TapToTab : Video-Based Guitar Tabs Generation using AI and Audio Analysis
This addresses the problem of inconsistent and incomplete guitar tab generation for music education and transcription, though it appears incremental as it builds on existing deep learning and audio analysis methods.
The paper tackled automating guitar tablature generation from video inputs by using YOLO models for fretboard detection and Fourier Transform-based audio analysis for note identification, resulting in substantial improvements in detection accuracy and robustness compared to traditional techniques.
The automation of guitar tablature generation from video inputs holds significant promise for enhancing music education, transcription accuracy, and performance analysis. Existing methods face challenges with consistency and completeness, particularly in detecting fretboards and accurately identifying notes. To address these issues, this paper introduces an advanced approach leveraging deep learning, specifically YOLO models for real-time fretboard detection, and Fourier Transform-based audio analysis for precise note identification. Experimental results demonstrate substantial improvements in detection accuracy and robustness compared to traditional techniques. This paper outlines the development, implementation, and evaluation of these methodologies, aiming to revolutionize guitar instruction by automating the creation of guitar tabs from video recordings.