On The Recognition And Evaluation Of The Piano Performing

Music recognition is a rising interdisciplinary research field, which involvesphysics, signal processing, human-computer interaction, music theory and musicpsychics. The goal of this recognition is to extract the music characters by audiosignal processing and feature selection so that they can be used in music analysis,classification and automatic score recording. To describe piano music characters bycomputer, this dissertation combines the technique of multimedia, signal processingand pattern recognition with music theory to make the computer to imitate the pianoperformance evaluation of human.The work in this paper belongs to the research field of music identificationtheory and its application. Employing audio signal analysis and processing, we makeit possible for computer to recognize music characters and evaluate its performance.We first outline the conception and framework of automatic piano performanceevaluation system based on music recognition theory. The evaluation system is thenimplemented with performance testing in this paper. Based on music theory and thenotes physical attributes, we propose seven music features for identification. Amongthese seven features, we focus on pitch, duration and tonality characters detection.Experiments are conducted for comparisons among time-domain parallel processing,harmonics peak value method and wavelet transform. The paper advances the pitchdetection algorithm and for the first time illustrates the time-event figure to explainthe result of pitch and duration features recognition. Experimental results show thatthe advanced STFT pitch detection method achieves high recognition ratio. The paperfor the first time implements the computer detection of the music piece tonality,integrating the property of each note with the style of the whole piece. Based onmusic feature selection, the system is able to evaluate the piano playing performanceby scores and visualization. The automatic piano performance evaluation system wellsynthesizes the knowledge in both music and technology areas, making it possible forcomputers to evaluate music performance automatically. Intelligent music recognitionwill help the music performance learning. The experiment shows that the systemfulfills the anticipated requirements, therefore establishes solid foundation for thefuture work.