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Found 31 papers, 16 papers with code
Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds
Multi-label imbalanced classification poses a significant challenge in machine learning, particularly evident in bioacoustics where animal sounds often co-occur, and certain sounds are much less frequent than others.
Self-Supervised Learning for Few-Shot Bird Sound Classification
Self-supervised learning (SSL) in audio holds significant potential across various domains, particularly in situations where abundant, unlabeled data is readily available at no cost.
Performance and energy balance: a comprehensive study of state-of-the-art sound event detection systems
In recent years, deep learning systems have shown a concerning trend toward increased complexity and higher energy consumption.
Diffusion-based speech enhancement with a weighted generative-supervised learning loss
Diffusion-based generative models have recently gained attention in speech enhancement (SE), providing an alternative to conventional supervised methods.
Posterior sampling algorithms for unsupervised speech enhancement with recurrent variational autoencoder
Nevertheless, the involved iterative variational expectation-maximization (VEM) process at test time, which relies on a variational inference method, results in high computational complexity.
Unsupervised speech enhancement with diffusion-based generative models
To address this issue, we introduce an alternative approach that operates in an unsupervised manner, leveraging the generative power of diffusion models.
Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detection
Bioacoustic sound event detection allows for better understanding of animal behavior and for better monitoring biodiversity using audio.
Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learning
The bioacoustic community recasted the problem of sound event detection within the framework of few-shot learning, i. e. training a system with only few labeled examples.
SAMbA: Speech enhancement with Asynchronous ad-hoc Microphone Arrays
Speech enhancement in ad-hoc microphone arrays is often hindered by the asynchronization of the devices composing the microphone array.
Post-Processing Independent Evaluation of Sound Event Detection Systems
It summarizes the system performance over a range of operating modes resulting from varying the decision threshold that is used to translate the system output scores into a binary detection output.
Fast and efficient speech enhancement with variational autoencoders
Unsupervised speech enhancement based on variational autoencoders has shown promising performance compared with the commonly used supervised methods.
A weighted-variance variational autoencoder model for speech enhancement
A zero-mean complex-valued Gaussian distribution is usually assumed for the generative model, where the speech information is encoded in the variance as a function of a latent variable.
Audio-visual speech enhancement with a deep Kalman filter generative model
Deep latent variable generative models based on variational autoencoder (VAE) have shown promising performance for audiovisual speech enhancement (AVSE).
Description and analysis of novelties introduced in DCASE Task 4 2022 on the baseline system
The aim of the Detection and Classification of Acoustic Scenes and Events Challenge Task 4 is to evaluate systems for the detection of sound events in domestic environments using an heterogeneous dataset.
A benchmark of state-of-the-art sound event detection systems evaluated on synthetic soundscapes
A last experiment is proposed in order to study the impact of non-target events on systems outputs.
Threshold Independent Evaluation of Sound Event Detection Scores
Performing an adequate evaluation of sound event detection (SED) systems is far from trivial and is still subject to ongoing research.
AUTOMATED AUDIO CAPTIONING BY FINE-TUNING BART WITH AUDIOSET TAGS
utomated audio captioning is the multimodal task of describing environmental audio recordings with fluent natural language.
Ranked #7 on
Audio captioning
on AudioCaps
The impact of non-target events in synthetic soundscapes for sound event detection
Detection and Classification Acoustic Scene and Events Challenge 2021 Task 4 uses a heterogeneous dataset that includes both recorded and synthetic soundscapes.
Attention-based distributed speech enhancement for unconstrained microphone arrays with varying number of nodes
Speech enhancement promises higher efficiency in ad-hoc microphone arrays than in constrained microphone arrays thanks to the wide spatial coverage of the devices in the acoustic scene.
DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arrays
Deep neural network (DNN)-based speech enhancement algorithms in microphone arrays have now proven to be efficient solutions to speech understanding and speech recognition in noisy environments.
What's All the FUSS About Free Universal Sound Separation Data?
We introduce the Free Universal Sound Separation (FUSS) dataset, a new corpus for experiments in separating mixtures of an unknown number of sounds from an open domain of sound types.
Distributed speech separation in spatially unconstrained microphone arrays
We propose a distributed algorithm that can process spatial information in a spatially unconstrained microphone array.
Improving Sound Event Detection Metrics: Insights from DCASE 2020
The ranking of sound event detection (SED) systems may be biased by assumptions inherent to evaluation criteria and to the choice of an operating point.
UIAI System for Short-Duration Speaker Verification Challenge 2020
Our primary submission to the challenge is the fusion of seven subsystems which yields a normalized minimum detection cost function (minDCF) of 0. 072 and an equal error rate (EER) of 2. 14% on the evaluation set.
Foreground-Background Ambient Sound Scene Separation
Ambient sound scenes typically comprise multiple short events occurring on top of a somewhat stationary background.
DNN-Based Distributed Multichannel Mask Estimation for Speech Enhancement in Microphone Arrays
Multichannel processing is widely used for speech enhancement but several limitations appear when trying to deploy these solutions to the real-world.
Limitations of weak labels for embedding and tagging
Many datasets and approaches in ambient sound analysis use weakly labeled data. Weak labels are employed because annotating every data sample with a strong label is too expensive. Yet, their impact on the performance in comparison to strong labels remains unclear. Indeed, weak labels must often be dealt with at the same time as other challenges, namely multiple labels per sample, unbalanced classes and/or overlapping events. In this paper, we formulate a supervised learning problem which involves weak labels. We create a dataset that focuses on the difference between strong and weak labels as opposed to other challenges.
Joint NN-Supported Multichannel Reduction of Acoustic Echo, Reverberation and Noise
We consider the problem of simultaneous reduction of acoustic echo, reverberation and noise.
The Speed Submission to DIHARD II: Contributions & Lessons Learned
This paper describes the speaker diarization systems developed for the Second DIHARD Speech Diarization Challenge (DIHARD II) by the Speed team.
Sound event detection in domestic environments withweakly labeled data and soundscape synthesis
This paper presents Task 4 of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge and provides a first analysis of the challenge results.
Ranked #8 on
Sound Event Detection
on DESED
Rank-1 Constrained Multichannel Wiener Filter for Speech Recognition in Noisy Environments
Multichannel linear filters, such as the Multichannel Wiener Filter (MWF) and the Generalized Eigenvalue (GEV) beamformer are popular signal processing techniques which can improve speech recognition performance.
