Core Concepts
This research introduces PSELDNets, pre-trained neural networks for sound event localization and detection (SELD) trained on large-scale synthetic datasets, demonstrating state-of-the-art performance and efficient adaptability to various SELD tasks, even with limited data, using a novel data-efficient fine-tuning method called AdapterBit.
Hu, J., Cao, Y., Wu, M., Kang, F., Yang, F., Wang, W., Plumbley, M. D., & Yang, J. (2024). PSELDNets: Pre-trained Neural Networks on Large-scale Synthetic Datasets for Sound Event Localization and Detection. arXiv preprint arXiv:2411.06399.
This paper investigates the development of a general-purpose SELD model applicable to diverse real-world scenarios by leveraging the power of pre-trained sound event classification (SEC) models.