# MTDA-HSED: Mutual-Assistance Tuning and Dual-Branch Aggregating for Heterogeneous Sound Event Detection

arXiv: https://arxiv.org/abs/2409.06196 · HTML: https://mlsanity.com/paper/2409.06196/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition](https://mlsanity.com/paper/2205.13535/) (2022)
- [Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey](https://mlsanity.com/paper/2403.14608/) (2024)
- [BEATs: Audio Pre-Training with Acoustic Tokenizers](https://mlsanity.com/paper/2212.09058/) (2022)
- [Sound Event Detection: A Tutorial](https://mlsanity.com/paper/2107.05463/) (2021)
- [A Framework for the Robust Evaluation of Sound Event Detection](https://mlsanity.com/paper/1910.08440/) (2019)
- [Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution](https://mlsanity.com/paper/2203.07682/) (2022)
- [Adapter is All You Need for Tuning Visual Tasks](https://mlsanity.com/paper/2311.15010/) (2023)
- [Fine-tune the pretrained ATST model for sound event detection](https://mlsanity.com/paper/2309.08153/) (2023)
- [Towards duration robust weakly supervised sound event detection](https://mlsanity.com/paper/2101.07687/) (2021)
- [Threshold Independent Evaluation of Sound Event Detection Scores](https://mlsanity.com/paper/2201.13148/) (2022)
- [AST-SED: An Effective Sound Event Detection Method Based on Audio Spectrogram Transformer](https://mlsanity.com/paper/2303.03689/) (2023)
- [DCASE 2024 Task 4: Sound Event Detection with Heterogeneous Data and Missing Labels](https://mlsanity.com/paper/2406.08056/) (2024)

## Abstract (arXiv)

Sound Event Detection (SED) plays a vital role in comprehending and perceiving acoustic scenes. Previous methods have demonstrated impressive capabilities. However, they are deficient in learning features of complex scenes from heterogeneous dataset. In this paper, we introduce a novel dual-branch architecture named Mutual-Assistance Tuning and Dual-Branch Aggregating for Heterogeneous Sound Event Detection (MTDA-HSED). The MTDA-HSED architecture employs the Mutual-Assistance Audio Adapter (M3A) to effectively tackle the multi-scenario problem and uses the Dual-Branch Mid-Fusion (DBMF) module to tackle the multi-granularity problem. Specifically, M3A is integrated into the BEATs block as an adapter to improve the BEATs' performance by fine-tuning it on the multi-scenario dataset. The DBMF module connects BEATs and CNN branches, which facilitates the deep fusion of information from the BEATs and the CNN branches. Experimental results show that the proposed methods exceed the baseline of mpAUC by 5% on the DESED and MAESTRO Real datasets. Code is available at https://github.com/Visitor-W/MTDA.