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Content-Based Audio Classification and Retrieving Using Modified Bacterial Foraging Optimization Algorithm.

Amani K Samha1, Ghalib H Alshammri2, Stephen Jeswinde Nuagah3

  • 1Management Information System Department, College of Business Administration, King Saud University, Riyadh 28095, Saudi Arabia.

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Summary
This summary is machine-generated.

A new modified bacterial foraging optimization algorithm (MBFOA) improves audio classification and retrieval. This method enhances accuracy, sensitivity, and specificity for various applications, reducing computational complexity.

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Area of Science:

  • Computer Science
  • Signal Processing
  • Artificial Intelligence

Background:

  • Audio classification and retrieval are crucial for multimedia and diverse fields like medicine and surveillance.
  • Existing methods face challenges in computational complexity and feature selection.
  • Identifying optimal audio attributes is key for effective categorization.

Purpose of the Study:

  • To introduce a novel algorithm, the modified bacterial foraging optimization algorithm (MBFOA), for audio data retrieval and classification.
  • To reduce the computational complexity associated with current audio processing techniques.
  • To enhance the accuracy and efficiency of audio signal analysis.

Main Methods:

  • The study utilizes enhanced mel-frequency cepstral coefficients (EMFCC) and enhanced power normalized cepstral coefficients (EPNCC) combined with peak estimated signal.
  • The modified bacterial foraging optimization algorithm (MBFOA) is employed to optimize feature selection via a fitness function.
  • A probabilistic neural network (PNN) is used for differentiating between music and speech signals.

Main Results:

  • The MBFOA algorithm demonstrated superior performance compared to similar existing approaches.
  • The proposed method achieved higher levels of sensitivity, specificity, and overall accuracy in audio classification.
  • Feature extraction and characteristic listing were effectively performed post-classification.

Conclusions:

  • The MBFOA presents a significant advancement in audio classification and retrieval systems.
  • The algorithm offers improved computational efficiency and robust performance.
  • This approach holds potential for widespread application in multimedia and specialized domains requiring precise audio analysis.