NeuProNet: neural profiling networks for sound classification
| dc.contributor.author | Tran, Khanh-Tung | en |
| dc.contributor.author | Vu, Xuan-Son | en |
| dc.contributor.author | Nguyen, Khuong | en |
| dc.contributor.author | Nguyen, Hoang D. | en |
| dc.date.accessioned | 2024-06-04T09:14:36Z | |
| dc.date.available | 2024-06-04T09:14:36Z | |
| dc.date.issued | 2024-01-16 | en |
| dc.description.abstract | Real-world sound signals exhibit various aspects of grouping and profiling behaviors, such as being recorded from identical sources, having similar environmental settings, or encountering related background noises. In this work, we propose novel neural profiling networks (NeuProNet) capable of learning and extracting high-level unique profile representations from sounds. An end-to-end framework is developed so that any backbone architectures can be plugged in and trained, achieving better performance in any downstream sound classification tasks. We introduce an in-batch profile grouping mechanism based on profile awareness and attention pooling to produce reliable and robust features with contrastive learning. Furthermore, extensive experiments are conducted on multiple benchmark datasets and tasks to show that neural computing models under the guidance of our framework gain significant performance gaps across all evaluation tasks. Particularly, the integration of NeuProNet surpasses recent state-of-the-art (SoTA) approaches on UrbanSound8K and VocalSound datasets with statistically significant improvements in benchmarking metrics, up to 5.92% in accuracy compared to the previous SoTA method and up to 20.19% compared to baselines. Our work provides a strong foundation for utilizing neural profiling for machine learning tasks. | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Published Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Tran, K. T., Vu, X. S., Nguyen, K. and Nguyen, H. D. (2024) 'NeuProNet: neural profiling networks for sound classification', Neural Computing and Applications, 36, pp. 5873-5887. https://doi.org/10.1007/s00521-023-09361-8 | en |
| dc.identifier.doi | https://doi.org/10.1007/s00521-023-09361-8 | en |
| dc.identifier.eissn | 1433-3058 | en |
| dc.identifier.endpage | 5887 | en |
| dc.identifier.issn | 0941-0643 | en |
| dc.identifier.journaltitle | Neural Computing and Applications | en |
| dc.identifier.startpage | 5873 | en |
| dc.identifier.uri | https://hdl.handle.net/10468/15968 | |
| dc.identifier.volume | 36 | en |
| dc.language.iso | en | en |
| dc.publisher | Springer Nature | en |
| dc.rights | © 2024, the Authors. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | Neural profiling network | en |
| dc.subject | Audio classification | en |
| dc.subject | Deep learning | en |
| dc.subject | Signal processing | en |
| dc.title | NeuProNet: neural profiling networks for sound classification | en |
| dc.type | Article (peer-reviewed) | en |
| oaire.citation.issue | 11 | en |
| oaire.citation.volume | 36 | en |
