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A Lightweight Deep Learning-Based Approach for Jazz Music Generation in MIDI Format.

Prasant Singh Yadav1, Shadab Khan2, Yash Veer Singh3

  • 1Department of Computer Science and Engineering, Mahamaya Polytechnic of Information Technology (Govt.), Hathras, Uttar Pradesh 204102, India.

Computational Intelligence and Neuroscience
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Summary

This study introduces a lightweight deep learning model for generating original jazz music in MIDI format. The model creates music continuations based on input segments, ensuring similarity to the training dataset.

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Area of Science:

  • Artificial Intelligence
  • Music Information Retrieval
  • Computational Musicology

Background:

  • Accurate musical difficulty estimation is crucial for effective music learning but is complicated by subjective content and data scarcity.
  • Existing music generation models often lack genre specificity or require extensive computational resources.

Purpose of the Study:

  • To propose a lightweight deep learning model for generating original jazz music in MIDI format.
  • To enable music generation based on specific genres and input musical segments.
  • To address the challenges of subjectivity and data scarcity in computational music generation.

Main Methods:

  • Developed a lightweight deep learning model trained on classical jazz music in MIDI format.
  • The model takes a segment of music as input and generates its continuation.
  • Ensured dataset homogeneity to maintain desired output characteristics, focusing on classical jazz pieces.

Main Results:

  • The model successfully generates novel jazz music continuations in MIDI format.
  • Generated music exhibits similarity to the input data and adheres to the jazz genre.
  • The model demonstrates the capability to generate music with specific instrumental characteristics.

Conclusions:

  • The proposed lightweight deep learning approach offers an effective method for generating genre-specific music, particularly jazz.
  • This model can aid in music learning by providing tools for content generation and analysis.
  • Future work could explore broader genre applications and more sophisticated control over generated musical elements.