mlsanityData to 7 Oct 2026

Sound Event Detection Transformer: An Event-based End-to-End Model for Sound Event Detection

arXiv 2110.02011 ↗First posted 5 Oct 2021cs.SD cs.LG eess.AScited by 7

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Abstract (arXiv)

Sound event detection (SED) has gained increasing attention with its wide application in surveillance, video indexing, etc. Existing models in SED mainly generate frame-level prediction, converting it into a sequence multi-label classification problem. A critical issue with the frame-based model is that it pursues the best frame-level prediction rather than the best event-level prediction. Besides, it needs post-processing and cannot be trained in an end-to-end way. This paper firstly presents the one-dimensional Detection Transformer (1D-DETR), inspired by Detection Transformer for image object detection. Furthermore, given the characteristics of SED, the audio query branch and a one-to-many matching strategy for fine-tuning the model are added to 1D-DETR to form Sound Event Detection Transformer (SEDT). To our knowledge, SEDT is the first event-based and end-to-end SED model. Experiments are conducted on the URBAN-SED dataset and the DCASE2019 Task4 dataset, and both show that SEDT can achieve competitive performance.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 23 of its 52 references that are in the map. First posted on arXiv 5 Oct 2021.

Builds on

23 of 52 references.

References · 23

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  9. 2018A Comparison of Five Multiple Instance Learning Pooling Functions for Sound Event Detection with Weak Labelingcited by 37
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  11. 2017Sound Event Detection Using Spatial Features and Convolutional Recurrent Neural Networkcited by 14
  12. 2018Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environmentscited by 10
11 more
  1. 2019Guided Learning Convolution System for DCASE 2019 Task 4cited by 8
  2. 2019Sound event detection via dilated convolutional recurrent neural networkscited by 8
  3. 2018SONYC: A System for the Monitoring, Analysis and Mitigation of Urban Noise Pollutioncited by 5
  4. 2019Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detectioncited by 5
  5. 2019Evaluation of post-processing algorithms for polyphonic sound event detectioncited by 5
  6. 2016Robust Audio Event Recognition with 1-Max Pooling Convolutional Neural Networkscited by 3
  7. 2019Guided learning for weakly-labeled semi-supervised sound event detectioncited by 3
  8. 2020DETR for Crowd Pedestrian Detectioncited by 3
  9. 2019Weakly Labeled Sound Event Detection Using Tri-training and Adversarial Learningcited by 2
  10. 2020Multi-Branch Learning for Weakly-Labeled Sound Event Detectioncited by 2
  11. 2020Learning generic feature representation with synthetic data for weakly-supervised sound event detection by inter-frame distance losscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 3; 2026: 2.

7 papers.

  1. 2025SpeechR: A Benchmark for Speech Reasoning in Large Audio-Language Modelscited by 14
  2. 2025Detect Any Sound: Open-Vocabulary Sound Event Detection with Multi-Modal Queriescited by 9
  3. 2023DiffSED: Sound Event Detection with Denoising Diffusioncited by 4
  4. 2025Robust detection of overlapping bioacoustic sound eventscited by 4
  5. 2022A Hybrid System of Sound Event Detection Transformer and Frame-wise Model for DCASE 2022 Task 4cited by 0
  6. 2026Towards Open World Sound Event Detectioncited by 0
  7. 2026A Neuromorphic Trigger for Efficient Audio Event Detectioncited by 0