Search Results for author: Stefan Uhlich

Found 20 papers, 12 papers with code

The Whole Is Greater than the Sum of Its Parts: Improving DNN-based Music Source Separation

1 code implementation13 May 2023 Ryosuke Sawata, Naoya Takahashi, Stefan Uhlich, Shusuke Takahashi, Yuki Mitsufuji

We modify the target network, i. e., the network architecture of the original DNN-based MSS, by adding bridging paths for each output instrument to share their information.

Music Source Separation

Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects

1 code implementation4 Nov 2022 Junghyun Koo, Marco A. Martínez-Ramírez, Wei-Hsiang Liao, Stefan Uhlich, Kyogu Lee, Yuki Mitsufuji

We propose an end-to-end music mixing style transfer system that converts the mixing style of an input multitrack to that of a reference song.

Contrastive Learning Disentanglement +2

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).

AutoTTS: End-to-End Text-to-Speech Synthesis through Differentiable Duration Modeling

no code implementations21 Mar 2022 Bac Nguyen, Fabien Cardinaux, Stefan Uhlich

Using this differentiable duration method, we introduce AutoTTS, a direct text-to-waveform speech synthesis model.

Decoder Speech Synthesis +1

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

TRUNet: Transformer-Recurrent-U Network for Multi-channel Reverberant Sound Source Separation

no code implementations8 Oct 2021 Ali Aroudi, Stefan Uhlich, Marc Ferras Font

We evaluate the network on a realistic and challenging reverberant dataset, generated from measured room impulse responses of an actual microphone array.

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

Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives

1 code implementation12 Feb 2021 Takuya Narihira, Javier Alonsogarcia, Fabien Cardinaux, Akio Hayakawa, Masato Ishii, Kazunori Iwaki, Thomas Kemp, Yoshiyuki Kobayashi, Lukas Mauch, Akira Nakamura, Yukio Obuchi, Andrew Shin, Kenji Suzuki, Stephen Tiedmann, Stefan Uhlich, Takuya Yashima, Kazuki Yoshiyama

While there exist a plethora of deep learning tools and frameworks, the fast-growing complexity of the field brings new demands and challenges, such as more flexible network design, speedy computation on distributed setting, and compatibility between different tools.

All for One and One for All: Improving Music Separation by Bridging Networks

5 code implementations8 Oct 2020 Ryosuke Sawata, Stefan Uhlich, Shusuke Takahashi, Yuki Mitsufuji

This paper proposes several improvements for music separation with deep neural networks (DNNs), namely a multi-domain loss (MDL) and two combination schemes.

Music Source Separation

Unsupervised Cross-Domain Speech-to-Speech Conversion with Time-Frequency Consistency

no code implementations15 May 2020 Mohammad Asif Khan, Fabien Cardinaux, Stefan Uhlich, Marc Ferras, Asja Fischer

This procedure bears the problem that the generated magnitude spectrogram may not be consistent, which is required for finding a phase such that the full spectrogram has a natural-sounding speech waveform.

Generative Adversarial Network

Open-Unmix - A Reference Implementation for Music Source Separation

1 code implementation The Journal of Open Source Software 2019 Fabian-Robert Stöter, Stefan Uhlich, Antoine Liutkus, and YukiMitsufuji

Music source separation is the task of decomposing music into its constitutive components, e. g., yielding separated stems for the vocals, bass, and drums.

Ranked #15 on Music Source Separation on MUSDB18 (using extra training data)

Music Source Separation

Iteratively Training Look-Up Tables for Network Quantization

no code implementations13 Nov 2018 Fabien Cardinaux, Stefan Uhlich, Kazuki Yoshiyama, Javier Alonso García, Stephen Tiedemann, Thomas Kemp, Akira Nakamura

In this paper we introduce a training method, called look-up table quantization, LUT-Q, which learns a dictionary and assigns each weight to one of the dictionary's values.

object-detection Object Detection +1

Cannot find the paper you are looking for? You can Submit a new open access paper.