Running up Blueberry Hill: Prototyping whole body interaction in harmony space

Musical harmony is considered to be one of the most abstract and technically difficult parts of music. It is generally taught formally via abstract, domain-specific concepts, principles, rules and heuristics. By contrast, when harmony is represented using an existing interactive desktop tool, Harmony Space, a new, parsimonious, but equivalently expressive, unified level of description emerges. This focuses not on abstract concepts, but on concrete locations, objects, areas and trajectories. This paper presents a design study of a prototype version of Harmony Space driven by whole body navigation, and characterizes the new opportunities presented for the principled manipulation of chord sequences and bass lines. These include: deeper engagement and directness; rich physical cues for memory and reflection, embodied engagement with rhythmic time constraints; hands which are free for other simultaneous activities (such as playing a traditional instrument); and qualitatively new possibilities for collaborative use

A Functional Taxonomy of Music Generation Systems

Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with reference to existing systems. The taxonomy organizes systems according to the purposes for which they were designed. It also reveals the inter-relatedness amongst the systems. This design-centered approach contrasts with predominant methods-based surveys and facilitates the identification of grand challenges to set the stage for new breakthroughs.Comment: survey, music generation, taxonomy, functional survey, survey, automatic composition, algorithmic compositio

Harmonic Change Detection from Musical Audio

In this dissertation, we advance an enhanced method for computing Harte et al.’s [31] Harmonic Change Detection Function (HCDF). HCDF aims to detect harmonic transitions in musical audio signals. HCDF is crucial both for the chord recognition in Music Information Retrieval (MIR) and a wide range of creative applications. In light of recent advances in harmonic description and transformation, we depart from the original architecture of Harte et al.’s HCDF, to revisit each one of its component blocks, which are evaluated using an exhaustive grid search aimed to identify optimal parameters across four large style-specific musical datasets. Our results show that the newly proposed methods and parameter optimization improve the detection of harmonic changes, by 5.57% (f-score) with respect to previous methods. Furthermore, while guaranteeing recall values at > 99%, our method improves precision by 6.28%. Aiming to leverage novel strategies for real-time harmonic-content audio processing, the optimized HCDF is made available for Javascript and the MAX and Pure Data multimedia programming environments. Moreover, all the data as well as the Python code used to generate them, are made available.<br /

Analysis and synthesis of musical harmonies based on formal grammars

Music is one of the most fundamental and universal forms of expression of humanity, being part of the human society since its origins. As any artistic manifestation, it is a cultural product and its evolution and use in all kind of scenarios show us how important it is for the humanity. The relation between music and mathematics has been a close one, investigated since the Pythagoreans, and one of the main products of this relation is tonal music, considered one of the greatest contributions of western civilization. So not only is music one of the forms of expression that most directly speak to the emotions, it is also the one whose foundations are more firmly mathematical, making music an ideal testing ground for analyzing the formalization of emotionally charged forms of expression, and to explore the limits of such formalization. Music is not subject to simple deterministic rules, but there is an obvious structure underneath it. Investigating and formalizing such structure is important to better understand music and even to develop new ways of composition using computers as a synergistic partner or as an independent composer. Thus, in this work, we consider the usefulness and the limits of applicability of formal grammars. The use of grammars can give important insights into the harmonic structure of a piece of music, and a lot of researchers have developed grammars and applied them to different styles of music. In musical theory there are harmonic concepts that are not well-defined in a mathematical sense. In this work we considered several of these concepts and see how, and whether, grammars can be modified to accommodate them. However, the ambiguous nature of music limits the possibility of formal analysis of harmonic sequences: using the grammars we could not precisely define (and, consequently, recognize) some musical concepts, such as modulation (the change of key within a musical piece). Trying to formalize modulation leads to unavoidable ambiguities in the grammar. Consequently we tried using other methods to formalize these concepts, and we successfully could determine modulations using a numerical costbased method. Then we used grammars to analyze separately the non-modulating segments in which our algorithm has divided the music. The combination of different methods and technologies developed in this work appears to be a very bright perspective for analyzing musical pieces

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

Interface Design for Empowerment: a Case Study from Music

It is very seldom that psychological theory is applied to human - computer interface design — because very few theories have yet been formulated which are applicable. For the most part designers have to be content to use guidelines and models, which have less applicability. So, the work described in this chapter is unusual, because it describes an interface to a program which teaches about musical harmony, based on psychological theories. The success of that approach is borne out by the fact that the theories suggest the use of a specific style of interface, based on a two-dimensional spatial representation of harmony relationships. This in turn has been shown to be very successful in teaching novice users about harmony

Automatic musical key detection

Selles töös oleme pakkunud mudeli tonaalsuse avastamiseks, mis on võimeline tegelema muusikaga erinevatest muusikalisest traditsioonedest ilma, et nende põhjalik analüüs oleks nõutud. Meie mudel põhineb eeldusel, et enamik muusikalisi traditsioone kasutavad hieraarhia kehtestaniseks helide kestust. Oleme pakkunud algoritmi automaatseks helilaadi avastamiseks. Meetod oli hinnatud nii sümboolse kui ka audio andmestiku peal.In this thesis we have proposed a model for tonality estimation, which is capable of handling music coming from various musical traditions and does not require their thorough analysis. In our model we have employed an assumption, that most musical traditions use duration to maintain pitch salience. Proceeding from this assumption, we have proposed an algorithm for automatic key detection, based on a distributional approach. The proposed method was evaluated on both symbolic and acoustic datasets