Self-supervised learning for infant cry analysis

no code implementations • 2 May 2023 • Arsenii Gorin, Cem Subakan, Sajjad Abdoli, Junhao Wang, Samantha Latremouille, Charles Onu

In this paper, we explore self-supervised learning (SSL) for analyzing a first-of-its-kind database of cry recordings containing clinical indications of more than a thousand newborns.

Domain Adaptation Self-Supervised Learning

Knowledge Distillation for Multi-Target Domain Adaptation in Real-Time Person Re-Identification

1 code implementation • 12 May 2022 • Félix Remigereau, Djebril Mekhazni, Sajjad Abdoli, Le Thanh Nguyen-Meidine, Rafael M. O. Cruz, Eric Granger

Despite the recent success of deep learning architectures, person re-identification (ReID) remains a challenging problem in real-word applications.

Domain Adaptation Knowledge Distillation +2

Speaker detection in the wild: Lessons learned from JSALT 2019

1 code implementation • 2 Dec 2019 • Paola Garcia, Jesus Villalba, Herve Bredin, Jun Du, Diego Castan, Alejandrina Cristia, Latane Bullock, Ling Guo, Koji Okabe, Phani Sankar Nidadavolu, Saurabh Kataria, Sizhu Chen, Leo Galmant, Marvin Lavechin, Lei Sun, Marie-Philippe Gill, Bar Ben-Yair, Sajjad Abdoli, Xin Wang, Wassim Bouaziz, Hadrien Titeux, Emmanuel Dupoux, Kong Aik Lee, Najim Dehak

This paper presents the problems and solutions addressed at the JSALT workshop when using a single microphone for speaker detection in adverse scenarios.

Audio and Speech Processing Sound

Cross-Representation Transferability of Adversarial Attacks: From Spectrograms to Audio Waveforms

1 code implementation • 22 Oct 2019 • Karl Michel Koerich, Mohammad Esmaeilpour, Sajjad Abdoli, Alceu de Souza Britto Jr., Alessandro Lameiras Koerich

Furthermore, the audio waveforms reconstructed from the perturbed spectrograms are also able to fool a 1D CNN trained on the original audio.

Universal Adversarial Audio Perturbations

1 code implementation • arXiv preprint 2019 • Sajjad Abdoli, Luiz G. Hafemann, Jerome Rony, Ismail Ben Ayed, Patrick Cardinal, Alessandro L. Koerich

We demonstrate the existence of universal adversarial perturbations, which can fool a family of audio classification architectures, for both targeted and untargeted attack scenarios.

Audio Classification

End-to-End Environmental Sound Classification using a 1D Convolutional Neural Network

3 code implementations • 18 Apr 2019 • Sajjad Abdoli, Patrick Cardinal, Alessandro Lameiras Koerich

In this paper, we present an end-to-end approach for environmental sound classification based on a 1D Convolution Neural Network (CNN) that learns a representation directly from the audio signal.

Environmental Sound Classification General Classification +1