Online Active Learning For Sound Event Detection
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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.
Key facts. Cited by 1 paper 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
11 of 20 references.
References · 11
- 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,657
- 2020Energy-based Out-of-distribution Detectioncited by 1,299
- 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,245
- 2022Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentationcited by 719
- 2020Learning under Concept Drift: A Reviewcited by 477
- 2021Wav2CLIP: Learning Robust Audio Representations From CLIPcited by 213
- 2022HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detectioncited by 176
- 2023Active learning for data streams: a surveycited by 29
- 2022Investigating Active-learning-based Training Data Selection for Speech Spoofing Countermeasurecited by 7
- 2022Stream-based Active Learning with Verification Latency in Non-stationary Environmentscited by 5
- 2022Active Learning of Non-semantic Speech Tasks with Pretrained Modelscited by 1
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Citing papers per year: 2026: 1.
1 paper.