arXiv (OAI Expanded)
open access
Deep Learning-Based Predictive Fixed-Filter Active Noise Control for Dynamic Noises
The existing Generative Fixed-Filter Active Noise Control (GFANC) method generates a suitable control filter based on the current noise frame. This reactive design aims to estimate a control filter that is optimal for the present frame rather than the upcoming one. Consequently, it suffers from an i
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Coupled Meta-Adaptive Filtering for Active Noise Control Under Time-Varying Acoustic Paths#1105504
- Domain-Adaptive Dual-Gating Mixture of Experts for Generalizable Speech Deepfake Detection#1118782
- Co-Initialization of Control Filter and Secondary Path via Meta-Learning for Active Noise Control#974304
- DUA-D2C: Dynamic Uncertainty Aware Method for Overfitting Remediation in Deep Learning#972887
- Fuel Efficiency Prediction Using Deep Learning#105427
Record · ID 1109073
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive