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Few-shot bioacoustic event detection at the DCASE 2023 challenge

2023-06-15 · Ines Nolasco, Burooj Ghani, Shubhr Singh, Ester Vidaña-Vila, Helen Whitehead, Emily Grout, Michael Emmerson, Frants Jensen, Ivan Kiskin, Joe Morford, Ariana Strandburg-Peshkin, Lisa Gill, Hanna Pamuła, Vincent Lostanlen, Dan Stowell

Few-shot bioacoustic event detection consists in detecting sound events of specified types, in varying soundscapes, while having access to only a few examples of the class of interest. This task ran as part of the DCASE challenge for the third time this year with an evaluation set expanded to include new animal species, and a new rule: ensemble models were no longer allowed. The 2023 few shot task received submissions from 6 different teams with F-scores reaching as high as 63% on the evaluation set. Here we describe the task, focusing on describing the elements that differed from previous years. We also take a look back at past editions to describe how the task has evolved. Not only have the F-score results steadily improved (40% to 60% to 63%), but the type of systems proposed have also become more complex. Sound event detection systems are no longer simple variations of the baselines provided: multiple few-shot learning methodologies are still strong contenders for the task.

📄 PDF Abstract BibTeX arXiv:2306.09223

Code (1)

c4dm/dcase-few-shot-bioacoustic 공식 구현 pytorch

Tasks

Event DetectionFew-Shot Learning Sound Event Detection

Few-shot bioacoustic event detection at the DCASE 2022 challenge

2022-07-14 · I. Nolasco, S. Singh, E. Vidana-Villa, E. Grout 외

Few-shot sound event detection is the task of detecting sound events, despite having only a few labelled examples of the class of interest. This framework is particularly useful in bioacoustics, where often there is a ne…

Event Detection Sound Event Detection Transductive Learning

Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learning

2023-09-02 · Ilyass Moummad, Romain Serizel, Nicolas Farrugia

Deep learning has been widely used recently for sound event detection and classification. Its success is linked to the availability of sufficiently large datasets, possibly with corresponding annotations when supervised …

Contrastive Learning Data Augmentation Event Detection Few-Shot Learning+1

Segment-level Metric Learning for Few-shot Bioacoustic Event Detection

2022-07-15 · Haohe Liu, Xubo Liu, Xinhao Mei, Qiuqiang Kong 외

Few-shot bioacoustic event detection is a task that detects the occurrence time of a novel sound given a few examples. Previous methods employ metric learning to build a latent space with the labeled part of different so…

Event Detection Few-Shot Learning Metric Learning Model Optimization

Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detection

2023-09-16 · Ilyass Moummad, Romain Serizel, Nicolas Farrugia

Bioacoustic sound event detection allows for better understanding of animal behavior and for better monitoring biodiversity using audio. Deep learning systems can help achieve this goal, however it is difficult to acquir…

Event Detection Few-Shot Learning Sound Event Detection

Bioacoustic Event Detection with prototypical networks and data augmentation

2021-12-16 · Mark Anderson, Naomi Harte

This report presents deep learning and data augmentation techniques used by a system entered into the Few-Shot Bioacoustic Event Detection for the DCASE2021 Challenge. The remit was to develop a few-shot learning system …

Data Augmentation Event Detection Few-Shot Learning