HAD-ANC: A Hybrid System Comprising an Adaptive Filter and Deep Neural Networks for Active Noise Control ANC system

Announcement

This GitHub account was created to comply with INTERSPEECH's double-blind regulations, and it will be relocated to a different address once the acceptance results are published.

Model for HAD-ANC

Gated convolutional recurrent network (GCRN) for HAD-ANC

Description of our PyTorch implementation of GCRN.

We uploaded 2 pre-trained GCRNs for HAD-ANC.

$GCRN_{1}$ follows the adaptive filter to handle nonlinear distortion by reducing the residual error of linear filtering and models the reverse of both loudspeaker and secondary path.

$GCRN_{2}$ models the loudspeaker and secondary path to force the adaptive filter to estimate only the primary path.

Subscription of train and validation set preparation

1. Download 16 kHz noise signal

Download DEMAND https://zenodo.org/record/1227121#.ZAxCu3ZByUk

and MS-SNSD https://github.com/microsoft/MS-SNSD.

Note that exclude labeled as "babble" signal in noise train and noise_test of MS-SNSD.

2. Create development set

Split all signals into 6-second audio clips and index them in order.

Then normalize all 15639 clips.

3. Create validation set

Select the audio clips indexed by a multiple of 10 in the development set.

Multiply each of them by random numbers between 0.3 and 1.0.

4. Create train set

Once the validation sets consisting of 1,563 signals are created, the remaining audio clips from the development set compose the train set.

Acknowledge

In our process, we study an important project:

https://github.com/JupiterEthan/GCRN-complex.

Thanks for authors to open source code!