Authors:
Hui Liu
;
Tingting Xue
and
Tanja Schultz
Affiliation:
Cognitive Systems Lab, University of Bremen, Germany
Keyword(s):
Pitch Statistics, Pitch Histogram, Merged Pitch Histogram, Pitch-duration Histogram, Pitch-related Features, Music Computing, Music Information Retrieval.
Abstract:
The traditional pitch histogram and various features extracted from it play a pivotal role in music information retrieval. In the research on songs, especially applying pitch statistics to investigate the main melody, we found that the pitch histogram may not necessarily reflect the notes' pitch characteristic of the whole song perfectly. Therefore, we took the note duration into account to propose two advanced versions of pitch histograms and validated their applicability. This paper introduces these two novel histograms: the merged pitch histogram by merging consecutively repeated pitches and the pitch-duration histogram by utilizing each pitch's duration information. Complemented by the description of their calculation algorithms, the discussion of their advantages and limitations, the analysis of their application to songs from various languages and cultures, and the demonstration of their use cases in state-of-the-art research works, the proposed histograms' characteristics and
usefulness are intuitively revealed.
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