Zobrazeno 1 - 10
of 157
pro vyhledávání: '"Tzanetakis, George"'
Autor:
Zhao, Yichun, Tzanetakis, George
Publikováno v:
Conference on Sonification of Health and Environmental Data (SoniHED 2022)
Sonification can provide valuable insights about data but most existing approaches are not designed to be controlled by the user in an interactive fashion. Interactions enable the designer of the sonification to more rapidly experiment with sound des
Externí odkaz:
http://arxiv.org/abs/2404.08813
Autor:
Pedersoli, Fabrizio, Wiebe, Dryden, Banitalebi, Amin, Zhang, Yong, Tzanetakis, George, Yi, Kwang Moo
We propose a new framework for extracting visual information about a scene only using audio signals. Audio-based methods can overcome some of the limitations of vision-based methods i.e., they do not require "line-of-sight", are robust to occlusions
Externí odkaz:
http://arxiv.org/abs/2208.02337
Autor:
Turian, Joseph, Shier, Jordie, Khan, Humair Raj, Raj, Bhiksha, Schuller, Björn W., Steinmetz, Christian J., Malloy, Colin, Tzanetakis, George, Velarde, Gissel, McNally, Kirk, Henry, Max, Pinto, Nicolas, Noufi, Camille, Clough, Christian, Herremans, Dorien, Fonseca, Eduardo, Engel, Jesse, Salamon, Justin, Esling, Philippe, Manocha, Pranay, Watanabe, Shinji, Jin, Zeyu, Bisk, Yonatan
What audio embedding approach generalizes best to a wide range of downstream tasks across a variety of everyday domains without fine-tuning? The aim of the HEAR benchmark is to develop a general-purpose audio representation that provides a strong bas
Externí odkaz:
http://arxiv.org/abs/2203.03022
We release synth1B1, a multi-modal audio corpus consisting of 1 billion 4-second synthesized sounds, paired with the synthesis parameters used to generate them. The dataset is 100x larger than any audio dataset in the literature. We also introduce to
Externí odkaz:
http://arxiv.org/abs/2104.12922
Publikováno v:
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020
We introduce a data-driven approach to automatic pitch correction of solo singing performances. The proposed approach predicts note-wise pitch shifts from the relationship between the respective spectrograms of the singing and accompaniment. This app
Externí odkaz:
http://arxiv.org/abs/2002.05511
We describe a machine-learning approach to pitch correcting a solo singing performance in a karaoke setting, where the solo voice and accompaniment are on separate tracks. The proposed approach addresses the situation where no musical score of the vo
Externí odkaz:
http://arxiv.org/abs/1902.00956
There are many applications scenarios for which the computational performance and memory footprint of the prediction phase of Deep Neural Networks (DNNs) needs to be optimized. Binary Neural Networks (BDNNs) have been shown to be an effective way of
Externí odkaz:
http://arxiv.org/abs/1705.07175
The Orchive is a large collection of over 20,000 hours of audio recordings from the OrcaLab research facility located off the northern tip of Vancouver Island. It contains recorded orca vocalizations from the 1980 to the present time and is one of th
Externí odkaz:
http://arxiv.org/abs/1307.0589
Publikováno v:
Computer Music Journal, 2006 Jul 01. 30(2), 42-62.
Externí odkaz:
https://www.jstor.org/stable/3682003
Publikováno v:
Computer Music Journal, 2004 Jul 01. 28(2), 24-33.
Externí odkaz:
https://www.jstor.org/stable/3681824