23,456 research outputs found
A Planning-based Approach for Music Composition
. Automatic music composition is a fascinating field within computational
creativity. While different Artificial Intelligence techniques have been used
for tackling this task, Planning – an approach for solving complex combinatorial
problems which can count on a large number of high-performance systems and
an expressive language for describing problems – has never been exploited.
In this paper, we propose two different techniques that rely on automated planning
for generating musical structures. The structures are then filled from the bottom
with “raw” musical materials, and turned into melodies. Music experts evaluated
the creative output of the system, acknowledging an overall human-enjoyable
trait of the melodies produced, which showed a solid hierarchical structure and a
strong musical directionality. The techniques proposed not only have high relevance
for the musical domain, but also suggest unexplored ways of using planning
for dealing with non-deterministic creative domains
Phantasms in music
Tese de mestrado, Teoria da Literatura, Universidade de Lisboa, Faculdade de Letras, 2009Esta tese é sobre a forma como a música pode ser descrita mimeticamente. Começando por discutir o tratamento contemporâneo deste tópico, comparo vários argumentos sobre música (em particular música e representação') e descrevo a razão porque estes são relevantes para a questão original da mimêsis. No segundo e terceiro capítulos, discuto ideias ou soluções (para o problema da música mimética' de Aristóteles) para os problemas colocados no primeiro capítulo, e relaciono estes com conceitos usados por Aristóteles nos seus escritos sobre música. O terceiro capítulo trata especificamente do tópico da phantasia e dou ênfase à importância da phantasia no argumento de Aristóteles sobre mimêsis e imitações em relação à música.This thesis attempts to describe how music can be called mimetic. Beginning with a discussion of current work on this topic, I compare various arguments on music (mainly music and representation') and why I find them to be relevant to this original question of mimêsis. In the second and third chapters, I build on ideas or solutions (for the mimetic music' problem originally taken from Aristotle) for problems posed in the first chapter and relate them to concepts Aristotle uses when writing about music. The third chapter specifically treats the topic of phantasia and I propose the importance of phantasia in Aristotle's argument of mimesis and imitations as related to music
Automatic Music Composition using Answer Set Programming
Music composition used to be a pen and paper activity. These these days music
is often composed with the aid of computer software, even to the point where
the computer compose parts of the score autonomously. The composition of most
styles of music is governed by rules. We show that by approaching the
automation, analysis and verification of composition as a knowledge
representation task and formalising these rules in a suitable logical language,
powerful and expressive intelligent composition tools can be easily built. This
application paper describes the use of answer set programming to construct an
automated system, named ANTON, that can compose melodic, harmonic and rhythmic
music, diagnose errors in human compositions and serve as a computer-aided
composition tool. The combination of harmonic, rhythmic and melodic composition
in a single framework makes ANTON unique in the growing area of algorithmic
composition. With near real-time composition, ANTON reaches the point where it
can not only be used as a component in an interactive composition tool but also
has the potential for live performances and concerts or automatically generated
background music in a variety of applications. With the use of a fully
declarative language and an "off-the-shelf" reasoning engine, ANTON provides
the human composer a tool which is significantly simpler, more compact and more
versatile than other existing systems. This paper has been accepted for
publication in Theory and Practice of Logic Programming (TPLP).Comment: 31 pages, 10 figures. Extended version of our ICLP2008 paper.
Formatted following TPLP guideline
Hippocampal sclerosis affects fMR-adaptation of lyrics and melodies in songs
Songs constitute a natural combination of lyrics and melodies, but it is unclear whether and how these two song components are integrated during the emergence of a memory trace. Network theories of memory suggest a prominent role of the hippocampus, together with unimodal sensory areas, in the build-up of conjunctive representations. The present study tested the modulatory influence of the hippocampus on neural adaptation to songs in lateral temporal areas. Patients with unilateral hippocampal sclerosis and healthy matched controls were presented with blocks of short songs in which lyrics and/or melodies were varied or repeated in a crossed factorial design. Neural adaptation effects were taken as correlates of incidental emergent memory traces. We hypothesized that hippocampal lesions, particularly in the left hemisphere, would weaken adaptation effects, especially the integration of lyrics and melodies. Results revealed that lateral temporal lobe regions showed weaker adaptation to repeated lyrics as well as a reduced interaction of the adaptation effects for lyrics and melodies in patients with left hippocampal sclerosis. This suggests a deficient build-up of a sensory memory trace for lyrics and a reduced integration of lyrics with melodies, compared to healthy controls. Patients with right hippocampal sclerosis showed a similar profile of results although the effects did not reach significance in this population. We highlight the finding that the integrated representation of lyrics and melodies typically shown in healthy participants is likely tied to the integrity of the left medial temporal lobe. This novel finding provides the first neuroimaging evidence for the role of the hippocampus during repetitive exposure to lyrics and melodies and their integration into a song
Deep Learning Techniques for Music Generation -- A Survey
This paper is a survey and an analysis of different ways of using deep
learning (deep artificial neural networks) to generate musical content. We
propose a methodology based on five dimensions for our analysis:
Objective - What musical content is to be generated? Examples are: melody,
polyphony, accompaniment or counterpoint. - For what destination and for what
use? To be performed by a human(s) (in the case of a musical score), or by a
machine (in the case of an audio file).
Representation - What are the concepts to be manipulated? Examples are:
waveform, spectrogram, note, chord, meter and beat. - What format is to be
used? Examples are: MIDI, piano roll or text. - How will the representation be
encoded? Examples are: scalar, one-hot or many-hot.
Architecture - What type(s) of deep neural network is (are) to be used?
Examples are: feedforward network, recurrent network, autoencoder or generative
adversarial networks.
Challenge - What are the limitations and open challenges? Examples are:
variability, interactivity and creativity.
Strategy - How do we model and control the process of generation? Examples
are: single-step feedforward, iterative feedforward, sampling or input
manipulation.
For each dimension, we conduct a comparative analysis of various models and
techniques and we propose some tentative multidimensional typology. This
typology is bottom-up, based on the analysis of many existing deep-learning
based systems for music generation selected from the relevant literature. These
systems are described and are used to exemplify the various choices of
objective, representation, architecture, challenge and strategy. The last
section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P.
Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music
Generation, Computational Synthesis and Creative Systems, Springer, 201
Computer-aided Melody Note Transcription Using the Tony Software: Accuracy and Efficiency
accepteddate-added: 2015-05-24 19:18:46 +0000 date-modified: 2017-12-28 10:36:36 +0000 keywords: Tony, melody, note, transcription, open source software bdsk-url-1: https://code.soundsoftware.ac.uk/attachments/download/1423/tony-paper_preprint.pdfdate-added: 2015-05-24 19:18:46 +0000 date-modified: 2017-12-28 10:36:36 +0000 keywords: Tony, melody, note, transcription, open source software bdsk-url-1: https://code.soundsoftware.ac.uk/attachments/download/1423/tony-paper_preprint.pdfWe present Tony, a software tool for the interactive an- notation of melodies from monophonic audio recordings, and evaluate its usability and the accuracy of its note extraction method. The scientific study of acoustic performances of melodies, whether sung or played, requires the accurate transcription of notes and pitches. To achieve the desired transcription accuracy for a particular application, researchers manually correct results obtained by automatic methods. Tony is an interactive tool directly aimed at making this correction task efficient. It provides (a) state-of-the art algorithms for pitch and note estimation, (b) visual and auditory feedback for easy error-spotting, (c) an intelligent graphical user interface through which the user can rapidly correct estimation errors, (d) extensive export functions enabling further processing in other applications. We show that Tony’s built in automatic note transcription method compares favourably with existing tools. We report how long it takes to annotate recordings on a set of 96 solo vocal recordings and study the effect of piece, the number of edits made and the annotator’s increasing mastery of the software. Tony is Open Source software, with source code and compiled binaries for Windows, Mac OS X and Linux available from https://code.soundsoftware.ac.uk/projects/tony/
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