Implementing Methods for Analysing Music Based on Lerdahl and Jackendoff’s Generative Theory of Tonal Music

Hamanaka, Masatoshi; Hirata, Keiji; Tojo, Satoshi

doi:10.1007/978-3-319-25931-4_9

Cited by 10 publications

(7 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Representations based on Schenker's or on Lerdahl and Jackendoff 's theories are studied, since these can capture different levels of structural information. Most works only have the aim of automatic analysis of musical pieces (Marsden, 2010;Marsden et al, 2013;Hamanaka et al, 2016Hamanaka et al, , 2017, but others have used this approach to generate music that follows a defined structure (Groves, 2016;Carnovalini and Rodà, 2019a) Other systems approached the problem of structure without any specific representation. GEDMAS (Anderson et al, 2013) explicitly generates structure, seen as successions of 8-bar segments, through a Markov chain.…”

Section: Structure and Mappingmentioning

confidence: 99%

Section: Open Challenges For Music Generation Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Computational Creativity and Music Generation Systems: An Introduction to the State of the Art

Carnovalini

Rodà

2020

Front. Artif. Intell.

100

View full text Add to dashboard Cite

Computational Creativity is a multidisciplinary field that tries to obtain creative behaviors from computers. One of its most prolific subfields is that of Music Generation (also called Algorithmic Composition or Musical Metacreation), that uses computational means to compose music. Due to the multidisciplinary nature of this research field, it is sometimes hard to define precise goals and to keep track of what problems can be considered solved by state-of-the-art systems and what instead needs further developments. With this survey, we try to give a complete introduction to those who wish to explore Computational Creativity and Music Generation. To do so, we first give a picture of the research on the definition and the evaluation of creativity, both human and computational, needed to understand how computational means can be used to obtain creative behaviors and its importance within Artificial Intelligence studies. We then review the state of the art of Music Generation Systems, by citing examples for all the main approaches to music generation, and by listing the open challenges that were identified by previous reviews on the subject. For each of these challenges, we cite works that have proposed solutions, describing what still needs to be done and some possible directions for further research.

show abstract

Section: Structure and Mappingmentioning

confidence: 99%

Section: Open Challenges For Music Generation Systemsmentioning

confidence: 99%

Computational Creativity and Music Generation Systems: An Introduction to the State of the Art

Carnovalini

Rodà

2020

Front. Artif. Intell.

100

View full text Add to dashboard Cite

show abstract

“…Rafael and Oertl (2010) built a global structure from patterns extracted by the algorithm from Hsu et al (1998). Some studies, such as by Hamanaka et al (2016), have attempted to compute large-scale structures as theorized by Schenker (1935) or later by the Generative Theory of Tonal Music (GTTM) of Lerdahl and Jackendoff (1983). Other works also modeled specific large-scale features, such as tonal tension (Lerdahl and Krumhansl, 2007;Farbood, 2010).…”

Section: Mir High-level Structure and Sonata Formmentioning

confidence: 99%

Learning Sonata Form Structure on Mozart’s String Quartets

Allegraud¹,

Bigo²,

Feisthauer³

et al. 2019

Transactions of the International Society for Music Information Retrieval

View full text Add to dashboard Cite

The musical analysis of large-scale structures, such as the classical sonata form, requires to integrate multiple analyses of local musical events into a global coherent analysis. Modelling large-scale structures is still a challenging task for the research community. It includes building large and accurate annotated corpora, as well as developing practical and efficient tools in order to visualize the analyses of these corpora. It finally requires the conception of effective and properly evaluated MIR algorithms. We propose a machine learning approach for the sonata form structure on 32 movements from Mozart's string quartets. We release an open dataset, encoding two reference analyses of these 32 movements, totaling more than 1800 curated annotations, as well as flexible visualizations of these analyses. We discuss the occurrence in this corpus of melodic, harmonic, and rhythmic features induced by pitches, durations, and rests. We investigate whether the presence or the absence of these features can be characteristic of the different sections forming a sonata form. We then compute the emission and transition probabilities of several Hidden Markov Models intended to match the structure of sonata forms at several resolutions. Our results confirm that the sonata form is better identified when the parameters are learned rather than manually set up. These results open perspectives on the computational analysis of musical forms by mixing human knowledge and machine learning from annotated scores.

show abstract

“…Other studies used more complex graph and tree structures designed for information retrieval purposes [19][20][21], sometimes inspired by musicological theories like Schenkerian theory and Lerdahl and Jackendoff's Generative theory of tonal music [9,15,17].…”

Section: Introductionmentioning

confidence: 99%

Symbolic Music Similarity through a Graph-Based Representation

Simonetta

Carnovalini

Orio

et al. 2018

Proceedings of the Audio Mostly 2018 on Sound in Immersion and Emotion

View full text Add to dashboard Cite

In this work, a novel representation system for symbolic music is described. The proposed representation system is graph-based and could theoretically represent music both from a horizontal (contrapuntal) and from a vertical (harmonic) point of view, by keeping into account contextual and harmonic information. It could also include relationships between internal variations of motifs and themes. This is achieved by gradually simplifying the melodies and generating layers of reductions that include only the most important notes from a structural and harmonic viewpoint. This representation system has been tested in a music information retrieval task, namely melodic similarity, and compared to another system that performs the same task but does not consider any contextual or harmonic information, showing how the structural information is needed in order to find certain relations between musical pieces. Moreover, a new dataset consisting of more than 5000 leadsheets is presented, with additional meta-musical information taken from different web databases, including author, year of first performance, lyrics, genre and stylistic tags. CCS CONCEPTS• Information systems → Music retrieval; • Applied computing → Sound and music computing;

show abstract

Implementing Methods for Analysing Music Based on Lerdahl and Jackendoff’s Generative Theory of Tonal Music

Cited by 10 publications

References 6 publications

Computational Creativity and Music Generation Systems: An Introduction to the State of the Art

Computational Creativity and Music Generation Systems: An Introduction to the State of the Art

Learning Sonata Form Structure on Mozart’s String Quartets

Symbolic Music Similarity through a Graph-Based Representation

Contact Info

Product

Resources

About