178 research outputs found
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
Harmonic analysis of music using combinatory categorial grammar
FP7 grant 249520 (GRAMPLUS)Various patterns of the organization of Western tonal music exhibit hierarchical structure,
among them the harmonic progressions underlying melodies and the metre underlying
rhythmic patterns. Recognizing these structures is an important part of unconscious
human cognitive processing of music. Since the prosody and syntax of natural
languages are commonly analysed with similar hierarchical structures, it is reasonable
to expect that the techniques used to identify these structures automatically in natural
language might also be applied to the automatic interpretation of music.
In natural language processing (NLP), analysing the syntactic structure of a sentence
is prerequisite to semantic interpretation. The analysis is made difficult by the
high degree of ambiguity in even moderately long sentences. In music, a similar sort of
structural analysis, with a similar degree of ambiguity, is fundamental to tasks such as
key identification and score transcription. These and other tasks depend on harmonic
and rhythmic analyses. There is a long history of applying linguistic analysis techniques
to musical analysis. In recent years, statistical modelling, in particular in the
form of probabilistic models, has become ubiquitous in NLP for large-scale practical
analysis of language. The focus of the present work is the application of statistical
parsing to automatic harmonic analysis of music.
This thesis demonstrates that statistical parsing techniques, adapted from NLP with
little modification, can be successfully applied to recovering the harmonic structure
underlying music. It shows first how a type of formal grammar based on one used
for linguistic syntactic processing, Combinatory Categorial Grammar (CCG), can be
used to analyse the hierarchical structure of chord sequences. I introduce a formal
language similar to first-order predicate logical to express the hierarchical tonal harmonic
relationships between chords. The syntactic grammar formalism then serves as
a mechanism to map an unstructured chord sequence onto its structured analysis.
In NLP, the high degree of ambiguity of the analysis means that a parser must
consider a huge number of possible structures. Chart parsing provides an efficient
mechanism to explore them. Statistical models allow the parser to use information
about structures seen before in a training corpus to eliminate improbable interpretations
early on in the process and to rank the final analyses by plausibility. To apply the
same techniques to harmonic analysis of chord sequences, a corpus of tonal jazz chord
sequences annotated by hand with harmonic analyses is constructed. Two statistical
parsing techniques are adapted to the present task and evaluated on their success at recovering the annotated structures. The experiments show that parsing using a statistical
model of syntactic derivations is more successful than a Markovian baseline
model at recovering harmonic structure. In addition, the practical technique of statistical
supertagging serves to speed up parsing without any loss in accuracy.
This approach to recovering harmonic structure can be extended to the analysis of
performance data symbolically represented as notes. Experiments using some simple
proof-of-concept extensions of the above parsing models demonstrate one probabilistic
approach to this. The results reported provide a baseline for future work on the task of
harmonic analysis of performances
CLiFF Notes: Research In Natural Language Processing at the University of Pennsylvania
The Computational Linguistics Feedback Forum (CLIFF) is a group of students and faculty who gather once a week to discuss the members\u27 current research. As the word feedback suggests, the group\u27s purpose is the sharing of ideas. The group also promotes interdisciplinary contacts between researchers who share an interest in Cognitive Science.
There is no single theme describing the research in Natural Language Processing at Penn. There is work done in CCG, Tree adjoining grammars, intonation, statistical methods, plan inference, instruction understanding, incremental interpretation, language acquisition, syntactic parsing, causal reasoning, free word order languages, ... and many other areas. With this in mind, rather than trying to summarize the varied work currently underway here at Penn, we suggest reading the following abstracts to see how the students and faculty themselves describe their work. Their abstracts illustrate the diversity of interests among the researchers, explain the areas of common interest, and describe some very interesting work in Cognitive Science.
This report is a collection of abstracts from both faculty and graduate students in Computer Science, Psychology and Linguistics. We pride ourselves on the close working relations between these groups, as we believe that the communication among the different departments and the ongoing inter-departmental research not only improves the quality of our work, but makes much of that work possible
The Evolution of Imagination
This book develops a theory of how the imagination functions, and how it evolved. The imagination is characterized as an embodied cognitive system. The system draws upon sensory-motor, visual, and linguistic capacities, but it is a flexible, developmental ability, typified by creative improvisation. The imagination is a voluntary simulation system that draws on perceptual, emotional, and conceptual elements, for the purpose of creating works that adaptively investigate external (environmental) and internal (psychological) resources. Beyond the adaptive useful values of this system, imagination also possesses significant intrinsic value (e.g., in the joy of play, and states of wonder). The book argues that imagination is not a late arrival in the evolution of mind, but one of the earliest human abilities
Music in Evolution and Evolution in Music
Music in Evolution and Evolution in Music by Steven Jan is a comprehensive account of the relationships between evolutionary theory and music. Examining the ‘evolutionary algorithm’ that drives biological and musical-cultural evolution, the book provides a distinctive commentary on how musicality and music can shed light on our understanding of Darwin’s famous theory, and vice-versa.
Comprised of seven chapters, with several musical examples, figures and definitions of terms, this original and accessible book is a valuable resource for anyone interested in the relationships between music and evolutionary thought. Jan guides the reader through key evolutionary ideas and the development of human musicality, before exploring cultural evolution, evolutionary ideas in musical scholarship, animal vocalisations, music generated through technology, and the nature of consciousness as an evolutionary phenomenon.
A unique examination of how evolutionary thought intersects with music, Music in Evolution and Evolution in Music is essential to our understanding of how and why music arose in our species and why it is such a significant presence in our lives
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