Morse code automatic identification method based on Bi-LSTM neural network

Abstract

The invention relates to the technical field of communication signal processing, in particular to a Morse code automatic identification method based on a Bi-LSTM neural network. The method comprises the following steps: S1, constructing a convolutional neural network and a Bi-LSTM neural network, and performing sequence modeling in combination with the Bi-LSTM neural network and the convolutionalneural network to generate a multi-mode LSTM model; S2, training the multi-mode LSTM model in a joint training mode, and performing joint optimization on parameters of the Bi-LSTM neural network and the convolutional neural network; S3, acquiring a Morse code audio signal, and preprocessing the Morse code audio signal to obtain a preprocessed audio signal; S4, analyzing and converting the preprocessed audio signal to generate a frequency spectrum image of the audio signal; S5, inputting the frequency spectrum image into a multi-mode LSTM model, and outputting a probability vector result; and S6, judging the content of the Morse code by utilizing a probability vector result. According to the method, the deep neural network model based on Bi-LSTM can be utilized to efficiently and accuratelycomplete automatic identification of the Morse code.

Description

technical field [0001] The invention relates to the technical field of communication signal processing, in particular to an automatic recognition method of Morse code based on a Bi-LSTM neural network. Background technique [0002] Telegraph communication in the world generally adopts the Morse code method, and manual copying is used in the process of copying and receiving messages. There is no machine or equipment that can replace manual copying and receiving of messages. As an international telegraph communication symbol, Morse code uses various combinations of signals of different lengths to represent certain letters, numbers and punctuation marks. When writing, a short signal is generally represented by a dot ".", and a long signal is represented by a dash "-". In order to distinguish dots and dashes and to distinguish each character, there are strict regulations on the length of dots and dashes and various intervals: 1 dash is equal to the length of 3 dots without inte...

AI Technical Summary

Problems solved by technology

It can be seen from this that there are three major defects in the manual copying work method: 1. The operator's working hours are short. Generally, the manual operator has to rest after working for two hours, and cannot perform continuous copying work for a long time; 2. The operator The labor intensity is high, and the telegraph operator must have super brain and manual skills, receive and grasp the sound signal very accurately, and correct the few errors in the process of sending the machine to get a text file as the result of the message. ; 3. The operator training period is long, which is not conducive to rapid and large-scale replication

[0004] In recent years, with the popularity and popularization of artificial intelligence and deep learning, a large number of tasks that require manual repetitive labor have been replaced by artificial intelligence and deep learning technology, but the recognition of Morse code still relies on experienced operators To complete, there is no effective technical means that can use artificial intelligence and deep learning technology to complete the automatic recognition of Morse code

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