Soloist: Generating Mixed-Initiative Tutorials from Existing Guitar Instructional Videos Through Audio Processing

Paper Title

Soloist: Generating Mixed-Initiative Tutorials from Existing Guitar Instructional Videos Through Audio Processing

Paper Information

• Domain: Human-Computer Interaction (HCI), Intelligent Tutoring Systems, Music Learning Technology
• Keywords: Soloist, Video Learning, Intelligent Tutoring Systems, Music Learning, Audio Processing, Mixed Learning, Waveform Navigation, Video Transformation

Research Background and Problem

• Problems and Challenges:

  • Online music learning videos, while convenient, lack immediate feedback and targeted guidance, making it difficult for users to assess their performance and improve.
  • Video navigation is unsuitable for music learning, as it is challenging to replay specific segments or easily locate desired content.
  • Current intelligent tutoring systems provide limited support for music learning and often require manual tutorial creation from scratch, which is costly and difficult to personalize.

• Significance:

  • Millions of enthusiasts learn instruments, especially guitar, through online videos. However, existing methods are inefficient and need improvements to enhance learning experiences and outcomes.
  • Intelligent systems can help users overcome device limitations and knowledge barriers, expanding the accessibility and inclusivity of education.

• Research Motivation and Related Work:

  • Intelligent Tutoring Systems (ITS) have been applied in fields like language learning, but music learning still faces technical challenges in audio processing and user performance recognition.
  • Interactive video tutorials enhance user experience but fail to address navigation issues in music learning.
  • Existing systems (e.g., Yousician) limit the scale and flexibility of learning content, such as the predefined nature of teaching resources.

Solution

• Proposed Method:

  • Soloist System: A mixed-initiative guitar solo tutorial generation framework that extracts information from existing music instructional videos using deep learning-based audio processing.
  • The system automatically segments instrument demonstration areas in videos and generates interactive visualizations to support efficient navigation and real-time user performance feedback.

• Innovations:

  • Utilizes publicly available instructional videos as tutorial sources, eliminating the need for creating content from scratch.
  • Combines audio segmentation with score detection to generate interactive and customizable video navigation tools.
  • Mixed teaching approach: integrates algorithms with user intervention for personalized adjustments.

• Implementation Steps and Key Technologies:

  • Backend Processing:
    • Uses Spleeter for sound separation, decomposing video audio into vocal and instrumental tracks.
    • Employs CREPE for pitch detection, extracting notes and melody information from the instrument regions.
    • Automatic video segmentation: detects non-silent audio segments to generate instrument demonstration areas.
  • Frontend Interaction:
    • Visualizes waveforms and area markers for quick playback control and looped segment practice.
    • Real-time feedback tools include melody visualization, audio playback, accuracy scoring, and progress tracking.

Research Outcomes

• Specific Results:

  • Soloist generates personalized interactive tutorials through technical processing, enabling users to learn guitar solos more efficiently.
  • The system's voice and instrument segmentation performed close to manual annotations in experiments, with scoring algorithms providing initial guidance and allowing manual adjustments for improved accuracy.
  • User studies indicate that the system's navigation tools and feedback mechanisms significantly enhanced their guitar learning experience.

• Advantages:

  • Addresses navigation difficulties and lack of feedback in traditional video learning.
  • Offers flexibility to support various types of videos and teaching styles.
  • Suitable for diverse user roles, including beginners and advanced players.

• Experimental Results:

  • Technical evaluations show the system's segmentation accuracy surpasses random or uniform segmentation baselines and aligns with manual segmentation performance (F1 score ≈ 0.9).
  • In user studies, all participants expressed positive feedback on the system's functionality and experience, particularly in repeat practice areas and visual feedback tools.

• Limitations and Future Directions:

  • Currently supports only single-note melodies; future work could expand to multi-note and chord detection.
  • Some features, such as query and connection areas, had low usage rates among certain user groups, requiring improved discoverability.
  • The system does not address abstract musical concepts like rhythm and expressiveness; future iterations could incorporate more complex scoring models.

Conclusion

The Soloist system combines artificial intelligence with user interaction to innovatively enhance navigation and feedback experiences in online music video learning. It provides an efficient and cost-effective alternative for instrument learning while contributing an effective technical framework to the field of music education.