Xueying Zhang

Progress in speech emotion recognition

Emotional information in speech signal is an important information resource. When verbal expression combined with human emotion, emotional speech processing is no longer a simple mathematical model or pure calculations. Fluctuations of the mood are controlled by the brain perception; speech signal processing based on cognitive psychology can capture emotion better. In this paper the relevance analysis between speech emotion and human cognition is introduced firstly. The recent progress in speech emotion recognition was summarized including the review of speech emotion databases, feature extraction and emotion recognition networks. Secondly a fuzzy cognitive map network based on cognitive psychology is introduced into emotional speech recognition. In addition, the mechanism of the human brain for cognitive emotional speech is explored. To improve the recognition accuracy, this report also tries to integrate event-related potentials to speech emotion recognition. This idea is the conception and prospect of speech emotion recognition mashed up with cognitive psychology in the future.

Energy-efficient application assignment in profile-based data center management through a repairing genetic algorithm

Abstract The massive deployment of data center services and cloud computing comes with exorbitant energy costs and excessive carbon footprint. This demands green initiatives and energy-efficient strategies for greener data centers. Assignment of an application to different virtual machines has a significant impact on both energy consumption and resource utilization in virtual resource management of a data centre. However, energy efficiency and resource utilization are conflicting in general. Thus, it is imperative to develop a scalable application assignment strategy that maintains a trade-off between energy efficiency and resource utilization. To address this problem, this paper formulates application assignment to virtual machines as a profile-driven optimization problem under constraints. Then, a Repairing Genetic Algorithm (RGA) is presented to solve the large-scale optimization problem. It enhances penalty-based genetic algorithm by incorporating the Longest Cloudlet Fastest Processor (LCFP), from which an initial population is generated, and an infeasible-solution repairing procedure (ISRP). The application assignment with RGA is integrated into a three-layer energy management framework for data centres. Experiments are conducted to demonstrate the effectiveness of the presented approach, e.g., 23% less energy consumption and 43% more resource utilization in comparison with the steady-state Genetic Algorithm (GA) under investigated scenarios.

An AFK-SVD Sparse Representation Approach for Speech Signal Processing

Sparse representation is a common issue in many signal processing problems. In speech signal processing, how to sparsely represent a speech signal by dictionary learning for improving transmission efficiency has attracted considerable attention in recent years. K-SVD algorithm for dictionary learning is a typical method. But it requires to know the dictionary size prior to dictionary training. A suitable dictionary size can effectively avoid the problem of under-representation or over-representation, which affects the quality of reconstruction speech significantly. To tackle this problem, an Adaptive dictionary size Feedback filtering K-SVD (AFK-SVD) approach is presented in this paper for dictionary leaning. The proposed method first selects the dictionary size adaptively based on the speech signal feasure prior to dictionary learning, and then filters out the noise caused by over-representation. The approach has two unique features: (1) a learning model is constructed based on the training set specifically for adaptive determination of a range of the dictionary size; and (2) a two-level feedback filter measure is developed for removal of speech distortion caused by over-representation. The speech signals from TIMIT speech data sets are used to demonstrate the presented AFK-SVD approach. Experimental results showed that, in comparison with K-SVD, the proposed AFK-SVD method can improve the quality of the reconstructed speech signal in PESQ by 0.8 and SNR by 3 - 7 dB in average.

Extraction of EEG Components Based on Time - Frequency Blind Source Separation

In order to extract EEG characteristic waves better, this paper adopts the method of combining wavelet transform with time-frequency blind source separation based on smooth pseudo Wigner-Ville distribution. Firstly, the EEG signal is extracted by wavelet transform to reconstruct the β wave band signal and reconstructed as the initial extracted characteristic wave. Then, to remove the other components which are less relevant to get the enhanced beta wave signal, the time-frequency blind source separation technique based on the smooth pseudo-Wigner distribution is used for the initial extracted Target wave. Finally, the features are extracted, and the support vector machine is used to classify and identify the emotional categories. The experimental results show that the recognition rate is improved when the characteristic wave is extracted by using wavelet transform only.

A hybrid NRS- CART algorithm and its application on coal mine floor water-inrush prediction

With the increase of water-inrush accidents from coal mine, water-inrush prediction has become a significant aim for coal mine safety experts. As an intelligent classifying algorithm, the Classification and Regression Tree (CART) is a potential method for predicting the possibility of water inrush from coal seam floor. One of its main advantages is that the Decision Rules (DRs) can be extracted from its structure. Another is that these DRs can be used to analysis safety problems. However, the time of establishing the decision tree is too long because of the existence of the redundant information. This paper presents an effective method named NRS-CART, which is a hybrid method by combining neighborhood rough set (NRS) and classification and regression tree (CART). Moreover, the novel approach was used to detect and classify water-inrush possibilities. The experimental results showed that it only took 0.3455 seconds to predict the water-inrush possibility using the proposed method, whereas the CART spent 1.0411 seconds to predict for the same dataset, and at the same time the prediction accuracy was also improved from 88.78% to 93.90%.