# Online Active Learning For Sound Event Detection

arXiv: https://arxiv.org/abs/2309.14460 · HTML: https://mlsanity.com/paper/2309.14460/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Wav2CLIP: Learning Robust Audio Representations From CLIP](https://mlsanity.com/paper/2110.11499/) (2021)
- [HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection](https://mlsanity.com/paper/2202.00874/) (2022)
- [Active learning for data streams: a survey](https://mlsanity.com/paper/2302.08893/) (2023)
- [Investigating Active-learning-based Training Data Selection for Speech Spoofing Countermeasure](https://mlsanity.com/paper/2203.14553/) (2022)
- [Stream-based Active Learning with Verification Latency in Non-stationary Environments](https://mlsanity.com/paper/2204.06822/) (2022)
- [Active Learning of Non-semantic Speech Tasks with Pretrained Models](https://mlsanity.com/paper/2211.00119/) (2022)

## Built on by (most cited first, 1 of 1)

- [Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning](https://mlsanity.com/paper/2607.13555/) (2026)

## Abstract (arXiv)

Data collection and annotation is a laborious, time-consuming prerequisite for supervised machine learning tasks. Online Active Learning (OAL) is a paradigm that addresses this issue by simultaneously minimizing the amount of annotation required to train a classifier and adapting to changes in the data over the duration of the data collection process. Prior work has indicated that fluctuating class distributions and data drift are still common problems for OAL. This work presents new loss functions that address these challenges when OAL is applied to Sound Event Detection (SED). Experimental results from the SONYC dataset and two Voice-Type Discrimination (VTD) corpora indicate that OAL can reduce the time and effort required to train SED classifiers by a factor of 5 for SONYC, and that the new methods presented here successfully resolve issues present in existing OAL methods.