Window Function-less DFT with Reduced Noise and Latency for Real-Time Music Analysis

TL;DR

This paper introduces a window function-less DFT algorithm that reduces noise and latency, enhancing real-time music analysis by improving resolution and signal clarity without increasing computational load.

Contribution

The authors present a novel DFT-based method that eliminates window functions, achieving lower noise and latency while maintaining high resolution for real-time music applications.

Findings

Significantly reduced sidelobes and noise levels.

Enhanced time resolution without losing frequency detail.

Better performance than traditional FFT and DFT methods.

Abstract

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.