# Exploring Performance-Complexity Trade-Offs in Sound Event Detection Models

arXiv: https://arxiv.org/abs/2503.11373 · HTML: https://mlsanity.com/paper/2503.11373/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 38 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [BEATs: Audio Pre-Training with Acoustic Tokenizers](https://mlsanity.com/paper/2212.09058/) (2022)
- [Hymba: A Hybrid-head Architecture for Small Language Models](https://mlsanity.com/paper/2411.13676/) (2024)
- [The Benefit Of Temporally-Strong Labels In Audio Event Classification](https://mlsanity.com/paper/2105.07031/) (2021)
- [PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and Aggregation](https://mlsanity.com/paper/2102.01243/) (2021)
- [Broadcasted Residual Learning for Efficient Keyword Spotting](https://mlsanity.com/paper/2106.04140/) (2021)
- [Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection](https://mlsanity.com/paper/1702.06286/) (2017)
- [A Comparison of Five Multiple Instance Learning Pooling Functions for Sound Event Detection with Weak Labeling](https://mlsanity.com/paper/1810.09050/) (2018)
- [Were RNNs All We Needed?](https://mlsanity.com/paper/2410.01201/) (2024)
- [ERANNs: Efficient Residual Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/2106.01621/) (2021)
- [Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation](https://mlsanity.com/paper/2211.04772/) (2022)
- [Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks](https://mlsanity.com/paper/2306.04186/) (2023)
- [Large-scale weakly supervised audio classification using gated convolutional neural network](https://mlsanity.com/paper/1710.00343/) (2017)
- [Fine-tune the pretrained ATST model for sound event detection](https://mlsanity.com/paper/2309.08153/) (2023)
- [Effective Pre-Training of Audio Transformers for Sound Event Detection](https://mlsanity.com/paper/2409.09546/) (2024)
- [Threshold Independent Evaluation of Sound Event Detection Scores](https://mlsanity.com/paper/2201.13148/) (2022)
- [Sound event detection via dilated convolutional recurrent neural networks](https://mlsanity.com/paper/1911.10888/) (2019)
- [MAT-SED: A Masked Audio Transformer with Masked-Reconstruction Based Pre-training for Sound Event Detection](https://mlsanity.com/paper/2408.08673/) (2024)
- [Sound Event Detection with Depthwise Separable and Dilated Convolutions](https://mlsanity.com/paper/2002.00476/) (2020)
- [Efficient Parallelization of a Ubiquitous Sequential Computation](https://mlsanity.com/paper/2311.06281/) (2023)
- [TF-SepNet: An Efficient 1D Kernel Design in CNNs for Low-Complexity Acoustic Scene Classification](https://mlsanity.com/paper/2309.08200/) (2023)

## Abstract (arXiv)

We target the problem of developing new low-complexity networks for the sound event detection task. Our goal is to meticulously analyze the performance-complexity trade-off, aiming to be competitive with the large state-of-the-art models, at a fraction of the computational requirements. We find that low-complexity convolutional models previously proposed for audio tagging can be effectively adapted for event detection (which requires frame-wise prediction) by adjusting convolutional strides, removing the global pooling, and, importantly, adding a sequence model before the (now frame-wise) classification heads. Systematic experiments reveal that the best choice for the sequence model type depends on which complexity metric is most important for the given application. We also investigate the impact of enhanced training strategies such as knowledge distillation. In the end, we show that combined with an optimized training strategy, we can reach event detection performance comparable to state-of-the-art transformers while requiring only around 5% of the parameters. We release all our pre-trained models and the code for reproducing this work to support future research in low-complexity sound event detection at https://github.com/theMoro/EfficientSED.