
doi: 10.1049/ccs2.12036
Abstract Recently Chinese music has been regarded as an independent music school. However, the definition of Chinese music has been an abstract and subjective concept. That makes music information retrieval (MIR) tasks hard to perform on Chinese music. Previous musicological studies have explained how musical elements like melody and instruments shape a certain kind of music genre including Chinese music, but the findings cannot be directly applied to relevant MIR tasks towards large‐scale users and real‐world problems. In this study, a pipeline of performing a perceptual survey is designed to explore how different musical elements influence people's perception of ‘Chinese style’ in music. Participants with various backgrounds were presented with categorised music excerpts performed in the Erhu or violin and then gave ‘Chinese style’ ratings. Statistical analysis indicates that music content contributes more than instruments. Results were compared between musicians and non‐musicians. Subsequently, a supplementary automatic music classification experiment is conducted in comparison with the survey results to discuss the authors’ choice of stimuli in the survey and similarities between computer auditory and human perception. In general, the results in this study can be useful for MIR tasks, such as understanding, representation, and recommendation of Chinese music.
TK7885-7895, Computer engineering. Computer hardware, automatic music style classification, music perception, Computer applications to medicine. Medical informatics, music information retrieval, R858-859.7, Chinese music style
TK7885-7895, Computer engineering. Computer hardware, automatic music style classification, music perception, Computer applications to medicine. Medical informatics, music information retrieval, R858-859.7, Chinese music style
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 16 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
