arXiv 2409.086182024-09-13cs.SDcs.CVeess.AS

TapToTab：基于视频的AI与音频分析吉他六线谱生成方法

TapToTab : Video-Based Guitar Tabs Generation using AI and Audio Analysis

Ali Ghaleb, Eslam ElSadawy, Ihab Essam, Mohamed Abdelhakim, Seif-Eldin Zaki, Natalie Fahim, Razan Bayoumi, Hanan Hindy

AI总结：

针对现有吉他六线谱自动生成方法指板检测与音符识别准确率不足的问题，提出结合YOLO实时指板检测与傅里叶变换音频分析的深度学习方法，显著提升检测准确率与鲁棒性，可用于革新吉他教学。

摘要：

从视频输入自动生成吉他六线谱在提升音乐教育效果、转录准确性与演奏分析方面具有重要应用前景。现有方法在一致性与完整性上存在挑战，尤其在指板检测与音符精准识别方面问题突出。针对这些问题，本文提出一种先进方法：采用深度学习（具体为YOLO模型）实现实时指板检测，结合基于傅里叶变换的音频分析完成精准音符识别。实验结果表明，与传统技术相比，该方法在检测准确率与鲁棒性上均有大幅提升。本文阐述了上述方法的开发、实现与评估过程，旨在通过自动化从视频录制内容生成吉他六线谱的方式革新吉他教学。

英文摘要：

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.

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