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Found 33 papers, 15 papers with code
Sample Compression for Continual Learning
Continual learning algorithms aim to learn from a sequence of tasks, making the training distribution non-stationary.
ReTreever: Tree-based Coarse-to-Fine Representations for Retrieval
In this paper, we propose a tree-based method for organizing and representing reference documents at various granular levels, which offers the flexibility to balance cost and utility, and eases the inspection of the corpus content and retrieval operations.
FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks
Large language models have revolutionized natural language processing through self-supervised pretraining on massive datasets.
Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood Planers
Specifically, our convolutional autoencoder with skip connections (Skip-CAE) and our Skip-CAE transformer outperform the DCASE autoencoder baseline, one-class SVM, isolation forest and a published convolutional autoencoder architecture, respectively obtaining an area under the ROC curve of 0. 846 and 0. 875 on a dataset of real-factory planer sounds.
Investigating the Effectiveness of Explainability Methods in Parkinson's Detection from Speech
Speech impairments in Parkinson's disease (PD) provide significant early indicators for diagnosis.
Adaptation Odyssey in LLMs: Why Does Additional Pretraining Sometimes Fail to Improve?
Our further token-level perplexity observations reveals that the perplexity degradation is due to a handful of tokens that are not informative about the domain.
Dynamic HumTrans: Humming Transcription Using CNNs and Dynamic Programming
We propose a novel approach for humming transcription that combines a CNN-based architecture with a dynamic programming-based post-processing algorithm, utilizing the recently introduced HumTrans dataset.
LMAC-TD: Producing Time Domain Explanations for Audio Classifiers
Neural networks are typically black-boxes that remain opaque with regards to their decision mechanisms.
Open-Source Conversational AI with SpeechBrain 1.0
This paper presents SpeechBrain 1. 0, a significant milestone in the evolution of the toolkit, which now has over 200 recipes for speech, audio, and language processing tasks, and more than 100 models available on Hugging Face.
DASB -- Discrete Audio and Speech Benchmark
Discrete audio tokens have recently gained considerable attention for their potential to connect audio and language processing, enabling the creation of modern multimodal large language models.
How Should We Extract Discrete Audio Tokens from Self-Supervised Models?
Discrete audio tokens have recently gained attention for their potential to bridge the gap between audio and language processing.
Phoneme Discretized Saliency Maps for Explainable Detection of AI-Generated Voice
In this paper, we propose Phoneme Discretized Saliency Maps (PDSM), a discretization algorithm for saliency maps that takes advantage of phoneme boundaries for explainable detection of AI-generated voice.
Listenable Maps for Zero-Shot Audio Classifiers
Interpreting the decisions of deep learning models, including audio classifiers, is crucial for ensuring the transparency and trustworthiness of this technology.
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 #32 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).
