Few-Shot Emergency Siren Detection

@article{Cantarini2022FewShotES,
  title={Few-Shot Emergency Siren Detection},
  author={Michela Cantarini and Leonardo Gabrielli and Stefano Squartini},
  journal={Sensors (Basel, Switzerland)},
  year={2022},
  volume={22}
}
It is a well-established practice to build a robust system for sound event detection by training supervised deep learning models on large datasets, but audio data collection and labeling are often challenging and require large amounts of effort. This paper proposes a workflow based on few-shot metric learning for emergency siren detection performed in steps: prototypical networks are trained on publicly available sources or synthetic data in multiple combinations, and at inference time, the… 
View PDF
2 Citations
Background Citations
1
Methods Citations
1

2 Citations

Detection of Active Emergency Vehicles using Per-Frame CNNs and Output Smoothing

This work proposes a sequential methodology for the detection of active EVs, using an off-the-shelf CNN model operating at a frame level and a downstream smoother that accounts for the temporal aspect of EV lights.

A3CarScene: An audio-visual dataset for driving scene understanding

69 References

Proposal-based Few-shot Sound Event Detection for Speech and Environmental Sounds with Perceivers

This paper proposes novel approaches to few-shot sound event detection utilizing region proposals and the Perceiver architecture, which is capable of accurately localizing sound events with very few examples of each class of interest.

An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments

This paper is aimed at providing the audio recognition community with a carefully annotated dataset for FSL and OSR comprised of 1360 clips from 34 classes divided into pattern sounds and unwanted sounds.

Metric Learning with Background Noise Class for Few-Shot Detection of Rare Sound Events

This paper aims to achieve few-shot detection of rare sound events, from query sequence that contain not only the target events but also the other events and background noise, and proposes metric learning with background noise class for the few- shot detection.

Multi-label Few-shot Learning for Sound Event Recognition

A One-vs.-Rest episode selection strategy is proposed to mitigate the issue of the complexity of forming an episode and apply the strategy to the multi-label few-shot problem.

Few-Shot Continual Learning for Audio Classification

This work introduces a few-shot continual learning framework for audio classification, where a trained base classifier is continuously expanded to recognize novel classes based on only few labeled data at inference time, which enables fast and interactive model updates by end-users with minimal human effort.

Few-Shot Sound Event Detection

This work adapts state-of-the-art metric-based few-shot learning methods to automate the detection of similar-sounding events, requiring only one or few examples of the target event, and develops a method to automatically construct a partial set of labeled examples to reduce user labeling effort.

Few-Shot Acoustic Event Detection Via Meta Learning

This paper formulate few-shot AED problem and explores different ways of utilizing traditional supervised methods for this setting as well as a variety of meta-learning approaches, which are conventionally used to solve few- shot classification problem.

A Mutual Learning Framework for Few-Shot Sound Event Detection

This work proposes to update class prototypes with transductive inference to make the class prototypes as close to the true class center as possible, and proposes to use the updated class prototypes to fine-tune the feature extractor.

FEW-SHOT BIOACOUSTIC EVENT DETECTION WITH PROTOTYPICAL NETWORKS, KNOWLEDGE DISTILLATION AND ATTENTION TRANSFER LOSS Technical Report

The report presents the results of submission to Task 5 (Few-shot Bioacoustics Event Detection) of Detection and Classification of Acoustic Scenes and Events Challenge (DCASE) 2021. This task focuses…

A Study of Few-Shot Audio Classification

This research addresses two audio classification tasks with the Prototypical Network few-shot learning algorithm, and assess performance of various encoder architectures, which include recurrent neural networks, as well as one- and two-dimensional convolutional neural networks.
...