# A Multi-Task Learning Framework for Sound Event Detection using High-level Acoustic Characteristics of Sounds

arXiv: https://arxiv.org/abs/2305.10729 · HTML: https://mlsanity.com/paper/2305.10729/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2023.

## Built on by (most cited first, 5 of 5)

- [Dual Knowledge Distillation for Efficient Sound Event Detection](https://mlsanity.com/paper/2402.02781/) (2024)
- [Towards Multilingual Audio-Visual Question Answering](https://mlsanity.com/paper/2406.09156/) (2024)
- [A Multi-task Learning Balanced Attention Convolutional Neural Network Model for Few-shot Underwater Acoustic Target Recognition](https://mlsanity.com/paper/2504.13102/) (2025)
- [More Than A Shortcut: A Hyperbolic Approach To Early-Exit Networks](https://mlsanity.com/paper/2511.00641/) (2025)
- [TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios](https://mlsanity.com/paper/2607.06179/) (2026)

## Abstract (arXiv)

Sound event detection (SED) entails identifying the type of sound and estimating its temporal boundaries from acoustic signals. These events are uniquely characterized by their spatio-temporal features, which are determined by the way they are produced. In this study, we leverage some distinctive high-level acoustic characteristics of various sound events to assist the SED model training, without requiring additional labeled data. Specifically, we use the DCASE Task 4 2022 dataset and categorize the 10 classes into four subcategories based on their high-level acoustic characteristics. We then introduce a novel multi-task learning framework that jointly trains the SED and high-level acoustic characteristics classification tasks, using shared layers and weighted loss. Our method significantly improves the performance of the SED system, achieving a 36.3% improvement in terms of the polyphonic sound event detection score compared to the baseline on the DCASE 2022 Task 4 validation set.