# Data-driven Estimation of Sinusoid Frequencies

arXiv: https://arxiv.org/abs/1906.00823 · HTML: https://mlsanity.com/paper/1906.00823/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2019.

## Built on by (most cited first, 10 of 10)

- [Learning to Estimate Without Bias](https://mlsanity.com/paper/2110.12403/) (2021)
- [SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network](https://mlsanity.com/paper/2106.05490/) (2021)
- [Deep Learning-based Estimation for Multitarget Radar Detection](https://mlsanity.com/paper/2305.05621/) (2023)
- [Deep Learning-Based Multiband Signal Fusion for 3-D SAR Super-Resolution](https://mlsanity.com/paper/2305.02017/) (2023)
- [Attention-Refined Unrolling for Sparse Sequential micro-Doppler Reconstruction](https://mlsanity.com/paper/2306.14233/) (2023)
- [Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems](https://mlsanity.com/paper/2306.15341/) (2023)
- [Dilated CNNs for Periodic Signal Processing: A Low-Complexity Approach](https://mlsanity.com/paper/2604.21651/) (2026)
- [Frequency Estimation Based on SNR-adaptive Frequency Estimator Under Wide SNR Range](https://mlsanity.com/paper/2609.07034/) (2026)
- [Fold First, Detect Directly: Communication Symbol Detection Without Unfolding for Low-Bitrate Modulo-ADCs](https://mlsanity.com/paper/2609.11298/) (2026)
- [Accuracy- and Real-Time-Aware 4D Radar Preprocessing for Autonomous Driving Perception Systems](https://mlsanity.com/paper/2609.18542/) (2026)

## Abstract (arXiv)

Frequency estimation is a fundamental problem in signal processing, with applications in radar imaging, underwater acoustics, seismic imaging, and spectroscopy. The goal is to estimate the frequency of each component in a multisinusoidal signal from a finite number of noisy samples. A recent machine-learning approach uses a neural network to output a learned representation with local maxima at the position of the frequency estimates. In this work, we propose a novel neural-network architecture that produces a significantly more accurate representation, and combine it with an additional neural-network module trained to detect the number of frequencies. This yields a fast, fully-automatic method for frequency estimation that achieves state-of-the-art results. In particular, it outperforms existing techniques by a substantial margin at medium-to-high noise levels.