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Music Similarity Detection Guided by Deep Learning Model.

Xiuli Wang1

  • 1Moscow Academy of Art, Weinan Teachers College, Weinan 714000, Shaanxi, China.

Computational Intelligence and Neuroscience
|March 2, 2023
PubMed
Summary

This study introduces a novel music similarity detection (MSD) algorithm using deep learning and convolutional neural networks (CNNs). The method enhances music feature extraction, achieving a 75.6% detection rate on the GTZAN dataset.

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Area of Science:

  • Computer Science
  • Digital Signal Processing
  • Machine Learning

Background:

  • Digital music and network technologies have increased interest in music similarity detection (MSD) for music style classification.
  • Current MSD methods rely on feature extraction, training, and detection, with deep learning (DL) showing promise for improved feature extraction efficiency.

Purpose of the Study:

  • To develop and evaluate a novel music similarity detection (MSD) algorithm leveraging deep learning (DL) and convolutional neural networks (CNNs).
  • To enhance music feature extraction by integrating the Harmony and Percussive Source Separation (HPSS) algorithm with CNNs for improved MSD accuracy.

Main Methods:

  • An MSD algorithm was constructed using convolutional neural networks (CNNs), a type of deep learning (DL) algorithm.
  • The Harmony and Percussive Source Separation (HPSS) algorithm decomposed music spectrograms into harmonic and percussive components, which were processed alongside the original spectrogram data by the CNN.
  • Training hyperparameters were adjusted, and the dataset was expanded to analyze the impact of network parameters on music detection rates.

Main Results:

  • Experiments conducted on the GTZAN Genre Collection dataset demonstrated that the proposed method effectively improves MSD using a single feature.
  • The developed algorithm achieved a final detection accuracy of 75.6%, outperforming other classical detection methods.

Conclusions:

  • The integration of DL-based CNNs with HPSS for feature extraction represents a superior approach to music similarity detection.
  • The study highlights the effectiveness of the proposed method in improving music genre classification accuracy and its potential for advancing digital music analysis.