Independent component analysis based automobile sound identification method

Abstract

The present invention relates to a vehicle audio identification method based on independent component analysis, and belongs to the audio processing and mode identification technology field. The utility model has the steps that audio frequency spectrums of different vehicles are gotten by a pretreatment of different vehicle sounds, frequency spectrums are trimmed down according to characteristics of the vehicle sounds, and coefficients of frequency domain are changed to log domain, thereby enhancing the robustness of the audio identification of the vehicle. Getting the characteristics of the vehicle sounds by using the independent component analysis adapts to small sample training characteristics of vehicle identification. The samples which are ready for identifying are rebuilt in a characteristic space consisting of independent components, a Euclidean distance between the samples which are ready for identifying and the center of the vehicle is gotten, and the samples which are ready for identifying are classified according to the Euclidean distance. The present invention can identify the vehicle sounds highly efficiently and fleetly, thereby especially adapting to real time computation circumstance. The present invention can be used in a plurality of circumstances like detecting of passing vehicles in military field and intelligent traffic system in civilian field and so on.

Description

technical field [0001] The invention relates to a vehicle sound recognition method based on independent component analysis, which belongs to the technical field of audio processing and pattern recognition. Background technique [0002] In the field of pattern recognition, independent component analysis (Independent Component Analysis, ICA) and principal component analysis (Principal Component Analysis, PCA) have many similarities in processing methods and ideas. Both methods can realize the linear representation of observed data. Both can project data from a high-dimensional space to a more meaningful low-dimensional space to achieve the purpose of dimensionality reduction and redundancy reduction, and perform corresponding processing, such as data structure analysis, data feature extraction, etc. The decomposition based on the PCA principle can only ensure that the decomposed components are uncorrelated, and cannot guarantee that these components are independent of each oth...

AI Technical Summary

Problems solved by technology

[0004] In the recognition of vehicle sounds, due to the influence of the environment on the extracted sound, the speed of the vehicle, and the individual differences in the same type of vehicles, the establishment of a feature library for vehicle sounds is a problem of extracting effective features from a small sample training set.

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