ArticleSensors (Basel, Switzerland)2024
ESC-NAS: Environment Sound Classification Using Hardware-Aware Neural Architecture Search for the Edge.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Structure-aware acoustic scene classification: a feature decoupling framework using HPSS and asymmetric convolutions.Scientific reports · 2026Article
- Context-Aware Multi-Agent Architecture for Wildfire Insights.Sensors (Basel, Switzerland) · 2026Article
- Advances in Audio Classification and Artificial Intelligence for Respiratory Health and Welfare Monitoring in Swine.Biology · 2026Review
- A lightweight CNN-transformer hybrid architecture with channel attention for real-time hazardous acoustic event detection.Frontiers in artificial intelligence · 2026Article
- Intelligent Classification of Urban Noise Sources Using TinyML: Towards Efficient Noise Management in Smart Cities.Sensors (Basel, Switzerland) · 2025Article
- Real-Time Acoustic Scene Recognition for Elderly Daily Routines Using Edge-Based Deep Learning.Sensors (Basel, Switzerland) · 2025Article
- Elephant Sound Classification Using Deep Learning Optimization.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
5 authors.
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Abstract
The combination of deep-learning and IoT plays a significant role in modern smart solutions, providing the capability of handling task-specific real-time offline operations with improved accuracy and minimised resource consumption. This study provides a novel hardware-aware neural architecture search approach called ESC-NAS, to design and develop deep convolutional neural network architectures specifically tailored for handling raw audio inputs in environmental sound classification applications under limited computational resources. The ESC-NAS process consists of a novel cell-based neural architecture search space built with 2D convolution, batch normalization, and max pooling layers, and capable of extracting features from raw audio. A black-box Bayesian optimization search strategy explores the search space and the resulting model architectures are evaluated through hardware simulation. The models obtained from the ESC-NAS process achieved the optimal trade-off between model performance and resource consumption compared to the existing literature. The ESC-NAS models achieved accuracies of 85.78%, 81.25%, 96.25%, and 81.0% for the FSC22, UrbanSound8K, ESC-10, and ESC-50 datasets, respectively, with optimal model sizes and parameter counts for edge deployment.
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Registered trials
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