Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Soundscapes in Spectrograms: Pioneering Multilabel Classification for South Asian Sounds

Sudip Chakrabarty, Pappu Bishwas, Rajdeep Chatterjee, Tathagata Bandyopadhyay, Digonto Biswas, Bibek Howlader

TL;DR

A novel spectrogram-based methodology with a superior ability to capture these complex auditory patterns is introduced, and a Convolutional Neural Network architecture is implemented to solve a demanding multilabel, multiclass classification problem on the SAS-KIIT 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.

Soundscapes in Spectrograms: Pioneering Multilabel Classification for South Asian Sounds

TL;DR

A novel spectrogram-based methodology with a superior ability to capture these complex auditory patterns is introduced, and a Convolutional Neural Network architecture is implemented to solve a demanding multilabel, multiclass classification problem on the SAS-KIIT 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.
Paper Structure (21 sections, 5 equations, 5 figures, 5 tables)

This paper contains 21 sections, 5 equations, 5 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Literature Survey and Related Works
  3. Datasets and Preprocessing
  4. SAS-KIIT Dataset
  5. Dataset Overview
  6. Organization and Characteristics
  7. UrbanSound8K Dataset
  8. Dataset Overview
  9. Organization and Characteristics
  10. Audio Mixing Process
  11. Proposed Methodology
  12. Mel Spectrogram Generation
  13. Computation of MFCC Features
  14. Input Preparation
  15. Model Architecture
  16. ...and 6 more sections

Figures (5)

  • Figure 1: t-SNE: A Representation of Class Distributions in the Feature Space for SAS-KIIT Dataset.
  • Figure 2: t-SNE: A Representation of Class Distributions in the Feature Space for UrbanSound8K Dataset.
  • Figure 3: Audio Mixing Process and Label Interactions.
  • Figure 4: Mixed audio is converted to Mel-spectrograms and fed into a CNN with ReLU and max pooling to extract features; dense layers output multilabel predictions, with dusty pink highlighting detected sound classes in an example recording.
  • Figure 5: Log-Scale Representation of Predicted Audio Classes: Orange bars indicate classes detected in the sample audio; Sky-blue bars show other predicted classes with lower probability scores in log scale; The dashed black line (- - -) represents the decision threshold, above which classes are considered present.