In this paper, we propose a method for supervised single-channel speech separation through sparse decomposition using periodic signal models. The proposed separation method employs sparse decomposition, which decomposes a signal into a set of periodic signals under a sparsity penalty. In order to achieve separation through sparse decomposition, the decomposed periodic signals have to be assigned to the corresponding sources. For the assignment of the periodic signal, we introduce clustering using a K-means algorithm to group the decomposed periodic signals into as many clusters as the number of speakers. After the clustering, each cluster is assigned to its corresponding speaker using preliminarily learnt codebooks. Through separation experiments, we compare our method with MaxVQ, which performs separation on the frequency spectrum domain. The experimental results in terms of signal-to-distortion ratio show that the proposed sparse decomposition method is comparable to the frequency domain approach and has less computational costs for assignment of speech components.

This paper proposes a rail-to-rail OTA. By adding a signal decomposing circuit at the input of given OTAs that have a limited input voltage range, a rail-to-rail OTA is obtained. Each decomposed input voltage signal is converted to a current signal by an OTA and each output current of OTAs is summed to obtain a linear output signal. Since the input signal is decomposed into small magnitude voltage signals, the OTAs used to the voltage-current conversion do not require a wide input-range and any OTA can be used to realize a rail-to-rail input voltage range OTA. HSPICE simulations are performed to verify the validity of the proposed method.