Soundscapes in Spectrograms: Pioneering Multilabel Classification for South Asian Sounds
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910046221238272 |
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| author | Chakrabarty, Sudip Bishwas, Pappu Chatterjee, Rajdeep Bandyopadhyay, Tathagata Biswas, Digonto Howlader, Bibek |
| author_facet | Chakrabarty, Sudip Bishwas, Pappu Chatterjee, Rajdeep Bandyopadhyay, Tathagata Biswas, Digonto Howlader, Bibek |
| contents | Environmental sound classification is a field of growing importance for urban monitoring and cultural soundscape analysis, especially within the acoustically rich environments of South Asia. These regions present a unique challenge as multiple natural, human, and cultural sounds often overlap, straining traditional methods that frequently rely on Mel Frequency Cepstral Coefficients (MFCC). This study introduces a novel spectrogram-based methodology with a superior ability to capture these complex auditory patterns. A Convolutional Neural Network (CNN) architecture is implemented to solve a demanding multilabel, multiclass classification problem on the SAS-KIIT dataset. To demonstrate robustness and comparability, the approach is also validated using the renowned UrbanSound8K dataset. The results confirm that the proposed spectrogram-based method significantly outperforms existing MFCC-based techniques, achieving higher classification accuracy across both datasets. This improvement lays the groundwork for more robust and accurate audio classification systems in real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08154 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Soundscapes in Spectrograms: Pioneering Multilabel Classification for South Asian Sounds Chakrabarty, Sudip Bishwas, Pappu Chatterjee, Rajdeep Bandyopadhyay, Tathagata Biswas, Digonto Howlader, Bibek Sound Multimedia Environmental sound classification is a field of growing importance for urban monitoring and cultural soundscape analysis, especially within the acoustically rich environments of South Asia. These regions present a unique challenge as multiple natural, human, and cultural sounds often overlap, straining traditional methods that frequently rely on Mel Frequency Cepstral Coefficients (MFCC). This study introduces a novel spectrogram-based methodology with a superior ability to capture these complex auditory patterns. A Convolutional Neural Network (CNN) architecture is implemented to solve a demanding multilabel, multiclass classification problem on the SAS-KIIT dataset. To demonstrate robustness and comparability, the approach is also validated using the renowned UrbanSound8K dataset. The results confirm that the proposed spectrogram-based method significantly outperforms existing MFCC-based techniques, achieving higher classification accuracy across both datasets. This improvement lays the groundwork for more robust and accurate audio classification systems in real-world applications. |
| title | Soundscapes in Spectrograms: Pioneering Multilabel Classification for South Asian Sounds |
| topic | Sound Multimedia |
| url | https://arxiv.org/abs/2603.08154 |