Noise classifying method based on FPGA accelerated PCNN algorithm

Abstract

The invention discloses a noise classifying method based on an FPGA accelerated PCNN algorithm. The noise classifying method comprises the following steps: 1, acquiring a noise sample by using recording equipment, and editing into an audio file; 2, performing time-frequency conversion on the audio file; 3, performing feature extraction: converting a noise spectrum map into a grayscale map, using agray value as input of a PCNN model, accelerating the iterative process of the PCNN algorithm through an FPGA, and outputting time sequences as extracted features of different classes of noises; 4, iterating each class of noises in the noise sample for 50-200 times through processing in the step 3, outputting the time sequences, and then dividing into a training set and a testing set; 5, averaging the time sequences during each iteration of the training set, using the training set as a reference template, calculating the Euclidean distance between the time sequence of the testing set and thetime sequence of the reference template, determining as the same class of noises when the Euclidean distance is smaller than the threshold of a noise class, and outputting a recognition result. By thenoise classifying method, the feature extracting time is shortened and the time cost is saved.

Description

technical field [0001] The invention relates to the technical field of noise classification, in particular to a noise classification method based on FPGA-accelerated PCNN algorithm. Background technique [0002] The problem of noise pollution has brought serious harm to human society and even the entire ecological environment. But the nature of noise pollution is very special, invisible and intangible, no pollutants will be produced, the distribution of pollution sources is wide, there are many types, and it is real-time. Noise pollution can cause harm to people, animals, instruments and buildings. Noise can not only damage hearing, but also induce a variety of cancer-causing and fatal diseases. However, China has a dense population and a complex living environment, and ordinary residents have a weak awareness of the hazards of noise pollution. In today's era, noise pollution has already had a serious impact on the people. The classification of urban noise has been insepar...

AI Technical Summary

Problems solved by technology

However, the time cost of the above methods is relatively large, mainly because the iterative process of the PCNN network is relatively time-consuming, and according to the existing literature, there is almost no hardware implementation of the classifier

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