5results about How to "Reduce network parameters" patented technology

The application discloses a speech separation method and device based on a multi-channel full convolution time domain network, comprising the following steps: (1) obtaining a plurality of noisy mixed multi-channel speech signals containing different sound sources, noise and reverberation, and taking corresponding pure single-channel speech signals as labels to form a training data set; (2) establishing a multi-channel full convolution time domain network, wherein the multi-channel full convolution time domain network comprises an encoder, a separator, a point multiplication module and a decoder, and the parameters of the encoder are fixed Gammatone filter coefficients; (3) inputting the training data set into the multi-channel full convolution time domain network for training; and (4) inputting a noisy mixed multi-channel speech signal to be separated into the multi-channel full convolution time domain network to obtain a pure single-channel speech signal after sound source separation. The application has better separation effect.

The application discloses a pyramid type time convolution network structure and a diagnosis method for real-time bearing fault diagnosis, relates to the technical field of mechanical part fault diagnosis, and is composed of four parts of data input, network segment 1, network segment 2 and result output. The data input part is used for preprocessing original data and transmitting the original data to the network segment 1, the network training is performed through the connection mode of neurons in the network segment 1, and the output prediction sequence is transmitted to the network segment 2. The network segment 2 classifies the prediction sequence to obtain different fault types and transmits the fault types to the result output part. The result output part is used for training and diagnosis, and finally outputs a diagnosis result. The pyramid type time convolution neural network structure and the diagnosis method can fuse different sensor data, have small network parameters, require less memory resources during network training, and can perform real-time fault diagnosis.

This invention relates to noise reduction methods, and more particularly to a lightweight multi-axis Transformer single-channel noise reduction method. It can fully extract the latent time-frequency features of speech signals. A multi-head dynamic local self-attention module is employed to efficiently extract local features. The proposed method has fewer network parameters and lower computational cost, while remaining competitive with state-of-the-art methods in terms of speech signal quality and intelligibility. It can fully extract the latent time-frequency features of speech signals, effectively reducing computer resource consumption. Hint blocks are used to help the model better learn frequency feature information.

The present application relates to the technical field of algorithm identification, in particular to a sheep group identity recognition method based on facial features, comprising constructing a sheep face original data set after de-redundancy and data enhancement, realizing sheep face area and three facial key point detection by using an improved RetinaFace network, extracting a 128-dimensional normalized sheep face feature vector through a HTL-Net network fused with TripletAttention, constructing a feature database, developing an intelligent platform integrating detection and recognition, and completing identity matching based on Euclidean distance. The method solves the problems of traditional ear tags, RFID and other recognition methods, such as easy to fall off, high cost, harm to sheep, low precision of existing recognition models, and difficult deployment, and has a sheep face detection accuracy of 96.15%, a recognition accuracy of 91.90%, a lightweight model, supports multi-form real-time recognition, and is suitable for large-scale sheep group precision management requirements.

An image segmentation method based on an improved U2-Net network includes the following steps: 1) preprocessing the image; 2) performing adaptive histogram smoothing on the image; 3) data augmentation; 4) obtaining output 1; 5) obtaining output 2; 6) obtaining output 3; 7) obtaining output 4; 8) obtaining output 5; 9) obtaining output 6, a significant probability map 6; 10) obtaining output 7, a significant probability map 5; 11) obtaining output 8, a significant probability map 4; 12) obtaining output 9, a significant probability map 3; 13) obtaining output 10, a significant probability map 2; 14) obtaining a significant probability map 1; 15) mapping significant probability maps 1 to 6 to pixel values ​​between 0 and 1 after passing them through a convolutional layer and a sigmoid activation function, representing the probability that each pixel belongs to the foreground (i.e., an object), thus obtaining the segmentation map. This method reduces the manpower and material resources required for manual processing, reduces computational load, and improves segmentation accuracy.