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Robust Audio Content Classification Using Hybrid-Based SMD and Entropy-Based VAD.

Kun-Ching Wang1

  • 1Department of Information Technology & Communication, Shih Chien University, No. 200, University Rd, Neimen Shiang, Kaohsiung 845, Taiwan.

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|December 8, 2020
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Summary

This study introduces a hierarchical audio content classification (ACC) system using voice activity detection (VAD) and hybrid features for robust performance in noisy environments. The approach effectively distinguishes speech, music, and noise, even with varying noise levels.

Keywords:
audio content classificationspectral entropyspeech/music discriminationsupport vector machinevoice activity detectionwavelet packet

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Acoustics

Background:

  • Audio signals often contain a mixture of speech, music, and background noise.
  • Variable noise levels pose a significant challenge for accurate audio content classification (ACC).

Purpose of the Study:

  • To propose a robust hierarchical audio content classification (ACC) system.
  • To enhance ACC performance in environments with variable noise levels.

Main Methods:

  • A three-part hierarchical approach: entropy-based voice activity detection (VAD), hybrid feature extraction (1D-SEI and 2D-TII), and support vector machine (SVM) classification.
  • Post-processing rules for segment refinement.

Main Results:

  • The proposed system successfully classifies noisy audio into noise, speech, and music.
  • Achieved comparable performance to existing methods across three datasets, even in variable noise conditions.
  • Demonstrated improved audio content discrimination through the VAD scheme and hybrid features.

Conclusions:

  • The hierarchical ACC system with entropy-based VAD and hybrid features offers a robust solution for audio classification.
  • The proposed architecture effectively handles variable noise levels, enhancing classification accuracy.