无需窗函数的DFT算法，显著降噪并提升实时音乐分析速度与精度。
Window Function-less DFT with Reduced Noise and Latency for Real-Time Music Analysis
- 后处理DFT输出，避免窗函数引入的频谱泄漏
- 指数间隔频点映射乐音，时间分辨率提升且不损失频率精度
- 适合低延迟音乐可视化与自动转录等实时应用
音乐分析应用需要在高时频分辨率和低噪声环境下实现，同时满足实时性对低延迟和低计算量的要求。本文提出一种基于DFT的算法，通过后处理DFT输出而无需使用窗函数，显著降低旁瓣和噪声，提升时间分辨率而不牺牲频率分辨率。采用指数间隔的输出频点，直接对应音乐中的音符。相比现有FFT与DFT方法，性能明显提升，为实时可视化提供了可能，并有助于自动转录等其他应用的分析质量改进。
原文摘要 · Abstract (English)
Music analysis applications demand algorithms that can provide both high time and frequency resolution while minimizing noise in an already-noisy signal. Real-time analysis additionally demands low latency and low computational requirements. We propose a DFT-based algorithm that accomplishes all these requirements by extending a method that post-processes DFT output without the use of window functions. Our approach yields greatly reduced sidelobes and noise, and improves time resolution without sacrificing frequency resolution. We use exponentially spaced output bins which directly map to notes in music. The resulting improved performance, compared to existing FFT and DFT-based approaches, creates possibilities for improved real-time visualizations, and contributes to improved analysis quality in other applications such as automatic transcription.
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