# Multitask frame-level learning for few-shot sound event detection

arXiv: https://arxiv.org/abs/2403.11091 · HTML: https://mlsanity.com/paper/2403.11091/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 17 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [SALSA: Spatial Cue-Augmented Log-Spectrogram Features for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2110.00275/) (2021)
- [Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes](https://mlsanity.com/paper/1810.04989/) (2018)
- [A Mutual learning framework for Few-shot Sound Event Detection](https://mlsanity.com/paper/2110.04474/) (2021)
- [Learnable Frontends that do not Learn: Quantifying Sensitivity to Filterbank Initialisation](https://mlsanity.com/paper/2302.10014/) (2023)
- [Few-shot bioacoustic event detection at the DCASE 2023 challenge](https://mlsanity.com/paper/2306.09223/) (2023)
- [Bioacoustic Event Detection with prototypical networks and data augmentation](https://mlsanity.com/paper/2112.09006/) (2021)

## Abstract (arXiv)

This paper focuses on few-shot Sound Event Detection (SED), which aims to automatically recognize and classify sound events with limited samples. However, prevailing methods methods in few-shot SED predominantly rely on segment-level predictions, which often providing detailed, fine-grained predictions, particularly for events of brief duration. Although frame-level prediction strategies have been proposed to overcome these limitations, these strategies commonly face difficulties with prediction truncation caused by background noise. To alleviate this issue, we introduces an innovative multitask frame-level SED framework. In addition, we introduce TimeFilterAug, a linear timing mask for data augmentation, to increase the model's robustness and adaptability to diverse acoustic environments. The proposed method achieves a F-score of 63.8%, securing the 1st rank in the few-shot bioacoustic event detection category of the Detection and Classification of Acoustic Scenes and Events Challenge 2023.