Zobrazeno 1 - 10
of 672
pro vyhledávání: '"Jonathan Le"'
Autor:
Stefan Uhlich, Giorgio Fabbro, Masato Hirano, Shusuke Takahashi, Gordon Wichern, Jonathan Le Roux, Dipam Chakraborty, Sharada Mohanty, Kai Li, Yi Luo, Jianwei Yu, Rongzhi Gu, Roman Solovyev, Alexander Stempkovskiy, Tatiana Habruseva, Mikhail Sukhovei, Yuki Mitsufuji
Publikováno v:
Transactions of the International Society for Music Information Retrieval, Vol 7, Iss 1, Pp 44–62-44–62 (2024)
This paper summarizes the cinematic demixing (CDX) track of the Sound Demixing Challenge 2023 (SDX’23). We provide a comprehensive summary of the challenge setup, detailing the structure of the competition and the datasets used. Especially, we deta
Externí odkaz:
https://doaj.org/article/4d5fad42081f45e48007f343da8811e3
Autor:
Yu Guo, Minjie Shen, Qiping Dong, Natasha M. Méndez-Albelo, Sabrina X. Huang, Carissa L. Sirois, Jonathan Le, Meng Li, Ezra D. Jarzembowski, Keegan A. Schoeller, Michael E. Stockton, Vanessa L. Horner, André M. M. Sousa, Yu Gao, Birth Defects Research Laboratory, Jon E. Levine, Daifeng Wang, Qiang Chang, Xinyu Zhao
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-23 (2023)
Abstract Fragile X messenger ribonucleoprotein 1 protein (FMRP) binds many mRNA targets in the brain. The contribution of these targets to fragile X syndrome (FXS) and related autism spectrum disorder (ASD) remains unclear. Here, we show that FMRP de
Externí odkaz:
https://doaj.org/article/c712033063bc4873a8e01bd8ebf89faa
Autor:
Saijo, Kohei, Ebbers, Janek, Germain, François G., Khurana, Sameer, Wichern, Gordon, Roux, Jonathan Le
The goal of text-queried target sound extraction (TSE) is to extract from a mixture a sound source specified with a natural-language caption. While it is preferable to have access to large-scale text-audio pairs to address a variety of text prompts,
Externí odkaz:
http://arxiv.org/abs/2409.13152
Reverberation as supervision (RAS) is a framework that allows for training monaural speech separation models from multi-channel mixtures in an unsupervised manner. In RAS, models are trained so that sources predicted from a mixture at an input channe
Externí odkaz:
http://arxiv.org/abs/2408.03438
Time-frequency (TF) domain dual-path models achieve high-fidelity speech separation. While some previous state-of-the-art (SoTA) models rely on RNNs, this reliance means they lack the parallelizability, scalability, and versatility of Transformer blo
Externí odkaz:
http://arxiv.org/abs/2408.03440
Autor:
Yin, Jie, Luo, Andrew, Du, Yilun, Cherian, Anoop, Marks, Tim K., Roux, Jonathan Le, Gan, Chuang
We study the problem of multimodal physical scene understanding, where an embodied agent needs to find fallen objects by inferring object properties, direction, and distance of an impact sound source. Previous works adopt feed-forward neural networks
Externí odkaz:
http://arxiv.org/abs/2407.11333
Publikováno v:
INTERSPEECH, Sep 2024, Kos Island, Greece
Single-channel speech dereverberation aims at extracting a dry speech signal from a recording affected by the acoustic reflections in a room. However, most current deep learning-based approaches for speech dereverberation are not interpretable for ro
Externí odkaz:
http://arxiv.org/abs/2407.08657
Sound event detection is the task of recognizing sounds and determining their extent (onset/offset times) within an audio clip. Existing systems commonly predict sound presence confidence in short time frames. Then, thresholding produces binary frame
Externí odkaz:
http://arxiv.org/abs/2406.04212
We introduce Self-Monitored Inference-Time INtervention (SMITIN), an approach for controlling an autoregressive generative music transformer using classifier probes. These simple logistic regression probes are trained on the output of each attention
Externí odkaz:
http://arxiv.org/abs/2404.02252
In music source separation, a standard training data augmentation procedure is to create new training samples by randomly combining instrument stems from different songs. These random mixes have mismatched characteristics compared to real music, e.g.
Externí odkaz:
http://arxiv.org/abs/2402.18407