Sound classification using sound spectrum features and convolutional neural networks
This paper proposes an alternative approach to sound classification using sound spectrum features, differing from the use of the Mel-Frequency Cepstral Coefficients (MFCC). Aligning with the crowd sourcing data collection application NoiseCapture, the data are kept in form of the post-processed soun...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/177694 |
| _version_ | 1826113731035136000 |
|---|---|
| author | Tan, Ki In Yean, Seanglidet Lee, Bu-Sung |
| author2 | College of Computing and Data Science |
| author_facet | College of Computing and Data Science Tan, Ki In Yean, Seanglidet Lee, Bu-Sung |
| author_sort | Tan, Ki In |
| collection | NTU |
| description | This paper proposes an alternative approach to sound classification using sound spectrum features, differing from the use of the Mel-Frequency Cepstral Coefficients (MFCC). Aligning with the crowd sourcing data collection application NoiseCapture, the data are kept in form of the post-processed sound spectrum instead of the raw audio files to maintain privacy of volunteers. Under such circumstances, MFCC, which requires audio processing, cannot be directly obtained from nor maximize the features of sound spectrum data stored in the application. As sound spectrum does not undergo further feature transformation, it retains audio features from the audio file and should therefore be classifiable when passed into a trained sound spectrum model. Hence, in this study, we aim to evaluate whether sound spectrum could be used as a replacement of MFCC, especially when audio file is inaccessible. The UrbanSound8K dataset and a mix of deep learning and machine learning models were used for the comparison. Experiment results show sound spectrum achieving comparable results in Convolutional Neural Network (CNN), with better predictions than its MFCC counterpart. Further comparisons draw insights that illustrate the need for more finetuning for sound spectrum data when using non-CNN models for sound classification due to the shape of the input features. |
| first_indexed | 2024-10-01T03:28:01Z |
| format | Conference Paper |
| id | ntu-10356/177694 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:28:01Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1776942024-05-29T02:44:33Z Sound classification using sound spectrum features and convolutional neural networks Tan, Ki In Yean, Seanglidet Lee, Bu-Sung College of Computing and Data Science School of Computer Science and Engineering 2022 3rd International Conference on Human-Centric Smart Environments for Health and Well-being (IHSH) Computer and Information Science Sound spectrum Sound classification This paper proposes an alternative approach to sound classification using sound spectrum features, differing from the use of the Mel-Frequency Cepstral Coefficients (MFCC). Aligning with the crowd sourcing data collection application NoiseCapture, the data are kept in form of the post-processed sound spectrum instead of the raw audio files to maintain privacy of volunteers. Under such circumstances, MFCC, which requires audio processing, cannot be directly obtained from nor maximize the features of sound spectrum data stored in the application. As sound spectrum does not undergo further feature transformation, it retains audio features from the audio file and should therefore be classifiable when passed into a trained sound spectrum model. Hence, in this study, we aim to evaluate whether sound spectrum could be used as a replacement of MFCC, especially when audio file is inaccessible. The UrbanSound8K dataset and a mix of deep learning and machine learning models were used for the comparison. Experiment results show sound spectrum achieving comparable results in Convolutional Neural Network (CNN), with better predictions than its MFCC counterpart. Further comparisons draw insights that illustrate the need for more finetuning for sound spectrum data when using non-CNN models for sound classification due to the shape of the input features. Nanyang Technological University National Research Foundation (NRF) Submitted/Accepted version We would like to acknowledge the funding support from Nanyang Technological University – URECA Undergraduate Research Programme for this research project. This research/project is supported by the Catalyst: Strategic Fund from Government Funding, administered by the Ministry of Business Innovation Employment, New Zealand under contract C09X1923, as well as the National Research Foundation, Singapore under its Industry Alignment Fund – Pre-positioning (IAF-PP) Funding Initiative. 2024-05-29T02:44:33Z 2024-05-29T02:44:33Z 2023 Conference Paper Tan, K. I., Yean, S. & Lee, B. (2023). Sound classification using sound spectrum features and convolutional neural networks. 2022 3rd International Conference on Human-Centric Smart Environments for Health and Well-being (IHSH), 94-99. https://dx.doi.org/10.1109/IHSH57076.2022.10092143 9781665463218 https://hdl.handle.net/10356/177694 10.1109/IHSH57076.2022.10092143 2-s2.0-85154566809 94 99 en © 2022 IEEE. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.1109/IHSH57076.2022.10092143. application/pdf |
| spellingShingle | Computer and Information Science Sound spectrum Sound classification Tan, Ki In Yean, Seanglidet Lee, Bu-Sung Sound classification using sound spectrum features and convolutional neural networks |
| title | Sound classification using sound spectrum features and convolutional neural networks |
| title_full | Sound classification using sound spectrum features and convolutional neural networks |
| title_fullStr | Sound classification using sound spectrum features and convolutional neural networks |
| title_full_unstemmed | Sound classification using sound spectrum features and convolutional neural networks |
| title_short | Sound classification using sound spectrum features and convolutional neural networks |
| title_sort | sound classification using sound spectrum features and convolutional neural networks |
| topic | Computer and Information Science Sound spectrum Sound classification |
| url | https://hdl.handle.net/10356/177694 |
| work_keys_str_mv | AT tankiin soundclassificationusingsoundspectrumfeaturesandconvolutionalneuralnetworks AT yeanseanglidet soundclassificationusingsoundspectrumfeaturesandconvolutionalneuralnetworks AT leebusung soundclassificationusingsoundspectrumfeaturesandconvolutionalneuralnetworks |