

Training Speech Enhancement Systems with Noisy Speech Datasets

no code implementations26 May 2021 Koichi Saito, Stefan Uhlich, Giorgio Fabbro, Yuki Mitsufuji

Furthermore, we propose a noise augmentation scheme for mixture-invariant training (MixIT), which allows using it also in such scenarios.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

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