A standard format proposal for hierarchical analyses and representations

In the realm of digital musicology, standardizations efforts to date have mostly concentrated on the representation of music. Analyses of music are increasingly being generated or communicated by digital means. We demonstrate that the same arguments for the desirability of standardization in the representation of music apply also to the representation of analyses of music: proper preservation, sharing of data, and facilitation of digital processing. We concentrate here on analyses which can be described as hierarchical and show that this covers a broad range of existing analytical formats. We propose an extension of MEI (Music Encoding Initiative) to allow the encoding of analyses unambiguously associated with and aligned to a representation of the music analysed, making use of existing mechanisms within MEI's parent TEI (Text Encoding Initiative) for the representation of trees and graphs

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

Graph based representation of the music symbolic level. A music information retrieval application

In this work, a new music symbolic level representation system is described. It has been tested in two information retrieval tasks concerning similarity between segments of music and genre detection of a given segment. It could include both harmonic and contrapuntal informations. Moreover, a new large dataset consisting of more than 5000 leadsheets is presented, with meta informations taken from different web databases, including author information, year of first performance, lyrics, genre, etc.ope

The expressive function in wor songs

[Abstract] We study some musical and expressive features of traditional Wor vocal music, an ancestral gender of the Biaks (Indonesia). A core aspect in Wor songs is the expression of wonder, which Biaks have developed into an Aesthetics of Surprise [1, 2]. We describe some key structural features in the pitch and time domain used as means to express such an aesthetics. We represent the acoustic and prosodic features encoding expressive content by means of an Expressive Function which contains expressive indices with internal structure [3, 4]. We propose an augmented expressive score [5] for the transcription of unaccompanied Wor songs.[Resumen] En este trabajo estudiamos la estructura musical y la técnica empleada en el canto no acompañado de la música vocal Wor, un género ancestral de música creado por el pueblo Biak (Indonesia) para celebrar momentos importantes de su historia o de su vida cotidiana. Un aspecto crucial del género Wor es la expresión de sorpresa y maravilla, que el pueblo Biak ha elaborado en una Estética del Asombro. Analizamos algunos de los rasgos cruciales en el dominio de la frecuencia y del tiempo que se emplean para expresar emociones y afectos de maravilla y asombro. Representamos los rasgos acústicos y prosódicos de contenido expresivo mediante una Función Expresiva, que contiene índices expresivos con estructura interna. Proponemos una Partitura Expresiva Aumentada como transcripción de la música vocal Wor cantada a capella

Perception based approach on pattern discovery and organisation of point-set data

The general topic of the thesis is computer aided music analysis on point-set data utilising theories outlined in Timo Laiho’s Analytic-Generative Methodology (AGM) [19]. The topic is in the field of music information retrieval, and is related to previous work on both pattern discovery and computational models of music. The thesis aims to provide analysis results that can be compared to existing studies. AGM introduces two concepts based on perception, sensation and cognitive processing: interval–time complex (IntiC) and musical vectors (muV). These provide a mathematical framework for the analysis of music. IntiC is a value associated with the velocity, or rate of change, between musical notes. Musical vectors are the vector representations of these rates of change. Laiho explains these attributes as meaningful for both music analysis and as tools for music generation. Both of these attributes can be computed from a point-set representation of music data. The concepts in AGM can be viewed as being related to geometric methods for pattern discovery algorithmsof Meredith, Lemström et al.[24] whointroduce afamily of ‘Structure Induction Algorithms’. These algorithms are used to find repeating patterns in multidimensional point-set data. Algorithmic implementations of intiC and muV were made for this thesis and examined in the use of rating and selecting patterns output by the pattern discovery algorithms. In addition software tools for using these concepts of AGM were created. The concepts of AGM and pattern discovery were further related to existing work in computer aided musicology

SCHUBOT: Machine Learning Tools for the Automated Analysis of Schubert’s Lieder

This paper compares various methods for automated musical analysis, applying machine learning techniques to gain insight about the Lieder (art songs) of com- poser Franz Schubert (1797-1828). Known as a rule-breaking, individualistic, and adventurous composer, Schubert produced hundreds of emotionally-charged songs that have challenged music theorists to this day. The algorithms presented in this paper analyze the harmonies, melodies, and texts of these songs. This paper begins with an exploration of the relevant music theory and ma- chine learning algorithms (Chapter 1), alongside a general discussion of the place Schubert holds within the world of music theory. The focus is then turned to automated harmonic analysis and hierarchical decomposition of MusicXML data, presenting new algorithms for phrase-based analysis in the context of past research (Chapter 2). Melodic analysis is then discussed (Chapter 3), using unsupervised clustering methods as a complement to harmonic analyses. This paper then seeks to analyze the texts Schubert chose for his songs in the context of the songs’ relevant musical features (Chapter 4), combining natural language processing with feature extraction to pinpoint trends in Schubert’s career

WuYun: Exploring hierarchical skeleton-guided melody generation using knowledge-enhanced deep learning

Although deep learning has revolutionized music generation, existing methods for structured melody generation follow an end-to-end left-to-right note-by-note generative paradigm and treat each note equally. Here, we present WuYun, a knowledge-enhanced deep learning architecture for improving the structure of generated melodies, which first generates the most structurally important notes to construct a melodic skeleton and subsequently infills it with dynamically decorative notes into a full-fledged melody. Specifically, we use music domain knowledge to extract melodic skeletons and employ sequence learning to reconstruct them, which serve as additional knowledge to provide auxiliary guidance for the melody generation process. We demonstrate that WuYun can generate melodies with better long-term structure and musicality and outperforms other state-of-the-art methods by 0.51 on average on all subjective evaluation metrics. Our study provides a multidisciplinary lens to design melodic hierarchical structures and bridge the gap between data-driven and knowledge-based approaches for numerous music generation tasks

An integrative approach to style analysis of folk dance melodies with classification using inductive learning

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Towards a style-specific basis for computational beat tracking

Outlined in this paper are a number of sources of evidence, from psychological, ethnomusicological and engineering grounds, to suggest that current approaches to computational beat tracking are incomplete. It is contended that the degree to which cultural knowledge, that is, the specifics of style and associated learnt representational schema, underlie the human faculty of beat tracking has been severely underestimated. Difficulties in building general beat tracking solutions, which can provide both period and phase locking across a large corpus of styles, are highlighted. It is probable that no universal beat tracking model exists which does not utilise a switching model to recognise style and context prior to application