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The solution methods for the largest eigenvalue (singular value) of nonnegative tensors and convergence analysis a r t i c l e i n f o a b s t r a c t In this paper we study two solution methods for finding the largest eigenvalue (singular value) of general square (rectangular) non-negative tensors. For a positive tensor, one can find the largest eigenvalue(More)
This paper introduces the SHRC-Ginkgo speech synthesis system for Blizzard Challenge 2013. A unit selection based approach is adopted to develop our speech synthesis system using audiobook speech corpus. Aiming at roughly labeled corpora with several hundred hours of speech, our system adopts lightly-supervised acoustic model training of speech recognition(More)
Recently, the deep neural networks (DNNs) based acoustic modeling methods have been successfully applied to many speech recognition tasks. This paper reports the work about applying DNNs for syllable based acoustic modeling in Chinese automatic speech recognition (ASR). Compared with initial/finals (IFs), syllable can implicitly model the intra-syllable(More)
Spike detection from high data rate neural recordings is desired to ease the bandwidth bottleneck of bio-telemetry. An appropriate spike detection method should be able to detect spikes under low signal-to-noise ratio (SNR) while meeting the power and area constraints of implantation. This paper introduces a spike detection system utilizing lifting-based(More)
Within the statistical learning framework, this paper studies the regression model associated with the correntropy induced losses. The correntropy, as a similarity measure, has been frequently employed in signal processing and pattern recognition. Motivated by its empirical successes, this paper aims at presenting some theoretical understanding towards the(More)
This paper addresses the robust gradient learning (RGL) problem. Gradient learning models aim at learning the gradient vector of some target functions in supervised learning problems, which can be further used to applications, such as variable selection, coordinate covariance estimation, and supervised dimension reduction. However, existing GL models are(More)
—Spike detection is an essential first step in the analysis of neural recording signals. A new spike detection hardware architecture combining absolute threshold method and stationary wavelet transform (SWT) is described. The method enables spike detection with 90% accuracy even when the signal-to-noise is-1dB. A noise monitoring block was implemented to(More)