# PRIME-ANC: Path-Ratio-Informed Modeling for Efficient Neural Filter Synthesis in Active Noise Control

arXiv: https://arxiv.org/abs/2609.31772 · HTML: https://mlsanity.com/paper/2609.31772/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Builds on (most cited first, 5 of 5)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Deep Generative Fixed-filter Active Noise Control](https://mlsanity.com/paper/2303.05788/) (2023)
- [Co-Initialization of Control Filter and Secondary Path via Meta-Learning for Active Noise Control](https://mlsanity.com/paper/2601.13849/) (2026)
- [Transformer-based End-to-End Control Filter Generation for Active Noise Control](https://mlsanity.com/paper/2605.00494/) (2026)
- [Robust Soft-Constrained Spatially Selective Active Noise Control for Hearables Under Secondary Path Variations](https://mlsanity.com/paper/2605.17407/) (2026)

## Abstract (arXiv)

Changes in listener acoustics require active noise control (ANC) filters to be redesigned for new acoustic paths. We introduce PRIME-ANC, a shared neural synthesizer that learns a bounded, path-dependent logmagnitude correction to a regularized path-ratio base. Minimum-phase reconstruction and truncation produce finite-impulse-response (FIR) filters; training optimizes their noise-control performance. Across ten random training/test splits per dataset, PRIME-ANC achieves average held-out one-third-octave reductions of 18.81 and 17.76 dB over 50 Hz-5 kHz on a ten-path dataset and a public earphone database, respectively. On original earphone measurements, it improves upon the path-ratio base by 7.89 dB. Ablation studies support the contributions of both the analytic base and the path-dependent correction. Given calibrated paths for a held-out listener condition, PRIME-ANC generates a path-specific FIR without iterative optimization. With three Gauss-Newton updates, it reaches 21.43 dB reduction, approaching direct weighted least-squares design while producing lower amplification and root-mean-square control output.