Generation of folk song melodies using Bayes transforms

The paper introduces the `Bayes transform', a mathematical procedure for putting data into a hierarchical representation. Applicable to any type of data, the procedure yields interesting results when applied to sequences. In this case, the representation obtained implicitly models the repetition hierarchy of the source. There are then natural applications to music. Derivation of Bayes transforms can be the means of determining the repetition hierarchy of note sequences (melodies) in an empirical and domain-general way. The paper investigates application of this approach to Folk Song, examining the results that can be obtained by treating such transforms as generative models

Computer Aided Statistical Analysis of Motive Use and Compositional Idiom

This thesis discusses the creation of a means of pitch-based data representation which allows automated logging and analysis of melodic motivic material. This system also allows analysis of a number of attributes of a composition which are not readily apparent to human analysis. By using a numerical data format which treats motivically related material as equivalent, groups of tonally equivalent intervals (n-tuples) can be logged and have statistical procedures carried out on them. This thesis looks at four applications of this approach: measuring the most commonly occurring motivic material; creating a transition matrix showing probabilities of movement between intervals; measuring the extent of disjunct or conjunct writing; and measuring concentration of motivic writing (the extent to which motives are reused). Following the discussion of the data representation system, a set of expositions taken from the piano sonatas of Haydn, Mozart, and Clementi are converted to this method of data representation, and results are collected for the above four applications. The implications of the results of this analysis are discussed, and further potential applications of the system are explored

Computer Aided Statistical Analysis of Motive Use and Compositional Idiom

This thesis discusses the creation of a means of pitch-based data representation which allows automated logging and analysis of melodic motivic material. This system also allows analysis of a number of attributes of a composition which are not readily apparent to human analysis. By using a numerical data format which treats motivically related material as equivalent, groups of tonally equivalent intervals (n-tuples) can be logged and have statistical procedures carried out on them. This thesis looks at four applications of this approach: measuring the most commonly occurring motivic material; creating a transition matrix showing probabilities of movement between intervals; measuring the extent of disjunct or conjunct writing; and measuring concentration of motivic writing (the extent to which motives are reused). Following the discussion of the data representation system, a set of expositions taken from the piano sonatas of Haydn, Mozart, and Clementi are converted to this method of data representation, and results are collected for the above four applications. The implications of the results of this analysis are discussed, and further potential applications of the system are explored

日本民謡の大規模音楽コーパスを用いた旋律の構造抽出

国立国語研究所 コーパス開発センター 非常勤研究員Adjunct Researcher, Center for Corpus Studies, NINJAL本論文では,(1)日本民謡の音楽的特徴--旋律に内在する法則--を科学的に捉え,(2)抽出した特徴に基づき日本民謡の地域性を客観的に判断する指標を示す。はじめに『日本民謡大観』(1944-1993)に収録されている種目のうち全国的に網羅的に存在する日本民謡1,794曲と,Web上に公開されている大規模音楽データベースに収録されている中国民謡1,984曲から,それぞれ音楽コーパスを構築する。日本民謡の比較対象として,中国民謡を用いる理由は,中国音楽が日本音楽の形成に多大な影響を与えてきたにもかかわらず,音楽文化や旋律のもつ雰囲気などの点で違いが見受けられるためである。分析の手順としては,各コーパスに対してVLMCモデルを用い,旋律中に繰り返し出現する音程推移パターンを抽出する。そして地域・種目などの背景要因に応じて日本民謡の楽曲データをグループに分割し,計量的な手法を用いてその特徴を比較検討する。その結果,日本音楽の特徴は,民俗学や歴史学において提唱されている日本列島の東西二分論(社会組織論)のほか,方言研究やアクセントの分布図とも一致することがわかった。In this study, we focus on the melodies of endangered Japanese folk songs, which were created by anonymous nonprofessional musicians or poets, and have been orally handed down from ancient times. The goal of the study is (1) to extract the basic structures from both traditional Japanese and Chinese folk songs that represent the characteristics found within the vast majority of the music corpora, and (2) to form regional classifications based on transition probabilities of the extracted musical patterns. Chinese folk songs were chosen as a target for comparison to Japanese folk songs because, despite the considerable influence of Chinese culture on Japan, the musical characteristics of both countries differ in many aspects. Specifically, we sample 1,794 works from Nihon Min-yo Taikan (Anthology of Japanese Folk Songs, 1944-1993) to investigate Japanese folk songs and 1,984 folk songs from a website providing virtual musical scores for Chinese folk songs. With this data, we probabilistically created a tree structure modeling a variable length Markov chain (VLMC) in order to compare minimum transition patterns occurring with high probabilities in terms of pitch intervals. As a result, we successively classify the regions into two basic groups, Eastern and Western Japan, which correspond to geographical factors and cultural backgrounds, and also matches accent distributions observed in the Japanese language

The Construction and Evaluation of Statistical Models of Melody and Harmony

This research is concerned with the development of representational and modelling techniques employed in the creation of statistical models of melody and four-part harmony. Previous work has demonstrated the utility of multiple viewpoint systems, along with techniques such as Prediction by Partial Match, in the construction of cognitive models of melodic perception. Primitive viewpoints represent surface and underlying musical attributes, while linked viewpoints model combinations of such attributes. A viewpoint selection algorithm optimises multiple viewpoint systems by minimising the information theoretic measure cross-entropy. Many more linked viewpoints are used in this research than have previously been available, and the results show that many new viewpoints are incorporated into optimised systems. A significant aspect of this work is the proposal and implementation of a set of novel extensions of the multiple viewpoint framework for four-part harmony. Statistical models are constructed with the aim that given a soprano part, alto, tenor and bass parts are added in a stylistically suitable way. Version 1 is as closely related to the modelling of melody as possible (chord replacing note), and is a baseline for gauging expected improvements as the framework is extended and generalised. Three versions of the framework have been implemented, and their performances compared and contrasted. The results indicate that the baseline version has been improved upon. Time complexity issues are discussed in detail, and selected viewpoints are examined from a music theoretic point of view for insights into why they perform well. Finally, melodies and harmonisations of given melodies are generated using the best performing models. The quality of the music suggests that, in spite of the improvements achieved so far, the models are still unable to fully capture the musical style of a corpus. Another six versions of the framework are described, which are expected to contribute further improvements