Single-Channel Signal Separation and Deconvolution with Generative Adversarial Networks
Authors: Qiuqiang Kong, Yong Xu, Philip J. B. Jackson, Wenwu Wang, Mark D. Plumbley
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We carry out the underdetermined single-channel signal separation and deconvolution experiments on MNIST dataset as a starting research to show the effectiveness of the proposed S-D approach with GANs. This paper is organized as follows: Section 2 formulates the underdetermined single-channel signal separation and deconvolution problem. Section 3 proposes the synthesising-decomposition (S-D) approach for this problem. Section 4 shows experimental results. Section 5 concludes and forecasts future work. |
| Researcher Affiliation | Collaboration | 1University of Surrey, Guildford, UK 2Tencent AI lab, Bellevue, USA {q.kong, w.wang, p.jackson, m.plumbley}@surrey.ac.uk, lucayongxu@tencent.com |
| Pseudocode | Yes | Algorithm 1 Training of a GAN [Goodfellow et al., 2014]. and Algorithm 2 Decomposition of a mixture source. |
| Open Source Code | Yes | The Py Torch implementation of this paper is released1. 1https://github.com/qiuqiangkong/gan_separation_deconvolution |
| Open Datasets | Yes | We carry out the underdetermined single-channel signal separation and deconvolution experiments on MNIST 10-digit dataset [Le Cun et al., 1998] |
| Dataset Splits | No | The paper mentions using the MNIST dataset for experiments and discusses training and testing, but it does not provide specific details on training, validation, and test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU, CPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch implementation' but does not specify the version number for PyTorch or any other software libraries used. |
| Experiment Setup | Yes | In the proposed S-D approach, we model the synthesising procedure with a deep convolutive generative adversarial network (DCGAN) [Radford et al., 2015], which can stabilize the training of a GAN and can generate high quality images as shown in [Radford et al., 2015]. A DCGAN consists of a generator G and a discriminator D. The input to G consists of a seed sampled from a Gaussian distribution N(0, σI). The Gaussian distribution has a dimension of 100 following [Radford et al., 2015]. The generator G has 4 transpose convolutional layers with number of feature maps of 512, 256, 128 and 1, respectively. Following [Radford et al., 2015], batch normalization [Ioffe and Szegedy, 2015] and Re LU non-linearity are applied after each transpose convolutional layer. The output of G is an image which has the same size as the images in the training data. The discriminator D takes a fake or a real image as input. The discriminator D consists of 4 convolutional layers, with a sigmoid output representing the probability that the input to D is from real data instead of generated data. Following [Radford et al., 2015], we use the Adam [Kingma and Ba, 2015] optimizer with a learning rate of 0.0002, a β1 of 0.5 and a β2 of 0.999 to train the generator. In decomposition, we freeze the trained generator G. We approximate p(x|ˆx) with a Gaussian distribution which works well in our experiment. We set β to 0.001 to regularize the mixing filters αk to be searched. The filters αk and zk are randomly initialized and optimized with Adam optimizer with a learning rate of 0.01, a β1 of 0.9 and a β2 of 0.999 (Algorithm 2). For comparison with regression based approaches, we apply a CNN [Xie et al., 2012] which consists 4 layers with batch normalization [Ioffe and Szegedy, 2015] and Re LU non-linearity. The number of layers and parameters are set to be the same as the discriminator D in the DCGAN. The CNN is trained to regress from individual source with noise s+n to individual source s. For comparison with decomposition based approaches, we train a dictionary for each of the 10 digits using NMF [Cichocki et al., 2009] with Euclidean distance. Each dictionary consists of 20 bases which performs well in our experiment. In decomposition, the trained dictionaries are concatenated to form a dictionary of 200 bases which is then used to decompose the mixtures. |