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2775results about How to "Improve noise immunity" patented technology

Voiceprint identification method based on Gauss mixing model and system thereof

The invention provides a voiceprint identification method based on a Gauss mixing model and a system thereof. The method comprises the following steps: voice signal acquisition; voice signal pretreatment; voice signal characteristic parameter extraction: employing a Mel Frequency Cepstrum Coefficient (MFCC), wherein an order number of the MFCC usually is 12-16; model training: employing an EM algorithm to train a Gauss mixing model (GMM) for a voice signal characteristic parameter of a speaker, wherein a k-means algorithm is selected as a parameter initialization method of the model; voiceprint identification: comparing a collected voice signal characteristic parameter to be identified with an established speaker voice model, carrying out determination according to a maximum posterior probability method, and if a corresponding speaker model enables a speaker voice characteristic vector X to be identified to has maximum posterior probability, identifying the speaker. According to the method, the Gauss mixing model based on probability statistics is employed, characteristic distribution of the speaker in characteristic space can be reflected well, a probability density function is common, a parameter in the model is easy to estimate and train, and the method has good identification performance and anti-noise capability.
Owner:LIAONING UNIVERSITY OF TECHNOLOGY

Time-resolved extreme-low-light multispectral imaging system and method

ActiveCN102393248AResolve time resolutionSolve the time resolution by using single photon counter line arraySpectrum investigationPhysicsHyperspectral imaging
The invention provides a time-resolved extreme-low-light multispectral imaging system and method and belongs to the field of extreme-low-light multispectral imaging, wherein the system is triggered through a trigger to ensure the time-resolved property, and the multispectral high-resolution two-dimensional color imaging for extreme-low-light objects is realized by combining a triggering technology of the trigger, the compressive sensing theory, a DLP (digital light processing) technology, a spectrum-dividing technology, an optical fiber coupling technology and a photon counter linear array detection technology; the system is composed of an extreme-low-light light source or self-luminous organisms, the trigger, an optical filter, an optical imaging system, a DMD (digital mirror device) micromirror array, an optical focussing and collecting system, a spectrophotometer, a photo counter lineary array consisting of a plurality of photon counters with different wavelengths, a driving control module and an optimization algorithm module; the sensitivity of the system can reach the level of a photon, and can be widely applied to the fields of self-luminous organism detection, medical treatment imaging, data acquisition, communication, astronomy, military, hyperspectral imaging, measurement in quantum mechanics and the like.
Owner:NAT SPACE SCI CENT CAS

Continuous voice recognition method based on deep long and short term memory recurrent neural network

The invention provides a continuous voice recognition method based on a deep long and short term memory recurrent neural network. According to the method, a noisy voice signal and an original pure voice signal are used as training samples, two deep long and short term memory recurrent neural network modules with the same structure are established, the difference between each deep long and short term memory layer of one module and the corresponding deep long and short term memory layer of the other module is obtained through cross entropy calculation, a cross entropy parameter is updated through a linear circulation projection layer, and a deep long and short term memory recurrent neural network acoustic model robust to environmental noise is finally obtained. By the adoption of the method, by establishing the deep long and short term memory recurrent neural network acoustic model, the voice recognition rate of the noisy voice signal is improved, the problem that because the scale of deep neutral network parameters is large, most of calculation work needs to be completed on a GPU is avoided, and the method has the advantages that the calculation complexity is low, and the convergence rate is high. The continuous voice recognition method based on the deep long and short term memory recurrent neural network can be widely applied to the multiple machine learning fields, such as speaker recognition, key word recognition and human-machine interaction, involving voice recognition.
Owner:TSINGHUA UNIV
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