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To overcome the limitation and obtain more appropriate control filters, a generative fixed-filter active noise control (GFANC) approach is proposed in this paper. Specifically, various control filters can be generated by using different hard weights to combine pre-trained sub control filters. The hard weights are automatically obtained via a one-dimensional convolutional neural network given the incoming noise.

Python 29.03% Jupyter Notebook 70.97%

gfanc-generative-fixed-filter-active-noise-control's Introduction

Generative-Fixed-filter-Active-Noise-Control-based-on-Deep-Learning (GFANC)

This is the code of my ICASSP2023 paper "Generative Fixed filter Active Noise Control based on Deep Learning". You can find the paper at https://arxiv.org/pdf/2208.08082.pdf or at IEEE Xplore.

To obtain more appropriate control filters for differen primary noises, a generative fixed-filter active noise control (GFANC) approach is proposed in this paper. Specifically, various control filters can be generated by using different hard weights to combine sub control filters. The hard weights are automatically obtained via a one-dimensional convolutional neural network given the incoming noise.

How to use the code:

we have provided the trained 1D CNN model, you can easily run the "Noise_Cancellation_GFANC.ipynb" file to get the noise reduction results. The real noises in our experiemnts are provided in "Real Noise Examples". The 1D CNN is trained using a synthetic noise dataset, its label file is 'Hard_Index.csv'. The entire dataset is available at https://drive.google.com/file/d/1hs7_eHITxL16HeugjQoqYFTs-Cm7J-Tq/view?usp=sharing

Especially, the pre-trained sub control filters are obtained on synthetic acoustic paths, where the primary and secondary paths are bandpass filters. If you want to use the GFANC method on new acoustic paths only requires obtaining the corresponding broadband control filter and decomposing it into sub control filters. Noticeably, the trained 1D CNN in the GFANC method can remain unchanged.

Platform: NVIDIA-SMI 466.47, Driver Version: 466.47, CUDA Version: 11.3 Environment: Jupyter Notebook 6.4.5, Python 3.9.7, Pytorch 1.10.1

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gfanc-generative-fixed-filter-active-noise-control's People

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gfanc-generative-fixed-filter-active-noise-control's Issues

GFANC for a headphone Pri and Sec Path.

Thanks for your sharing for ANC.
When I use a headphone Pri and Sec Path, I found GFANC can't work well.
In GFANC Paper, The Pri and Sec Path are bandpass FIR filter. I try to use our measure path, the mag response are below.

Pri Path:
first_path_02

Sec Path:
sec_path_02

I put 20~7980Hz white noise signal into train_fxlms_algorithm to create Pretrained_Sub_Control_filters. The fxlms output result is below.

20_7980_fxlms

Using the above Pretrained_Sub_Control_filters to train. The Accuracy is not good.

Epoch 37
Learning rate: 7.8125e-05
Training Loss: 0.12145824613980949 Training Accuracy: 0.5167499999999995
Validation Loss : 0.12397415339946746 Validation Accuracy : 0.5225000000000001

And the GFANC result for Aircraft nosie is also not good.

GFANC1

I think there is a lot of nolinear in our pri and sec path and the fxlms can't get a ideal FIR filter.

Training code

Hi,

I have read your work, and it is very interesting. Thank you for sharing this with the community. I would like to use your algorithm with other control paths, but the control is not applied, so I would like to retrain the model with my data. Therefore, I wanted to know if it is possible to share the training code of your model as well.

Kind regards,

The filters of SFANC and GFANC

Hi there,

First of all, I want to express my appreciation for your work on SFANC-FxNLMS and GFANC. It's inspiring to see someone working on open-source ANC systems, and your assistance has been incredibly helpful to me.

However, I have encountered a question that I am struggling to understand. Both SFANC and GFANC utilize the Control_filter_selection() function to generate the filters that SFANC and GFANC required (e.g., 10 filters for a 10-second duration). But, during the actual SFANC and GFANC stages, only 9 filters are utilized. This discrepancy arises because the first-second noise uses the filter that are entirely composed of zeros, and the rest of "i" second noise uses the "i - 1"th filter, and nobody utilize the last filter. I am puzzled as to why the "i" second noise cannot use the "i"th filter directly.

In the GFANC paper, it is mentioned that:
"However, GFANC and SFANC cannot handle the first-second noise because they update the control filter coefficients for the next second based on the first-second noise."

However, in the program, I haven't come across any code segment that explicitly updates the control filter coefficients based on the first-second noise. Instead, the control filter coefficients appear to be switched to self.Filter_vector[j] at the end of each second.

Could you please help clarify this discrepancy for me? I am eager to gain a better understanding of how the control filter coefficients are updated in SFANC-FxNLMS and GFANC. Thank you in advance for your assistance.

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