65 results about "Time delay neural network" patented technology

The invention provides a speaker verification method and a speaker verification device. The speaker verification method comdprises the following steps: acquiring second voice; converting first voice and the second voice, which are acquired in advance, into corresponding first spectrogram and second spectrogram; conducting feature extraction on the first spectrogram and the second spectrogram by virtue of convolutional neural network so as to acquire corresponding first features and second features; conducting feature extraction on the first features and the second features by virtue of time delay neural network so as to acquire corresponding third features and fourth features; and verifying a speaker in accordance with the third features and the fourth features. According to the speaker verification method and the speaker verification device provided by the invention, in a mode of combining the convolutional neural network and the ime delay neural network, the first voice and the second voice undergo the feature extraction, and the third features and the fourth features, which are finally extracted, are compared, so that speaker verification is implemented; and the speaker verification method and the speaker verification device provided by the invention are simple in computation and strong in robustness, and an excellent recognition effect can be achieved.

The invention belongs to the field of time sequence forecasting analysis and in particular relates to a wind speed sequence forecasting method based on Kalman filtering. A high-order AR (auto-regressive) model of a wind speed time sequence is constructed by virtue of a time sequence analysis method, so that a state equation and a measurement equation of Kalman filtering are constructed; a training sample pair of the wind speed time sequence is constructed by the time sequence analysis method; a wind speed sequence is forecasted and analyzed by adopting a time delay neural network; a forecasting result of the time delay neural network is used as a measurement value of Kalman filtering; diagonal covariance matrixes of the state equation and the measurement equation of the Kalman filtering method are determined according to the AR model and a forecasting error of the time delay neural network, so that the wind speed sequence can be forecasted and analyzed by the Kalman filtering method. The wind speed sequence forecasting method can be applied to an on-line wind speed forecasting analysis system of a wind power plant.

The invention discloses a voiceprint recognition method based on a TDNN (time delay neural network), and solves the problems that a voiceprint recognition algorithm is complicated and data are complex. The voiceprint recognition method is technically characterized by extremely strong feature extraction capacity of a neural network. The TDNN is used for extracting the feature vector of a voice segment of a speaker, a pooling layer and a softmax layer are used for acquiring the posterior probability of the voice segment of the speaker, a loss function is used for training to obtain a cross entropy, the softmax layer is removed after training, the feature vector for finally training a PLDA (probabilistic linear discriminant analysis) model is acquired, transcription of training data is omitted, and simple calculation and good recognition effects are achieved.

The invention discloses a nonlinear neural network model for a modeling wide band RF (Radio Frequency) power amplifier. The model comprises an input layer, a hidden layer and an output layer, wherein the input data of the input layer comprises advance items x (n+1), |x (n+1)|3, ..., |x (n+1)|<2Q+1>, aligning items x(n), |x(n)|, |x(n)|[3], ..., |x (n)|<2Q+1>, and delay items x (n-1), ..., x (n-M[1]), |x (n-1)|, |x (n-1)|, ..., |x (n-M[2])|, ..., |x (n-1)|<2Q+1>, ..., |x (n-M[Q+2]|<2Q+1>, wherein the x (n+1) is base band complex data of an input end of RF power amplifier at current time, and the output of the output layer is y(n). The nonlinear neutral network has the advantages that a generalized memory effect (memory effects at the delay time and the advance time shall be considered) is considered based on a super-strong memory effect and a strong static nonlinearity of the modeling RF power amplifier; meanwhile, an input signal of an input layer does not only comprises a base band signal, but also comprises a model of a base band complex signal and a high power of the model, and the output signal of the output layer is a plural signal, therefore the modeling precision is higher and can be improved by 5dB in comparison with a real time delay neural network model.

The invention discloses a power amplifier behavioral modeling system based on a neural network and a method thereof, the modeling system comprises an input layer, a hidden layer and an output layer, modeling is performed based on the neural network, signal processing is performed respectively, and the method further comprises two processes of system training and system operation. According to theinvention, an activation function in a traditional behavior-level modeling system based on a real-value time-delay neural network is replaced with a leakage linear unit function from a hyperbolic tangent function; according to the invention, while behavioral modeling of the power amplifier is realized, the hardware implementation complexity of modeling is reduced, the modeling convergence rate isimproved, and the method has wide application and development prospects in a communication system.

The invention relates to the technical field of deep learning, signal processing, speech recognition, feature extraction, pronunciation and the like, aims at improving the overall effect of a Tibetan Weizang dialect spoken language recognition model in allusion to spoken language application scenes of Tibetan Weizang dialects, and provides a Tibetan Weizang dialect spoken language recognition method based on a deep time delay neural network. An audio data set mixed by three Tibetan dialects is adopted, an original audio data set is expanded through speed disturbance, noise adding and reverberation methods, the expanded data set is utilized to train the deep time delay neural network based on a chained chain model of an open-source speech recognition toolbox kaldi, the deep time delay neural network serves as a Tibetan acoustic model, and the acoustic model is trained for the second time by using the part of the Weizang dialect in the audio data so as to obtain a deep time delay neural network acoustic model for the Weizang dialect. The method is mainly applied to Tibetan Weizang dialect spoken language recognition occasions.