GFANC-RL: reinforcement learning-based generative fixed-filter active noise control
The recent Generative Fixed-filter Active Noise Control (GFANC) method achieves a good trade-off between noise reduction performance and system stability. However, labelling noise data for training the Convolutional Neural Network (CNN) in GFANC is typically resource-consuming. Even worse, labelling...
Saved in:
Main Authors: | Luo, Zhengding, Ma, Haozhe, Shi, Dongyuan, Gan, Woon-Seng |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Article |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/180662 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Real-time implementation and explainable AI analysis of delayless CNN-based selective fixed-filter active noise control
by: Luo, Zhengding, et al.
Published: (2024) -
Deep generative fixed-filter active noise control
by: Luo, Zhengding, et al.
Published: (2023) -
Feedforward selective fixed-filter active noise control : algorithm and implementation
by: Shi, Dongyuan, et al.
Published: (2020) -
A frequency-domain output-constrained active noise control algorithm based on an intuitive circulant convolutional penalty factor
by: Shi, Dongyuan, et al.
Published: (2023) -
Spatial-frequency-based selective fixed-filter algorithm for multichannel active noise control
by: Su, Xiruo, et al.
Published: (2024)