539 research outputs found
Analysis of analysis: importance of different musical parameters for Schenkerian analysis
While criteria for Schenkerian analysis have been much discussed, such discussions have generally not been informed by data. Kirlin [Kirlin, Phillip B., 2014 “A Probabilistic Model of Hierarchical Music Analysis.” Ph.D. thesis, University of Massachusetts Amherst] has begun to fill this vacuum with a corpus of textbook Schenkerian analyses encoded using data structures suggested byYust [Yust, Jason, 2006 “Formal Models of Prolongation.” Ph.D. thesis, University of Washington] and a machine learning algorithm based on this dataset that can produce analyses with a reasonable degree of accuracy. In this work, we examine what musical features (scale degree, harmony, metrical weight) are most significant in the performance of Kirlin's algorithm.Accepted manuscrip
The Convergence of Schenkerian Music Theory and Generative Linguistics: An Analysis and Composition
This thesis engages a purported connection between Schenkerian music theory and the Minimalist Program within generative linguistics both scientifically and creatively. The first chapter expounds the link between Schenkerian theory and the Minimalist Program which has been recently substantiated in a doctoral dissertation by Somangshu Mukherji at Princeton University and details the methodological framework for investigating musical structures within this paradigm. Chapter two presents three case studies including the opening phrase of Mozart’s K. 332 Mvt. 1 piano sonata, and the tunes “Georgia on My Mind” and “Blue Bossa” in order to exemplify the aforementioned methodology and provide scientific evidence affirming this generative framework. Chapter three concludes with a creative investigation of the theoretical ideas which this thesis engages and consists of a string quartet that draws upon the notions of music and language, and music as derived from a computational system
Schenkerian Analysis for the Beginner
In the classroom, the teacher of Schenkerian analysis faces the challenge of reconciling the holistic evaluation of works with the sequential presentation of content from simple to complex. Drawing from previous learning taxonomies (Bloom 1956, Anderson and Krathwohl 2001, and Rifkin and Stoecker 2011), I propose an adapted one for Schenkerian analysis. I note differences between this taxonomy and analytical procedures shown in current Schenkerian textbooks (Cadwallader and Gagné 2011, Pankhurst 2008), pursue implications of the new learning taxonomy, and suggest a wide range of classroom activities that have proven effective in my own introductory course. Goals of the new taxonomy include the rapid building of students’ graphing competency, and motivating students to use Schenkerian analysis on their own outside of the classroom
Analysing symbolic music with probabilistic grammars
Recent developments in computational linguistics offer ways to approach the analysis of musical structure by inducing probabilistic models (in the form of grammars) over a corpus of music. These can produce idiomatic sentences from a probabilistic model of the musical language and thus offer explanations of the musical structures they model. This chapter surveys historical and current work in musical analysis using grammars, based on computational linguistic approaches. We outline the theory of probabilistic grammars and illustrate their implementation in Prolog using PRISM. Our experiments on learning the probabilities for simple grammars from pitch sequences in two kinds of symbolic musical corpora are summarized. The results support our claim that probabilistic grammars are a promising framework for computational music analysis, but also indicate that further work is required to establish their superiority over Markov 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
Recommended from our members
An approach to melodic segmentation and classification based on filtering with the Haar wavelet
We present a novel method of classification and segmentation of melodies in symbolic representation. The method is based on filtering pitch as a signal over time with the Haar-wavelet, and we evaluate it on two tasks. The filtered signal corresponds to a single-scale signal ws from the continuous Haar wavelet transform. The melodies are first segmented using local maxima or zero-crossings of ws. The
segments of ws are then classified using the k–nearest neighbour algorithm with Euclidian and city-block distances. The method proves more effective than using unfiltered pitch signals and Gestalt-based segmentation when used to recognize the parent works of segments from Bach’s Two-Part Inventions (BWV 772–786). When used to classify 360 Dutch folk tunes into 26 tune families, the performance of the
method is comparable to the use of pitch signals, but not as good as that of string-matching methods based on multiple features
Music analysis by computer:ontology and epistemology
This chapter examines questions of what is to be analysed in computational music analysis, what is to be produced, and how one can have confidence in the results. These are not new issues for music analysis, but their consequences are here considered explicitly from the perspective of computational analysis. Music analysis without computers is able to operate with multiple or even indistinct conceptions of the material to be analysed because it can use multiple references whose meanings shift from context to context. Computational analysis, by contrast, must operate with definite inputs and produce definite outputs. Computational analysts must therefore face the issues of error and approximation explicitly. While computational analysis must retain contact with the music analysis as it is generally practised, I argue that the most promising approach for the development of computational analysis is not systems to mimic human analysis, but instead systems to answer specific music-analytical questions. The chapter concludes with several consequent recommendations for future directions in computational music analysis
- …