Computational Models of Expressive Music Performance: A Comprehensive and Critical Review

Expressive performance is an indispensable part of music making. When playing a piece, expert performers shape various parameters (tempo, timing, dynamics, intonation, articulation, etc.) in ways that are not prescribed by the notated score, in this way producing an expressive rendition that brings out dramatic, affective, and emotional qualities that may engage and affect the listeners. Given the central importance of this skill for many kinds of music, expressive performance has become an important research topic for disciplines like musicology, music psychology, etc. This paper focuses on a specific thread of research: work on computational music performance models. Computational models are attempts at codifying hypotheses about expressive performance in terms of mathematical formulas or computer programs, so that they can be evaluated in systematic and quantitative ways. Such models can serve at least two purposes: they permit us to systematically study certain hypotheses regarding performance; and they can be used as tools to generate automated or semi-automated performances, in artistic or educational contexts. The present article presents an up-to-date overview of the state of the art in this domain. We explore recent trends in the field, such as a strong focus on data-driven (machine learning) approaches; a growing interest in interactive expressive systems, such as conductor simulators and automatic accompaniment systems; and an increased interest in exploring cognitively plausible features and models. We provide an in-depth discussion of several important design choices in such computer models, and discuss a crucial (and still largely unsolved) problem that is hindering systematic progress: the question of how to evaluate such models in scientifically and musically meaningful ways. From all this, we finally derive some research directions that should be pursued with priority, in order to advance the field and our understanding of expressive music performance

Composer Recognition based on 2D-Filtered Piano-Rolls

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Convolution-based classification of audio and symbolic representations of music

We present a novel convolution-based method for classification of audio and symbolic representations of music, which we apply to classification of music by style. Pieces of music are first sampled to pitch–time representations (piano-rolls or spectrograms) and then convolved with a Gaussian filter, before being classified by a support vector machine or by k-nearest neighbours in an ensemble of classifiers. On the well-studied task of discriminating between string quartet movements by Haydn and Mozart, we obtain accuracies that equal the state of the art on two data-sets. However, in multi-class composer identification, methods specialised for classifying symbolic representations of music are more effective. We also performed experiments on symbolic representations, synthetic audio and two different recordings of The Well-Tempered Clavier by J. S. Bach to study the method’s capacity to distinguish preludes from fugues. Our experimental results show that our approach performs similarly on symbolic representations, synthetic audio and audio recordings, setting our method apart from most previous studies that have been designed for use with either audio or symbolic data, but not both

Symbolic Music Representations for Classification Tasks: A Systematic Evaluation

Music Information Retrieval (MIR) has seen a recent surge in deep learning-based approaches, which often involve encoding symbolic music (i.e., music represented in terms of discrete note events) in an image-like or language like fashion. However, symbolic music is neither an image nor a sentence, and research in the symbolic domain lacks a comprehensive overview of the different available representations. In this paper, we investigate matrix (piano roll), sequence, and graph representations and their corresponding neural architectures, in combination with symbolic scores and performances on three piece-level classification tasks. We also introduce a novel graph representation for symbolic performances and explore the capability of graph representations in global classification tasks. Our systematic evaluation shows advantages and limitations of each input representation. Our results suggest that the graph representation, as the newest and least explored among the three approaches, exhibits promising performance, while being more light-weight in training