Stefan Uhlich | DeepAI

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∙ 08/14/2023

The Sound Demixing Challenge 2023 x2013 Cinematic Demixing Track

This paper summarizes the cinematic demixing (CDX) track of the Sound De...

0 Stefan Uhlich, et al. ∙

∙ 08/14/2023

The Sound Demixing Challenge 2023 x2013 Music Demixing Track

This paper summarizes the music demixing (MDX) track of the Sound Demixi...

0 Giorgio Fabbro, et al. ∙

∙ 05/13/2023

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

This paper presents the crossing scheme (X-scheme) for improving the per...

0 Ryosuke Sawata, et al. ∙

∙ 03/07/2023

Improving Self-Supervised Learning for Audio Representations by Feature Diversity and Decorrelation

Self-supervised learning (SSL) has recently shown remarkable results in ...

0 Bac Nguyen, et al. ∙

∙ 12/13/2022

A Statistical Model for Predicting Generalization in Few-Shot Classification

The estimation of the generalization error of classifiers often relies o...

0 Yassir Bendou, et al. ∙

∙ 11/04/2022

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

We propose an end-to-end music mixing style transfer system that convert...

0 Junghyun Koo, et al. ∙

∙ 08/24/2022

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

Music mixing traditionally involves recording instruments in the form of...

0 Marco A. Martínez Ramírez, et al. ∙

∙ 03/21/2022

Differentiable Duration Modeling for End-to-End Text-to-Speech

Parallel text-to-speech (TTS) models have recently enabled fast and high...

0 Bac Nguyen, et al. ∙

∙ 02/03/2022

Removing Distortion Effects in Music Using Deep Neural Networks

Audio effects are an essential element in the context of music productio...

0 Johannes Imort, et al. ∙

∙ 10/13/2021

Music Source Separation with Deep Equilibrium Models

While deep neural network-based music source separation (MSS) is very ef...

0 Yuichiro Koyama, et al. ∙

∙ 10/08/2021

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

In recent years, many deep learning techniques for single-channel sound ...

0 Ali Aroudi, et al. ∙

∙ 08/31/2021

Music Demixing Challenge 2021

Music source separation has been intensively studied in the last decade ...

0 Yuki Mitsufuji, et al. ∙

∙ 05/26/2021

Training Speech Enhancement Systems with Noisy Speech Datasets

Recently, deep neural network (DNN)-based speech enhancement (SE) system...

0 Koichi Saito, et al. ∙

∙ 03/24/2021

DNN Quantization with Attention

Low-bit quantization of network weights and activations can drastically ...

0 Ghouthi Boukli Hacene, et al. ∙

∙ 10/08/2020

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

This paper proposes several improvements for music separation with deep ...

0 Ryosuke Sawata, et al. ∙

∙ 05/23/2020

Exploring the Best Loss Function for DNN-Based Low-latency Speech Enhancement with Temporal Convolutional Networks

Recently, deep neural networks (DNNs) have been successfully used for sp...

0 Yuichiro Koyama, et al. ∙

∙ 05/15/2020

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

In recent years generative adversarial network (GAN) based models have b...

0 Mohammad Asif Khan, et al. ∙

∙ 11/05/2019

Closing the Training/Inference Gap for Deep Attractor Networks

This paper improves the deep attractor network (DANet) approach by closi...

0 Cyril Cadoux, et al. ∙

∙ 05/27/2019

Differentiable Quantization of Deep Neural Networks

We propose differentiable quantization (DQ) for efficient deep neural ne...

0 Stefan Uhlich, et al. ∙

∙ 11/13/2018

Iteratively Training Look-Up Tables for Network Quantization

Operating deep neural networks on devices with limited resources require...

0 Fabien Cardinaux, et al. ∙

∙ 07/07/2018

Improving DNN-based Music Source Separation using Phase Features

Music source separation with deep neural networks typically relies only ...

0 Joachim Muth, et al. ∙

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