6 results about "Noise enhancement" patented technology

This invention belongs to the field of multimedia analysis technology and discloses a multimodal media tampering detection method, system, device, and medium based on multi-view contrastive learning. The method includes: acquiring a training dataset, which includes training image-training text pairs and corresponding tampering category labels; introducing a cross-encoder based on a visual-language model, setting several multilayer perceptron head structures, and designing three contrastive learning methods: noise enhancement, prototype-based, and multi-label tampering classification, to obtain an initial multi-view contrastive learning framework; training the initial multi-view contrastive learning framework based on the training dataset to obtain a trained multi-view contrastive learning framework; and performing a tampering detection task on the image-text pair data to be detected based on the trained multi-view contrastive learning framework. The technical solution of this invention can improve the accuracy and robustness of multi-label classification.

This application belongs to the field of interconnection network reliability and fault diagnosis technology, and discloses a method for intermittent fault diagnosis of low-bandwidth long interconnection networks based on graph attention mechanism. Targeting the hierarchical recursive structure and high connectivity of the network, under the PMC fault diagnosis model, a multi-round testing strategy is used to obtain test symptoms within the node's neighborhood. A feature vector is constructed for each node using local statistical feature extraction methods, and preprocessed using zero-padding and noise enhancement techniques. Finally, a graph attention network model is constructed, and the importance weights of neighboring node test results are dynamically learned using the attention mechanism to achieve accurate diagnosis of node fault states in the network. This application leverages the powerful local information aggregation capability of graph attention networks to overcome the diagnostic limitations of traditional algorithms, maintaining high diagnostic accuracy and robustness even with high fault rates, incomplete test symptoms, and large network sizes.

The application provides a kind of electrically conductive sponge electromagnetic leakage dynamic monitoring method and system, by utilizing the leakage feature recognition degree of auxiliary noise enhancement based on spectrum attenuation coefficient, and utilizing the set threshold value, a plurality of IMF components are obtained by ensemble empirical mode decomposition, for each IMF component, determine the dominant frequency point and phase retention band;While the phase is retained in the retention band, the out-of-band phase is randomized, the proxy data set is constructed and the confidence interval is established, so as to screen out the leakage feature component, by extracting the weighted hilbert energy with the energy proportion in the phase retention band as the weight, and the multiscale sample entropy with the total bandwidth as the scale factor, a joint feature vector is constructed, and the leakage is judged based on the mahalanobis distance of the vector and the normal state.

The application provides a spoken English recognition method and system based on contrast learning and mixed attention, which comprises the following steps: acquiring audio of a spoken English test, adding noise of a random category to environmental recording to realize noise enhancement and further construct a positive sample; performing feature extraction on the data added with noise based on multi-scale and mixed attention; inputting the features after embedding and position coding into an encoder to perform context modeling; inputting the output of the encoder and the target features after embedding and position coding into a decoder to complete decoding; in the training process, calculating a contrast loss through input of the positive sample, calculating the loss of each sample at the same time, performing reverse transmission, and obtaining a recognition model; and inputting the audio of a candidate to be transcribed into the recognition model to obtain a recognition result.

The application belongs to the technical field of interconnection network reliability and fault diagnosis, and discloses a low-wrapping-length interconnection network intermittent fault diagnosis method based on a graph attention mechanism. Aiming at the hierarchical recursive structure characteristics and high connectivity characteristics of the network, under the PMC fault diagnosis model, the test signs in the node neighborhood are obtained through a multi-round test strategy. A local statistical feature extraction method is used to construct a feature vector for each node, and a zero padding and noise enhancement technology is used for pretreatment. Finally, a graph attention network model is constructed, the importance weight of the neighbor node test result is dynamically learned by using the attention mechanism, and the accurate diagnosis of the node fault state in the network is realized. The application uses the powerful local information aggregation capability of the graph attention network, breaks through the diagnosis degree limitation of the traditional algorithm, and can still maintain high diagnosis accuracy and robustness under the condition of high fault rate, incomplete test signs and large network scale.

The purpose of this disclosure is to provide a speech training noise enhancement system and method based on a hybrid noise generation model, comprising: an input module, a noise environment enhancement module, a speech noise enhancement module, and an output module; wherein, the input module is used to acquire simplified description information of the noise environment and clean speech data to be enhanced; the noise environment enhancement module converts the simplified description information of the noise environment into a structured noise event sequence with temporal features; the speech noise enhancement module generates multi-source hybrid noise according to the noise event sequence and adds the multi-source hybrid noise to the clean speech data according to preset rules to obtain noisy speech data; the output module is used to output the noisy speech data for use in speech model training. This disclosure fully integrates the interactive features of superposition, cancellation, and interference of different noise sources in the time dimension, solving the technical bottleneck that traditional mixing methods can only achieve linear superposition.