Zobrazeno 1 - 10
of 430
pro vyhledávání: '"Falk, Tiago"'
Autor:
Zhu, Yi, Falk, Tiago
Speech is known to carry health-related attributes, which has emerged as a novel venue for remote and long-term health monitoring. However, existing models are usually tailored for a specific type of disease, and have been shown to lack generalizabil
Externí odkaz:
http://arxiv.org/abs/2406.18731
Autor:
Abdollahi, Mahsa, Zhu, Yi, Guimarães, Heitor R., Coallier, Nico, Maucourt, Ségolène, Giovenazzo, Pierre, Falk, Tiago H.
In this paper, we present a multimodal dataset obtained from a honey bee colony in Montr\'eal, Quebec, Canada, spanning the years of 2021 to 2022. This apiary comprised 10 beehives, with microphones recording more than 2000 hours of high quality raw
Externí odkaz:
http://arxiv.org/abs/2406.03657
Autor:
Guimarães, Heitor R., Pimentel, Arthur, Avila, Anderson R., Rezagholizadeh, Mehdi, Chen, Boxing, Falk, Tiago H.
Self-supervised speech representation learning enables the extraction of meaningful features from raw waveforms. These features can then be efficiently used across multiple downstream tasks. However, two significant issues arise when considering the
Externí odkaz:
http://arxiv.org/abs/2403.08654
Autor:
L'Hommedieu, Michelle, L'Hommedieu, Justin, Begay, Cynthia, Schenone, Alison, Dimitropoulou, Lida, Margolin, Gayla, Falk, Tiago, Ferrara, Emilio, Lerman, Kristina, Narayanan, Shrikanth
Publikováno v:
JMIR mHealth and uHealth, Vol 7, Iss 12, p e13305 (2019)
Although traditional methods of data collection in naturalistic settings can shed light on constructs of interest to researchers, advances in sensor-based technology allow researchers to capture continuous physiological and behavioral data to provide
Externí odkaz:
https://doaj.org/article/4c89eb5fe22943d6a5a1e293dfff22b7
Autor:
Zhu, Yi, Abdollahi, Mahsa, Maucourt, Ségolène, Coallier, Nico, Guimarães, Heitor R., Giovenazzo, Pierre, Falk, Tiago H.
We present a longitudinal multi-sensor dataset collected from honey bee colonies (Apis mellifera) with rich phenotypic measurements. Data were continuously collected between May-2020 and April-2021 from 53 hives located at two apiaries in Qu\'ebec, C
Externí odkaz:
http://arxiv.org/abs/2311.10876
Autor:
Pimentel, Arthur, Guimarães, Heitor, Avila, Anderson R., Rezagholizadeh, Mehdi, Falk, Tiago H.
Recent advances with self-supervised learning have allowed speech recognition systems to achieve state-of-the-art (SOTA) word error rates (WER) while requiring only a fraction of the labeled training data needed by its predecessors. Notwithstanding,
Externí odkaz:
http://arxiv.org/abs/2309.14462
Keyword spotting (KWS) refers to the task of identifying a set of predefined words in audio streams. With the advances seen recently with deep neural networks, it has become a popular technology to activate and control small devices, such as voice as
Externí odkaz:
http://arxiv.org/abs/2309.12914
Existing deepfake speech detection systems lack generalizability to unseen attacks (i.e., samples generated by generative algorithms not seen during training). Recent studies have explored the use of universal speech representations to tackle this is
Externí odkaz:
http://arxiv.org/abs/2309.08099
Autor:
Chemudupati, Vamsikrishna, Tahaei, Marzieh, Guimaraes, Heitor, Pimentel, Arthur, Avila, Anderson, Rezagholizadeh, Mehdi, Chen, Boxing, Falk, Tiago
Large self-supervised pre-trained speech models have achieved remarkable success across various speech-processing tasks. The self-supervised training of these models leads to universal speech representations that can be used for different downstream
Externí odkaz:
http://arxiv.org/abs/2305.14546
Unsupervised speech models are becoming ubiquitous in the speech and machine learning communities. Upstream models are responsible for learning meaningful representations from raw audio. Later, these representations serve as input to downstream model
Externí odkaz:
http://arxiv.org/abs/2305.05443