# Cross-Referencing Self-Training Network for Sound Event Detection in Audio Mixtures

arXiv: https://arxiv.org/abs/2105.13392 · HTML: https://mlsanity.com/paper/2105.13392/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 55 references that are in the map.
- First posted on arXiv 27 May 2021.

## Builds on (most cited first, 17 of 17)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning](https://mlsanity.com/paper/1606.04586/) (2016)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Interpolation Consistency Training for Semi-Supervised Learning](https://mlsanity.com/paper/1903.03825/) (2019)
- [Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data](https://mlsanity.com/paper/2010.03622/) (2020)
- [Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection](https://mlsanity.com/paper/1702.06286/) (2017)
- [Sound Event Detection of Weakly Labelled Data with CNN-Transformer and Automatic Threshold Optimization](https://mlsanity.com/paper/1912.04761/) (2019)
- [Training Sound Event Detection On A Heterogeneous Dataset](https://mlsanity.com/paper/2007.03931/) (2020)
- [Guided learning for weakly-labeled semi-supervised sound event detection](https://mlsanity.com/paper/1906.02517/) (2019)
- [Polyphonic Sound Event Detection by using Capsule Neural Networks](https://mlsanity.com/paper/1810.06325/) (2018)
- [Semi-supervised Acoustic Event Detection based on tri-training](https://mlsanity.com/paper/1904.12926/) (2019)
- [Joint Acoustic and Class Inference for Weakly Supervised Sound Event Detection](https://mlsanity.com/paper/1811.04048/) (2018)
- [Guided multi-branch learning systems for sound event detection with sound separation](https://mlsanity.com/paper/2007.10638/) (2020)
- [Polyphonic sound event detection based on convolutional recurrent neural networks with semi-supervised loss function for DCASE challenge 2020 task 4](https://mlsanity.com/paper/2007.00947/) (2020)

## Built on by (most cited first, 2 of 2)

- [Locality-aware Cross-modal Correspondence Learning for Dense Audio-Visual Events Localization](https://mlsanity.com/paper/2409.07967/) (2024)
- [MAST: Label-Efficient, Robust, and Generalizable Sound Detection for Biodiversity Monitoring via Masked Audio Pretraining and Self-Training](https://mlsanity.com/paper/2609.15221/) (2026)

## Abstract (arXiv)

Sound event detection is an important facet of audio tagging that aims to identify sounds of interest and define both the sound category and time boundaries for each sound event in a continuous recording. With advances in deep neural networks, there has been tremendous improvement in the performance of sound event detection systems, although at the expense of costly data collection and labeling efforts. In fact, current state-of-the-art methods employ supervised training methods that leverage large amounts of data samples and corresponding labels in order to facilitate identification of sound category and time stamps of events. As an alternative, the current study proposes a semi-supervised method for generating pseudo-labels from unsupervised data using a student-teacher scheme that balances self-training and cross-training. Additionally, this paper explores post-processing which extracts sound intervals from network prediction, for further improvement in sound event detection performance. The proposed approach is evaluated on sound event detection task for the DCASE2020 challenge. The results of these methods on both "validation" and "public evaluation" sets of DESED database show significant improvement compared to the state-of-the art systems in semi-supervised learning.