Soundscapes in Spectrograms: Pioneering Multilabel Classification for South Asian Sounds

TL;DR

This paper presents a novel spectrogram-based CNN approach for multilabel classification of South Asian environmental sounds, outperforming traditional MFCC methods and validated on multiple datasets.

Contribution

Introduces a spectrogram-based CNN method for complex multilabel sound classification in South Asia, surpassing MFCC-based techniques.

Findings

Higher classification accuracy on SAS-KIIT dataset

Outperforms MFCC-based methods

Validated on UrbanSound8K dataset

Abstract

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.