Enhanced Multiple Sound Event Detection and Classification Using Physical Signal Properties in Recurrent Spiking Neural Networks
Sound event detection and classification present significant challenges, particularly in noisy environments with multiple overlapping sources. This paper introduces an innovative architecture for multiple sound event detection and classification utilizing recurrent spiking neural networks (SNNs). Ou...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10973233/ |
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| author | Zahra Roozbehi Ajit Narayanan Mahsa Mohaghegh Samaneh-Alsadat Saeedinia |
| author_facet | Zahra Roozbehi Ajit Narayanan Mahsa Mohaghegh Samaneh-Alsadat Saeedinia |
| author_sort | Zahra Roozbehi |
| collection | DOAJ |
| description | Sound event detection and classification present significant challenges, particularly in noisy environments with multiple overlapping sources. This paper introduces an innovative architecture for multiple sound event detection and classification utilizing recurrent spiking neural networks (SNNs). Our method uniquely leverages temporal data to detect and classify multiple sound sources simultaneously, integrating the physical concept of signal power matching with neuronal output power and employing a binaural strategy to enhance detection accuracy in real-world scenarios. The architecture processes spatiotemporal data to dynamically update synaptic weights, enabling precise identification of sound event categories and their occurrences. Our simulations reveal substantial performance improvements, achieving the highest precision of 73% in classification tasks, including multilayer perceptrons (MLP), convolutional recurrent neural networks (CRNN), and recurrent neural networks (RNN). Statistical analysis indicates that these improvements are significant (p-value ¡ 0.05). These findings suggest practical applications in various fields such as surveillance, autonomous vehicles, and smart home systems, where robust sound event detection is critical. |
| format | Article |
| id | doaj-art-b13aae6628ec411ca9d70efcfdbc0ad5 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b13aae6628ec411ca9d70efcfdbc0ad52025-08-20T01:49:15ZengIEEEIEEE Access2169-35362025-01-0113813128132510.1109/ACCESS.2025.356334610973233Enhanced Multiple Sound Event Detection and Classification Using Physical Signal Properties in Recurrent Spiking Neural NetworksZahra Roozbehi0https://orcid.org/0000-0003-1440-7015Ajit Narayanan1https://orcid.org/0000-0003-3075-7672Mahsa Mohaghegh2https://orcid.org/0000-0003-2228-8300Samaneh-Alsadat Saeedinia3https://orcid.org/0009-0000-3771-8073School of Electrical and Computer Engineering, Auckland University of Technology, Auckland, New ZealandSchool of Electrical and Computer Engineering, Auckland University of Technology, Auckland, New ZealandSchool of Electrical and Computer Engineering, Auckland University of Technology, Auckland, New ZealandDepartment of Electrical and Computer Engineering, Iran University of Science and Technology, Tehran, IranSound event detection and classification present significant challenges, particularly in noisy environments with multiple overlapping sources. This paper introduces an innovative architecture for multiple sound event detection and classification utilizing recurrent spiking neural networks (SNNs). Our method uniquely leverages temporal data to detect and classify multiple sound sources simultaneously, integrating the physical concept of signal power matching with neuronal output power and employing a binaural strategy to enhance detection accuracy in real-world scenarios. The architecture processes spatiotemporal data to dynamically update synaptic weights, enabling precise identification of sound event categories and their occurrences. Our simulations reveal substantial performance improvements, achieving the highest precision of 73% in classification tasks, including multilayer perceptrons (MLP), convolutional recurrent neural networks (CRNN), and recurrent neural networks (RNN). Statistical analysis indicates that these improvements are significant (p-value ¡ 0.05). These findings suggest practical applications in various fields such as surveillance, autonomous vehicles, and smart home systems, where robust sound event detection is critical.https://ieeexplore.ieee.org/document/10973233/Sound classificationspiking neural networkTempotronaudio signal powerdetectionmultiple sound sources |
| spellingShingle | Zahra Roozbehi Ajit Narayanan Mahsa Mohaghegh Samaneh-Alsadat Saeedinia Enhanced Multiple Sound Event Detection and Classification Using Physical Signal Properties in Recurrent Spiking Neural Networks IEEE Access Sound classification spiking neural network Tempotron audio signal power detection multiple sound sources |
| title | Enhanced Multiple Sound Event Detection and Classification Using Physical Signal Properties in Recurrent Spiking Neural Networks |
| title_full | Enhanced Multiple Sound Event Detection and Classification Using Physical Signal Properties in Recurrent Spiking Neural Networks |
| title_fullStr | Enhanced Multiple Sound Event Detection and Classification Using Physical Signal Properties in Recurrent Spiking Neural Networks |
| title_full_unstemmed | Enhanced Multiple Sound Event Detection and Classification Using Physical Signal Properties in Recurrent Spiking Neural Networks |
| title_short | Enhanced Multiple Sound Event Detection and Classification Using Physical Signal Properties in Recurrent Spiking Neural Networks |
| title_sort | enhanced multiple sound event detection and classification using physical signal properties in recurrent spiking neural networks |
| topic | Sound classification spiking neural network Tempotron audio signal power detection multiple sound sources |
| url | https://ieeexplore.ieee.org/document/10973233/ |
| work_keys_str_mv | AT zahraroozbehi enhancedmultiplesoundeventdetectionandclassificationusingphysicalsignalpropertiesinrecurrentspikingneuralnetworks AT ajitnarayanan enhancedmultiplesoundeventdetectionandclassificationusingphysicalsignalpropertiesinrecurrentspikingneuralnetworks AT mahsamohaghegh enhancedmultiplesoundeventdetectionandclassificationusingphysicalsignalpropertiesinrecurrentspikingneuralnetworks AT samanehalsadatsaeedinia enhancedmultiplesoundeventdetectionandclassificationusingphysicalsignalpropertiesinrecurrentspikingneuralnetworks |