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A multimodal dataset for electric guitar playing technique recognition.

Alexandros Mitsou1, Antonia Petrogianni1, Eleni Amvrosia Vakalaki1

  • 1Institute of Informatics and Telecommunications, NCSR 'Demokritos, 27, Neapoleos str &, Patriarchou Grigoriou E, Ag. Paraskevi 153 41, Athens, Greece.

Data in Brief
|December 11, 2023
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Summary

This study introduces a new multimodal dataset for electric guitar playing styles to improve music coaching software. The dataset aids in developing machine learning models for automatic technique detection.

Keywords:
Audio signal processingElectric guitar recordingsMachine learningMusic information retrieval

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Area of Science:

  • Music Technology
  • Machine Learning
  • Signal Processing

Background:

  • Automatic detection of musical instrument playing styles is crucial for intelligent music coaching software.
  • Current methodologies are limited by the availability of comprehensive, real-world datasets for training machine learning models.
  • Existing datasets often lack the diversity and completeness required for robust playing technique identification.

Purpose of the Study:

  • To introduce a novel multimodal dataset of electric guitar playing techniques.
  • To facilitate the development of advanced machine learning models for automatic guitar playing style assessment.
  • To provide a valuable resource for creating intelligent music coaching and training applications.

Main Methods:

  • A multimodal dataset comprising 549 video (MP4) and audio (WAV) samples of nine electric guitar techniques was created.
  • Recordings were made using a smartphone, simulating real-world conditions with diverse exercises, guitars, and amplifier simulations.
  • Accompanying musescores were provided, and Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models were developed for technique classification using audio data.

Main Results:

  • The dataset successfully supported the development and testing of SVM and CNN models for classifying electric guitar techniques.
  • The multimodal nature and real-world recording setup enhance the dataset's utility for practical applications.
  • Experimental results demonstrated the feasibility of using the dataset for training effective machine learning models.

Conclusions:

  • The introduced multimodal dataset addresses the scarcity of resources for electric guitar playing style analysis.
  • This dataset is a significant contribution to the field of intelligent music education and performance analysis.
  • The publicly available code and extendable dataset format encourage further research and development in automated music technique recognition.