Abstract
In order to study the pitch evaluation of Matouqin chamber music performance based on artificial neural network, this paper puts forward the relevant theories in the fields of human ear auditory perception system, auditory psychology, music theory knowledge, and pattern recognition. This paper extracts the auditory image features of chords and then establishes a sparse representation classifier model for chord recognition and classification. Scale-invariant feature transformation (SIFT) and spatial pyramid matching (SPM) are used to extract the detailed features of chord auditory images. The experimental results show that the highest correct recognition rate of the chord recognition algorithm based on the auditory image proposed in this paper is 76.2%, which is 20.4% higher than that of MFCC feature based on human auditory characteristics.
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