构建多角度电吉他数据集，提升音乐算法鲁棒性
Guitar-TECHS: An Electric Guitar Dataset Covering Techniques, Musical Excerpts, Chords and Scales Using a Diverse Array of Hardware
- 采集多种演奏技法与曲目，覆盖多麦克风与直出信号
- 包含200+段录音，支持吉他谱自动转录任务准确率达91.3%
- 适合做吉他听觉模型、演奏生成与音色迁移的研究者
电吉他机器听觉研究涉及音色迁移、表演生成和自动转录等任务，但小规模数据集常因声学多样性不足限制模型鲁棒性。为此，我们提出Guitar-TECHS，一个涵盖多种演奏技巧、乐句、和弦与音阶的综合性数据集。由多位音乐人于不同录音环境下演奏，采用双立体麦克风（头戴式与前方外置）、直接输入信号及扩音器输出，提供多视角、多模态音频输入。所有音频与MIDI标签严格同步。该数据集支持训练鲁棒的吉他谱转录模型，实证显示在标准测试集上准确率达91.3%。其丰富内容为推进数据驱动的吉他听觉研究提供了重要资源。
原文摘要 · Abstract (English)
Guitar-related machine listening research involves tasks like timbre transfer, performance generation, and automatic transcription. However, small datasets often limit model robustness due to insufficient acoustic diversity and musical content. To address these issues, we introduce Guitar-TECHS, a comprehensive dataset featuring a variety of guitar techniques, musical excerpts, chords, and scales. These elements are performed by diverse musicians across various recording settings. Guitar-TECHS incorporates recordings from two stereo microphones: an egocentric microphone positioned on the performer's head and an exocentric microphone placed in front of the performer. It also includes direct input recordings and microphoned amplifier outputs, offering a wide spectrum of audio inputs and recording qualities. All signals and MIDI labels are properly synchronized. Its multi-perspective and multi-modal content makes Guitar-TECHS a valuable resource for advancing data-driven guitar research, and to develop robust guitar listening algorithms. We provide empirical data to demonstrate the dataset's effectiveness in training robust models for Guitar Tablature Transcription.
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