A Study of Multi-dimensional Melodic Similarity Model Based on Perceptual Analysis

Perceived predominant melody of music is the most convenient and memorable description and can be used for content-based music retrieval. However, including human voice and multiple musical instruments playing together, it is difficult to extract a predominant contour of pitches directly from MP3 music recordings. In order to build a similarity music melody model, several audio files, whose melody is perceptually similar, are collected in our experiment. Many features are extracted and a melodic similarity model is defined through analyzing each feature and combinations of them. The melody model is evaluated based on classification results of six categories of Chinese folk music using Support Vector Machine. The experiment results show that 36-dimensional Constant Q Transform (CQT) feature can represent the melody of audio music pieces accurately. Further more, the classification results for audio data with similar melody are good enough to be used in audio music classification or segmentation and subsequently are very helpful in music information retrieval system.