Defining Fundamental Frequency for Almost Harmonic Signals
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Abstract (arXiv)
In this work, we consider the modeling of signals that are almost, but not quite, harmonic, i.e., composed of sinusoids whose frequencies are close to being integer multiples of a common frequency. Typically, in applications, such signals are treated as perfectly harmonic, allowing for the estimation of their fundamental frequency, despite the signals not actually being periodic. Herein, we provide three different definitions of a concept of fundamental frequency for such inharmonic signals and study the implications of the different choices for modeling and estimation. We show that one of the definitions corresponds to a misspecified modeling scenario, and provides a theoretical benchmark for analyzing the behavior of estimators derived under a perfectly harmonic assumption. The second definition stems from optimal mass transport theory and yields a robust and easily interpretable concept of fundamental frequency based on the signals' spectral properties. The third definition interprets the inharmonic signal as an observation of a randomly perturbed harmonic signal. This allows for computing a hybrid information theoretical bound on estimation performance, as well as for finding an estimator attaining the bound. The theoretical findings are illustrated using numerical examples.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Mar 2020.
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Citing papers per year: 2024: 2; 2025: 1; 2026: 1.
4 papers.
- 2024Fredformer: Frequency Debiased Transformer for Time Series Forecastingcited by 43
- 2025Tokenizing Single-Channel EEG with Time-Frequency Motif Learningcited by 14
- 2024FredNormer: Frequency Domain Normalization for Non-stationary Time Series Forecastingcited by 2
- 2026AdaMamba: Adaptive Frequency-Gated Mamba for Long-Term Time Series Forecastingcited by 0
- 2026AdaMamba: Adaptive Frequency-Gated Mamba for Long-Term Time Series Forecasting25 Apr 2026
- 2025Tokenizing Single-Channel EEG with Time-Frequency Motif Learning22 Feb 2025
- 2024FredNormer: Frequency Domain Normalization for Non-stationary Time Series Forecasting2 Oct 2024
- 2024Fredformer: Frequency Debiased Transformer for Time Series Forecasting13 Jun 2024