# Improving Polyphonic Sound Event Detection on Multichannel Recordings with the Sorensen-Dice Coefficient Loss and Transfer Learning

arXiv: https://arxiv.org/abs/2107.10471 · HTML: https://mlsanity.com/paper/2107.10471/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 34 references that are in the map.
- First posted on arXiv 22 Jul 2021.

## Builds on (most cited first, 21 of 21)

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- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [Dice Loss for Data-imbalanced NLP Tasks](https://mlsanity.com/paper/1911.02855/) (2019)
- [Sound Event Detection: A Tutorial](https://mlsanity.com/paper/2107.05463/) (2021)
- [Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection](https://mlsanity.com/paper/1702.06286/) (2017)
- [A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection](https://mlsanity.com/paper/2006.01919/) (2020)
- [Densely connected multidilated convolutional networks for dense prediction tasks](https://mlsanity.com/paper/2011.11844/) (2020)
- [A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection](https://mlsanity.com/paper/2106.06999/) (2021)
- [Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions](https://mlsanity.com/paper/2106.04492/) (2021)
- [Sound Event Detection Using Spatial Features and Convolutional Recurrent Neural Network](https://mlsanity.com/paper/1706.02291/) (2017)
- [First Order Ambisonics Domain Spatial Augmentation for DNN-based Direction of Arrival Estimation](https://mlsanity.com/paper/1910.04388/) (2019)
- [Impact of Sound Duration and Inactive Frames on Sound Event Detection Performance](https://mlsanity.com/paper/2102.01927/) (2021)

## Abstract (arXiv)

The Sorensen--Dice Coefficient has recently seen rising popularity as a loss function (also known as Dice loss) due to its robustness in tasks where the number of negative samples significantly exceeds that of positive samples, such as semantic segmentation, natural language processing, and sound event detection. Conventional training of polyphonic sound event detection systems with binary cross-entropy loss often results in suboptimal detection performance as the training is often overwhelmed by updates from negative samples. In this paper, we investigated the effect of the Dice loss, intra- and inter-modal transfer learning, data augmentation, and recording formats, on the performance of polyphonic sound event detection systems with multichannel inputs. Our analysis showed that polyphonic sound event detection systems trained with Dice loss consistently outperformed those trained with cross-entropy loss across different training settings and recording formats in terms of F1 score and error rate. We achieved further performance gains via the use of transfer learning and an appropriate combination of different data augmentation techniques.