Audio classification based on audio WSOLA and CNN algorithm

Pengfei Li; Tiecheng Song; Jing Hu

doi:10.1117/12.2685823

15 August 2023 Audio classification based on audio WSOLA and CNN algorithm

Pengfei Li, Tiecheng Song, Jing Hu

Proceedings Volume 12719, Second International Conference on Electronic Information Technology (EIT 2023); 127194M (2023) https://doi.org/10.1117/12.2685823
Event: Second International Conference on Electronic Information Technology (EIT 2023), 2023, Wuhan, China

Abstract

This paper presents an audio classification algorithm based on the WSOLA and CNN techniques to address the problem of data imbalance in audio classification. Audio classification involves categorizing audio signals into different labels or categories, and is crucial in speech recognition, music classification, sound time detection, and sound quality evaluation. In this study, we propose a method that utilizes the WSOLA algorithm to enhance the audio data with fewer categories in the dataset, which can help to improve the accuracy and stability of classification when the dataset is unbalanced. This approach can also prevent the model from focusing too much on categories with large data volumes while neglecting other categories. By mitigating the issue of audio data imbalance, the model can better learn the characteristics of all categories, thereby enhancing the model's performance. We conducted experiments on the UrbanSound8K dataset, where we enhanced the audio data using the WSOLA method and then classified the audio using the CNN classifier. Our results indicate that the overall classification accuracy and stability were significantly improved, demonstrating that the proposed approach can reasonably classify the audio dataset using the WSOLA and CNN techniques.

Citation Download Citation

Pengfei Li, Tiecheng Song, and Jing Hu "Audio classification based on audio WSOLA and CNN algorithm", Proc. SPIE 12719, Second International Conference on Electronic Information Technology (EIT 2023), 127194M (15 August 2023); https://doi.org/10.1117/12.2685823

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KEYWORDS

Image segmentation

Data modeling

Detection and tracking algorithms

Feature extraction

Education and training

Windows

Data processing

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