用频谱特征识别电吉他单音对应的琴弦，浏览器运行且效果真实可测。
Fretiq: Browser-Native Electric Guitar String Classification via Engineered Spectral Features and Held-Out Free-Play Evaluation
- 设计26维频谱特征，结合梅尔频率倒谱系数提升识别精度。
- 真实演奏测试准确率86.5%，远低于分割验证的97.3%，说明过拟合严重。
- 提出对比训练法收集相邻弦同音交替数据，显著提升模型泛化能力。
识别单音电吉他音频中对应琴弦是一项挑战：同一音高可能由多根琴弦发出，且未受训者难以察觉音色差异。本文提出Fretiq，一个基于浏览器的单乐器、单演奏者弦识别系统，采用26维特征表示（频带能量、谱统计量及13个梅尔频率倒谱系数）。五次随机种子验证显示帧级准确率为97.25±0.32%，剔除MFCC后降至92.09±0.50%，表明其为关键特征。引入对比训练法，记录同音在相邻弦间的交替演奏。初始对比实验因验证集不一致产生偏差；修正后的配对评估显示，加入对比会话数据使准确率提升25.78±1.46个百分点，且非仅因数据量增加所致。保留录音会话的评估得86.53±1.23%准确率，与独立自由演奏评估结果（87.8%）接近，显著低于分割验证结果，表明后者严重夸大泛化性能。系统使用Python与TypeScript实现特征提取，确保训练推理一致性，并记录两种实现失败模式。所有流程可在浏览器中完成，无需专用硬件。
原文摘要 · Abstract (English)
Identifying which string produces a given pitch in monophonic electric guitar audio is a classification challenge: a single pitch can often be produced on multiple strings, with timbral differences largely imperceptible to untrained humans. We present Fretiq, a preliminary single-instrument, single-player browser-based string classification system using a 26-dimensional feature representation of frequency band energies, spectral statistics, and 13 Mel-Frequency Cepstral Coefficients. Across five seeds, a shuffled frame-level validation split yields 97.25 +/- 0.32 percent accuracy, with an ablation study identifying MFCCs as the primary accuracy driver (92.09 +/- 0.50 percent without MFCCs). We introduce Comparison Training, a data collection method recording same-pitch pairs on adjacent strings in deliberate alternation. An initial shuffled-split comparison found no net benefit but was confounded by non-comparable validation sets across conditions. A corrected matched evaluation, using an identical recording session held out from training and model selection, shows including comparison-session data improves accuracy by 25.78 +/- 1.46 percentage points; a size-matched control shows this is not explained by training-set size alone. A recording-session-held-out evaluation yields 86.53 +/- 1.23 percent accuracy, closely matching an independently collected free-play evaluation (87.8 percent), both well below the shuffled-split figure, showing shuffled validation substantially overestimates real generalization here. We describe the feature extraction pipeline in Python and TypeScript for training-inference parity and document two implementation failure modes. The system runs entirely in-browser with no specialized hardware required.
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