Portfolio of musical compositions

INTRODUCTORY NOTE ON PERFORMANCE OF THE MUSIC HERE INCLUDED: This portfolio contains three works. The first, better now, is realised electronically; it has no score, and the six-channel version included herewith is the definitive performance source. A rendition in 5.1 surround is also included for referential convenience. This should not be used in public performance.The second and third works, Four Quartets and not even the rain, respectively, are scored for instrumental ensemble and voice and are intended to be performed as such. The recordings of these two pieces herewith included are synthetic, MIDI-based performances, which are included for the assistance of the score -reader only, because the timbral structure of the music is very important. These recordings should never be used for public performance

An approach for identifying salient repetition in multidimensional representations of polyphonic music

SIATEC is an algorithm for discovering patterns in multidimensional datasets (Meredith et al., 2002). This algorithm has been shown to be particularly useful for analysing musical works. However, in raw form, the results generated by SIATEC are large and difficult to interpret. We propose an approach, based on the generation of set-covers, which aims to identify particularly salient patterns that may be of musicological interest. Our method is capable of identifying principal musical themes in Bach Two-Part Inventions, and is able to offer a human analyst interesting insight into the structure of a musical work

Learning and Consolidation as Re-representation: Revising the Meaning of Memory

In this Hypothesis and Theory paper, we consider the problem of learning deeply structured knowledge representations in the absence of predefined ontologies, and in the context of long-term learning. In particular, we consider this process as a sequence of re-representation steps, of various kinds. The Information Dynamics of Thinking theory (IDyOT) admits such learning, and provides a hypothetical mechanism for the human-like construction of hierarchical memory, with the provision of symbols constructed by the system that embodies the theory. The combination of long-term learning and meaning construction in terms of symbols grounded in perceptual experience entails that the system, like a human, be capable of memory consolidation, to manage the complex and inconsistent structures that can result from learning of information that becomes more complete over time. Such consolidation changes memory structures, and thus changes their meaning. Therefore, memory consolidation entails re-representation, while re-representation entails changes of meaning. Ultimately, the theory proposes that the processes of learning and consolidation should be considered as repeated re-representation of what is learned

Exploring Musical Expression on the Web: Deforming, Exaggerating, and Blending Decomposed Recordings

We introduce a prototype of an educational web application for comparative performance analysis based on source separation and object-based audio techniques. The underlying system decomposes recordings of classical music performances into note events using score-informed source separation and represents the decomposed material using semantic web technologies. In a visual and interactive way, users can explore individual performances by highlighting specific musical aspects directly within the audio and by altering the temporal characteristics to obtain versions in which the micro-timing is exaggerated or suppressed. Multiple performances of the same work can be compared by juxtaposing and blending between the corresponding recordings. Finally, by adjusting the timing of events, users can generate intermediates of multiple performances to investigate their commonalities and differences

Reconstructing Human Expressiveness in Piano Performances with a Transformer Network

Capturing intricate and subtle variations in human expressiveness in music performance using computational approaches is challenging. In this paper, we propose a novel approach for reconstructing human expressiveness in piano performance with a multi-layer bi-directional Transformer encoder. To address the needs for large amounts of accurately captured and score-aligned performance data in training neural networks, we use transcribed scores obtained from an existing transcription model to train our model. We integrate pianist identities to control the sampling process and explore the ability of our system to model variations in expressiveness for different pianists. The system is evaluated through statistical analysis of generated expressive performances and a listening test. Overall, the results suggest that our method achieves state-of-the-art in generating human-like piano performances from transcribed scores, while fully and consistently reconstructing human expressiveness poses further challenges.Comment: 12 pages, 5 figures, submitted to CMMR 202

09051 Abstracts Collection -- Knowledge representation for intelligent music processing

From the twenty-fifth to the thirtieth of January, 2009, the Dagstuhl Seminar 09051 on ``Knowledge representation for intelligent music processing\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Centre for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations and demos given during the seminar as well as plenary presentations, reports of workshop discussions, results and ideas are put together in this paper. The first section describes the seminar topics and goals in general, followed by plenary `stimulus\u27 papers, followed by reports and abstracts arranged by workshop followed finally by some concluding materials providing views of both the seminar itself and also forward to the longer-term goals of the discipline. Links to extended abstracts, full papers and supporting materials are provided, if available. The organisers thank David Lewis for editing these proceedings

Re-Representing Metaphor: Modeling Metaphor Perception Using Dynamically Contextual Distributional Semantics

In this paper, we present a novel context-dependent approach to modeling word meaning, and apply it to the modeling of metaphor. In distributional semantic approaches, words are represented as points in a high dimensional space generated from co-occurrence statistics; the distances between points may then be used to quantifying semantic relationships. Contrary to other approaches which use static, global representations, our approach discovers contextualized representations by dynamically projecting low-dimensional subspaces; in these ad hoc spaces, words can be re-represented in an open-ended assortment of geometrical and conceptual configurations as appropriate for particular contexts. We hypothesize that this context-specific re-representation enables a more effective model of the semantics of metaphor than standard static approaches. We test this hypothesis on a dataset of English word dyads rated for degrees of metaphoricity, meaningfulness, and familiarity by human participants. We demonstrate that our model captures these ratings more effectively than a state-of-the-art static model, and does so via the amount of contextualizing work inherent in the re-representational process