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Music Recognition Algorithm based on T-S Cognitive Neural Network.

Fei Yan1

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Summary
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This study introduces an improved cognitive neural network for music recognition, enhancing accuracy and robustness. The novel algorithm effectively handles high-dimensional input data, outperforming existing methods in music content analysis.

Keywords:
T-Scognitive neural networkmusic recognition

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Area of Science:

  • Artificial Intelligence
  • Signal Processing
  • Music Information Retrieval

Background:

  • Music recognition relies on audio signal processing and feature extraction for content analysis.
  • Cognitive neural networks offer a promising approach for complex pattern recognition tasks in music.

Purpose of the Study:

  • To develop an improved cognitive neural network model for accurate and robust music recognition.
  • To address challenges associated with high-dimensional input data in music recognition systems.

Main Methods:

  • Utilized a T-S model cognitive neural network trained with an improved genetic algorithm.
  • Implemented a membership function parameter adjustment strategy combined with momentum and adaptive learning rate adjustments.
  • Introduced a compensation factor for membership degree to manage input dimension effects.

Main Results:

  • The proposed algorithm demonstrates successful application in music recognition systems, mitigating issues from excessive input dimensions.
  • Achieved higher accuracy rates compared to other existing music recognition algorithms.
  • Exhibited superior robustness in experimental evaluations.

Conclusions:

  • The novel cognitive neural network approach enhances music recognition accuracy and robustness.
  • The method effectively handles high-dimensional data, making it suitable for practical music recognition applications.