ArticleSensors (Basel, Switzerland)2024
A Comparative Study of Preprocessing and Model Compression Techniques in Deep Learning for Forest Sound Classification.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- Context-Aware Multi-Agent Architecture for Wildfire Insights.Sensors (Basel, Switzerland) · 2026Article
- Lightweight faster R-CNN for object detection in optical remote sensing images.Scientific reports · 2025Article
- Empowering voice assistants with TinyML for user-centric innovations and real-world applications.Scientific reports · 2025Review
- IESSP: Information Extraction-Based Sparse Stripe Pruning Method for Deep Neural Networks.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
- PoachNet: Predicting Poaching Using an Ontology-Based Knowledge Graph.Sensors (Basel, Switzerland) · 2024Article
- ESC-NAS: Environment Sound Classification Using Hardware-Aware Neural Architecture Search for the Edge.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep-learning models play a significant role in modern software solutions, with the capabilities of handling complex tasks, improving accuracy, automating processes, and adapting to diverse domains, eventually contributing to advancements in various industries. This study provides a comparative study on deep-learning techniques that can also be deployed on resource-constrained edge devices. As a novel contribution, we analyze the performance of seven Convolutional Neural Network models in the context of data augmentation, feature extraction, and model compression using acoustic data. The results show that the best performers can achieve an optimal trade-off between model accuracy and size when compressed with weight and filter pruning followed by 8-bit quantization. In adherence to the study workflow utilizing the forest sound dataset, MobileNet-v3-small and ACDNet achieved accuracies of 87.95% and 85.64%, respectively, while maintaining compact sizes of 243 KB and 484 KB, respectively. Henceforth, this study concludes that CNNs can be optimized and compressed to be deployed in resource-constrained edge devices for classifying forest environment sounds.
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Registered trials
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