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.
Abstract (translated)
环境声音分类在城市监测和文化声景分析领域中日益重要，尤其是在南亚这些声学丰富的环境中。这些地区面临的独特挑战是多种自然、人类和文化声音往往相互重叠，这对传统方法构成了考验，因为它们通常依赖于梅尔频率倒谱系数（MFCC）。本研究引入了一种基于频谱图的新颖方法，该方法具有捕捉复杂听觉模式的优越能力。通过在SAS-KIIT数据集上解决一个严格的多标签、多类分类问题，实施了卷积神经网络（CNN）架构。为了证明其稳健性和可比性，该方法还使用著名的UrbanSound8K数据集进行了验证。结果确认提出的基于频谱图的方法显著优于现有的MFCC基技术，在两个数据集中均实现了更高的分类精度。这一改进为实际应用中的更加强大和准确的声音分类系统奠定了基础。
URL
https://arxiv.org/abs/2603.08154
