Single Mixed Signal Research Articles

Compact Mixed-Signal Convolutional Neural Network Using a Single Modular Neuron

This paper demonstrates a compact mixed-signal (MS) convolutional neural network (CNN) design procedure by proposing a MS modular neuron unit that alleviates analog circuit related design issues such as noise. Through the first step of the proposed procedure, we design a CNN in software with a minimized number of channels for each layer while satisfying the network performance to the target, which creates low representational and computational cost. Then, the network is reconstructed and retrained with a single modular neuron that is recursively utilized for the entire network for the maximum hardware efficiency with a fixed number of parameters that consider signal attenuation. For the last step of the proposed procedure, the parameters of the networks are quantized to an implementable level of MS neurons. We designed the networks for MNIST and Cifar-10 and achieved compact CNNs with a single MS neuron with 97% accuracy for MNIST and 85% accuracy for Cifar-10 whose representational cost and computational cost are reduced to least two times smaller than prior works. The estimated energy per classification of the hardware network for Cifar-10 with a single MS neuron, designed with optimum noise and matching requirements, is $0.5\mu $ J, which is five times smaller than its digital counterpart.

Single Channel Source Separation with ICA-Based Time-Frequency Decomposition.

This paper relates to the separation of single channel source signals from a single mixed signal by means of independent component analysis (ICA). The proposed idea lies in a time-frequency representation of the mixed signal and the use of ICA on spectral rows corresponding to different time intervals. In our approach, in order to reconstruct true sources, we proposed a novelty idea of grouping statistically independent time-frequency domain (TFD) components of the mixed signal obtained by ICA. The TFD components are grouped by hierarchical clustering and k-mean partitional clustering. The distance between TFD components is measured with the classical Euclidean distance and the distance of Gaussian distribution introduced by as. In addition, the TFD components are grouped by minimizing the negentropy of reconstructed constituent signals. The proposed method was used to separate source signals from single audio mixes of two- and three-component signals. The separation was performed using algorithms written by the authors in Matlab. The quality of obtained separation results was evaluated by perceptual tests. The tests showed that the automated separation requires qualitative information about time-frequency characteristics of constituent signals. The best separation results were obtained with the use of the distance of Gaussian distribution, a distance measure based on the knowledge of the statistical nature of spectra of original constituent signals of the mixed signal.

Joint dictionary learning using a new optimization method for single-channel blind source separation

Cross projection is often produced between sub-dictionaries when a mixed speech signal is represented over a joint dictionary in single-channel blind source separation (SCBSS), which leads to poor separation performance. To solve this problem, we introduce a new optimization function of joint dictionary learning for SCBSS, which trains the identity sub-dictionaries and common sub-dictionary simultaneously. The existence of a common sub-dictionary can effectively avoid one source signal being represented by the interferer sub-dictionaries. The new optimization function presented in this paper effectively inhibits the cross projection via enforcing constraint and cross-penalty between the identity sub-dictionaries and common sub-dictionary. The optimization of the objective function is introduced in this work, which involves three steps: initial joint dictionary, coding update, and dictionary update. In the speech separation stage, the source separation is achieved by an identity sub-dictionary, the common sub-dictionary and the sparse coefficients of a single mixed signal over the joint dictionary. Experimental results verify that our algorithm works much better than the current algorithms.