Msf-Based Speaker Automatic Emotional Recognition In Continuous Chinese Mandarin

Qin, Yuqiang; Zhang, Xueying

doi:10.1016/j.proeng.2011.08.417

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…Figure 3 show simulation result of 4 emotional(angry, happy, sad and neutral) continuous Chinese mandarin "My father bought me a car(in Chinese)", and its emotional envelops features [20]. As depicted in the Figure.3, we can see that emotions are decomposed using the piecewise power function in continuous Chinese mandarin speech, and gain a lot of IMFemotions.…”

Section: Simulation Resultsmentioning

confidence: 94%

EEMD-Based Speaker Automatic Emotional Recognition in Chinese Mandarin

Qin¹,

Qi²

2014

Appl. Math. Inf. Sci.

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Emotion feature extraction is the key to speech emotional recognition. And ensemble empirical mode decomposition(EEMD) is a newly developed method aimed at eliminating emotion mode mixing present in the original empirical mode decomposition(EMD). To evaluate the performance of this new method, this paper investigates the effect of a parameters pertinent to EEMD: speech emotional envelope. Firstly, a speaker emotional envelope features extraction based on EEMD is proposed in the paper. Using the piecewise power function in speech emotional envelope has a better effect in emotional identification. At the same time, the proposed technique has been utilized for classification of four kinds of emotional(angry, happy, sad and neutral) speech signals. Emotional intrinsic mode functions(IMFe) are obtained by empirical mode decomposition on emotional speech signals, the fast fourier transform(FFT) of each intrinsic mode function is extracted as the emotional feature coefficient which is used in speaker emotional identification applying by vector quantization. MATLAB is used to calculate the characteristic of emotional speech signals using empirical mode decomposition (EEMD). We obtain an emotional envelope by transforming the IMFe of emotional speech signals, and obtain a new method of emotion recognition according to different emotional envelop feature vectors. The results indicate the proposed method works well in speaker emotional identification.

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Section: Simulation Resultsmentioning

confidence: 94%

EEMD-Based Speaker Automatic Emotional Recognition in Chinese Mandarin

Qin¹,

Qi²

2014

Appl. Math. Inf. Sci.

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“…A proportional correction factor is defined by equation (2). Where, k is the number of one-hop neighbor nodes.…”

Section: Initial Localizationmentioning

confidence: 99%

“…Real-time acquisition of nodes' location information is one of the basic functions of wireless sensor network. Therefore, localization technology is one of the key technologies in most applications of wireless sensor network [2].…”

Section: Introductionmentioning

confidence: 99%

RSSI-based Rings Overlap Localization Algorithm in Wireless Sensor Network

Qin¹,

Ying²

2015

TOAUTOCJ

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This paper proposes a RSSI-based rings overlap localization algorithm of Environment Adaption(RROLA/EA) for large-scale wireless sensor network (WSN). Localization technology is one of the key technologies in most applications of wireless sensor network. Localization technology is composed of two phases: ranging phase and localization phase. RSSI-based ranging method is adopted in the ranging phase and rings overlap method is adopted in the localization phase. Firstly, through binary linear regression the parameters of signal attenuation are calibrated for a certain environment. Then, through differential error estimate method the inner radius and outer radius of rings are got respectively around beacon nodes and blind node. At last, the centroid of the rings overlapping region is taken as the location of blind node. Theoretical analyses and simulation results under different scenarios prove the correctness and validity of the localization algorithm.

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PGSA-Based Localization Algorithm for Wireless Sensor Network

Qin

Ying

2016

International Journal on Smart Sensing and Intelligent Systems

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This paper proposes a novel localization algorithm for wireless sensor network (WSN). Accurate localization is very important for WSN. WSN localization problem is sometimes regarded as an optimization problem. Plant growth simulation algorithm (PGSA) is a kind of new intelligent optimization algorithm, which is intelligent simulation of plant growth in natural way. In addition to the common characteristics of intelligent algorithms, PGSA show robustness and provides a global optimal solution, etc. In this paper, further enhancement of the algorithm by adding the plant root of adaptive backlight function to effectively improve the computing speed and localization precision has been reported. Comparing this algorithm with simulated annealing algorithm (SAA), simulation resultsshow that this algorithm has a higher and more consistent localization precision and faster computational speed.

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Msf-Based Speaker Automatic Emotional Recognition In Continuous Chinese Mandarin

Cited by 3 publications

References 2 publications

EEMD-Based Speaker Automatic Emotional Recognition in Chinese Mandarin

EEMD-Based Speaker Automatic Emotional Recognition in Chinese Mandarin

RSSI-based Rings Overlap Localization Algorithm in Wireless Sensor Network

PGSA-Based Localization Algorithm for Wireless Sensor Network

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