ArticleSensors (Basel, Switzerland)2023
A CNN Sound Classification Mechanism Using Data Augmentation.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Structure-aware acoustic scene classification: a feature decoupling framework using HPSS and asymmetric convolutions.Scientific reports · 2026Article
- Advances in Audio Classification and Artificial Intelligence for Respiratory Health and Welfare Monitoring in Swine.Biology · 2026Review
- Enhanced Respiratory Sound Classification Using Deep Learning and Multi-Channel Auscultation.Journal of clinical medicine · 2025Article
- Leveraging large language models for automated detection of velopharyngeal dysfunction in patients with cleft palate.Frontiers in digital health · 2025Article
- A Comparative Study of Preprocessing and Model Compression Techniques in Deep Learning for Forest Sound Classification.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
3 authors.
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Abstract
Sound classification has been widely used in many fields. Unlike traditional signal-processing methods, using deep learning technology for sound classification is one of the most feasible and effective methods. However, limited by the quality of the training dataset, such as cost and resource constraints, data imbalance, and data annotation issues, the classification performance is affected. Therefore, we propose a sound classification mechanism based on convolutional neural networks and use the sound feature extraction method of Mel-Frequency Cepstral Coefficients (MFCCs) to convert sound signals into spectrograms. Spectrograms are suitable as input for CNN models. To provide the function of data augmentation, we can increase the number of spectrograms by setting the number of triangular bandpass filters. The experimental results show that there are 50 semantic categories in the ESC-50 dataset, the types are complex, and the amount of data is insufficient, resulting in a classification accuracy of only 63%. When using the proposed data augmentation method (
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