# Feature Aggregation in Joint Sound Classification and Localization Neural Networks

arXiv: https://arxiv.org/abs/2310.19063 · HTML: https://mlsanity.com/paper/2310.19063/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 42 of its 101 references that are in the map.
- First posted on arXiv 29 Oct 2023.

## Builds on (most cited first, 30 of 42)

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- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
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- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
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- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection](https://mlsanity.com/paper/1904.07392/) (2019)
- [Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation](https://mlsanity.com/paper/1901.02985/) (2019)
- [nnU-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2011.00848/) (2020)
- [Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/1807.00129/) (2018)
- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019](https://mlsanity.com/paper/2009.02792/) (2020)
- [Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network](https://mlsanity.com/paper/1710.10059/) (2017)
- [ACCDOA: Activity-Coupled Cartesian Direction of Arrival Representation for Sound Event Localization and Detection](https://mlsanity.com/paper/2010.15306/) (2020)
- [An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2010.13092/) (2020)
- [The Devil is in the Decoder: Classification, Regression and GANs](https://mlsanity.com/paper/1707.05847/) (2017)
- [A Four-Stage Data Augmentation Approach to ResNet-Conformer Based Acoustic Modeling for Sound Event Localization and Detection](https://mlsanity.com/paper/2101.02919/) (2021)
- [Polyphonic Sound Event Detection and Localization using a Two-Stage Strategy](https://mlsanity.com/paper/1905.00268/) (2019)
- [Broadband DOA estimation using Convolutional neural networks trained with noise signals](https://mlsanity.com/paper/1705.00919/) (2017)
- [SALSA: Spatial Cue-Augmented Log-Spectrogram Features for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2110.00275/) (2021)

## Abstract (arXiv)

This study addresses the application of deep learning techniques in joint sound signal classification and localization networks. Current state-of-the-art sound source localization deep learning networks lack feature aggregation within their architecture. Feature aggregation enhances model performance by enabling the consolidation of information from different feature scales, thereby improving feature robustness and invariance. This is particularly important in SSL networks, which must differentiate direct and indirect acoustic signals. To address this gap, we adapt feature aggregation techniques from computer vision neural networks to signal detection neural networks. Additionally, we propose the Scale Encoding Network (SEN) for feature aggregation to encode features from various scales, compressing the network for more computationally efficient aggregation. To evaluate the efficacy of feature aggregation in SSL networks, we integrated the following computer vision feature aggregation sub-architectures into a SSL control architecture: Path Aggregation Network (PANet), Weighted Bi-directional Feature Pyramid Network (BiFPN), and SEN. These sub-architectures were evaluated using two metrics for signal classification and two metrics for direction-of-arrival regression. PANet and BiFPN are established aggregators in computer vision models, while the proposed SEN is a more compact aggregator. The results suggest that models incorporating feature aggregations outperformed the control model, the Sound Event Localization and Detection network (SELDnet), in both sound signal classification and localization. The feature aggregation techniques enhance the performance of sound detection neural networks, particularly in direction-of-arrival regression.