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Found 20 papers, 13 papers with code
Listenable Maps for Audio Classifiers
Despite the impressive performance of deep learning models across diverse tasks, their complexity poses challenges for interpretation.
Focal Modulation Networks for Interpretable Sound Classification
The increasing success of deep neural networks has raised concerns about their inherent black-box nature, posing challenges related to interpretability and trust.
CL-MASR: A Continual Learning Benchmark for Multilingual ASR
To the best of our knowledge, CL-MASR is the first continual learning benchmark for the multilingual ASR task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Audio Editing with Non-Rigid Text Prompts
We show that the proposed editing pipeline is able to create audio edits that remain faithful to the input audio.
CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice
We introduce a simple-to-follow recipe aligned to the SpeechBrain toolkit for accent classification based on Common Voice 7. 0 (English) and Common Voice 11. 0 (Italian, German, and Spanish).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Unsupervised Improvement of Audio-Text Cross-Modal Representations
In this paper, we study unsupervised approaches to improve the learning framework of such representations with unpaired text and audio.
Self-supervised learning for infant cry analysis
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.
CryCeleb: A Speaker Verification Dataset Based on Infant Cry Sounds
This paper describes the Ubenwa CryCeleb dataset - a labeled collection of infant cries - and the accompanying CryCeleb 2023 task, which is a public speaker verification challenge based on cry sounds.
Posthoc Interpretation via Quantization
In this paper, we introduce a new approach, called Posthoc Interpretation via Quantization (PIQ), for interpreting decisions made by trained classifiers.
Resource-Efficient Separation Transformer
Transformers have recently achieved state-of-the-art performance in speech separation.
Learning Representations for New Sound Classes With Continual Self-Supervised Learning
In this paper, we work on a sound recognition system that continually incorporates new sound classes.
Exploring Self-Attention Mechanisms for Speech Separation
In particular, we extend our previous findings on the SepFormer by providing results on more challenging noisy and noisy-reverberant datasets, such as LibriMix, WHAM!, and WHAMR!.
Ranked #1 on
Speech Enhancement
on WHAM!
REAL-M: Towards Speech Separation on Real Mixtures
First, we release the REAL-M dataset, a crowd-sourced corpus of real-life mixtures.
SpeechBrain: A General-Purpose Speech Toolkit
SpeechBrain is an open-source and all-in-one speech toolkit.
Attention is All You Need in Speech Separation
Transformers are emerging as a natural alternative to standard RNNs, replacing recurrent computations with a multi-head attention mechanism.
Ranked #7 on
Speech Separation
on WSJ0-3mix
Two-Step Sound Source Separation: Training on Learned Latent Targets
In the first step we learn a transform (and it's inverse) to a latent space where masking-based separation performance using oracles is optimal.
Ranked #25 on
Speech Separation
on WSJ0-2mix
Continual Learning of New Sound Classes using Generative Replay
We show that by incrementally refining a classifier with generative replay a generator that is 4% of the size of all previous training data matches the performance of refining the classifier keeping 20% of all previous training data.
Learning the Base Distribution in Implicit Generative Models
Popular generative model learning methods such as Generative Adversarial Networks (GANs), and Variational Autoencoders (VAE) enforce the latent representation to follow simple distributions such as isotropic Gaussian.
Generative Adversarial Source Separation
Generative source separation methods such as non-negative matrix factorization (NMF) or auto-encoders, rely on the assumption of an output probability density.
Spectral Learning of Mixture of Hidden Markov Models
In this paper, we propose a learning approach for the Mixture of Hidden Markov Models (MHMM) based on the Method of Moments (MoM).
