no code implementations • 5 Jun 2024 • Mahsa Abdollahi, Yi Zhu, Heitor R. Guimarães, Nico Coallier, Ségolène Maucourt, Pierre Giovenazzo, Tiago H. Falk
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.
1 code implementation • 13 Mar 2024 • Heitor R. Guimarães, Arthur Pimentel, Anderson R. Avila, Mehdi Rezagholizadeh, Boxing Chen, Tiago H. Falk
Lastly, we show that the proposed recipe can be applied to other distillation methodologies, such as the recent DPWavLM.
1 code implementation • 17 Nov 2023 • Yi Zhu, Mahsa Abdollahi, Ségolène Maucourt, Nico Coallier, Heitor R. Guimarães, Pierre Giovenazzo, Tiago H. Falk
We then provide an overview of the phenotypic data distribution as well as a visualization of the sensor data patterns.
no code implementations • 25 Sep 2023 • Arthur Pimentel, Heitor Guimarães, Anderson R. Avila, Mehdi Rezagholizadeh, Tiago H. Falk
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.
no code implementations • 22 Sep 2023 • Heitor R. Guimarães, Arthur Pimentel, Anderson Avila, Tiago H. Falk
Keyword spotting (KWS) refers to the task of identifying a set of predefined words in audio streams.
1 code implementation • 15 Sep 2023 • Yi Zhu, Saurabh Powar, Tiago H. Falk
Existing deepfake speech detection systems lack generalizability to unseen attacks (i. e., samples generated by generative algorithms not seen during training).
no code implementations • 9 May 2023 • Heitor Guimarães, Arthur Pimentel, Anderson Avila, Mehdi Rezagholizadeh, Tiago H. Falk
Later, these representations serve as input to downstream models to solve a number of tasks, such as keyword spotting or emotion recognition.
no code implementations • 5 Apr 2023 • Yi Zhu, Mohamed Imoussaïne-Aïkous, Carolyn Côté-Lussier, Tiago H. Falk
We validate the effectiveness of the anonymization methods, compare their computational complexity, and quantify the impact across different testing scenarios for both within- and across-dataset conditions.
no code implementations • 18 Feb 2023 • Heitor R. Guimarães, Arthur Pimentel, Anderson R. Avila, Mehdi Rezagholizadeh, Boxing Chen, Tiago H. Falk
The proposed layer-wise distillation recipe is evaluated on top of three well-established universal representations, as well as with three downstream tasks.
no code implementations • 12 Nov 2022 • Heitor R. Guimarães, Arthur Pimentel, Anderson R. Avila, Mehdi Rezagholizadeh, Tiago H. Falk
Self-supervised speech representation learning aims to extract meaningful factors from the speech signal that can later be used across different downstream tasks, such as speech and/or emotion recognition.
no code implementations • 18 Mar 2020 • Karel Mundnich, Brandon M. Booth, Michelle L'Hommedieu, Tiantian Feng, Benjamin Girault, Justin L'Hommedieu, Mackenzie Wildman, Sophia Skaaden, Amrutha Nadarajan, Jennifer L. Villatte, Tiago H. Falk, Kristina Lerman, Emilio Ferrara, Shrikanth Narayanan
We designed the study to investigate the use of off-the-shelf wearable and environmental sensors to understand individual-specific constructs such as job performance, interpersonal interaction, and well-being of hospital workers over time in their natural day-to-day job settings.
2 code implementations • 3 Nov 2019 • Isabela Albuquerque, João Monteiro, Mohammad Darvishi, Tiago H. Falk, Ioannis Mitliagkas
In this work, we tackle such problem by focusing on domain generalization: a formalization where the data generating process at test time may yield samples from never-before-seen domains (distributions).
Ranked #70 on
Domain Generalization
on PACS
no code implementations • 20 Jun 2019 • Isabela Albuquerque, João Monteiro, Olivier Rosanne, Abhishek Tiwari, Jean-François Gagnon, Tiago H. Falk
Besides shedding light on the assumptions that hold for a particular dataset, the estimates of statistical shifts obtained with the proposed approach can be used for investigating other aspects of a machine learning pipeline, such as quantitatively assessing the effectiveness of domain adaptation strategies.
1 code implementation • 23 Jan 2019 • Isabela Albuquerque, João Monteiro, Tiago H. Falk
Afterwards, a recurrent model is trained with the goal of providing a sequence of inputs to the previously trained frames generator, thus yielding scenes which look natural.
3 code implementations • 16 Jan 2019 • Yannick Roy, Hubert Banville, Isabela Albuquerque, Alexandre Gramfort, Tiago H. Falk, Jocelyn Faubert
To help the field progress, we provide a list of recommendations for future studies and we make our summary table of DL and EEG papers available and invite the community to contribute.
no code implementations • 21 Feb 2018 • João Monteiro, Isabela Albuquerque, Zahid Akhtar, Tiago H. Falk
Non-linear binary classifiers trained on top of our proposed features can achieve a high detection rate (>90%) in a set of white-box attacks and maintain such performance when tested against unseen attacks.