25,668 research outputs found
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
A Simple Method to Produce Algorithmic MIDI Music based on Randomness, Simple Probabilities and Multi-Threading
This paper introduces a simple method for producing multichannel MIDI music
that is based on randomness and simple probabilities. One distinctive feature
of the method is that it produces and sends in parallel to the sound card more
than one unsynchronized channels by exploiting the multi-threading capabilities
of general purpose programming languages. As consequence the derived sound
offers a quite ``full" and ``unpredictable" acoustic experience to the
listener. Subsequently the paper reports the results of an evaluation with
users. The results were very surprising: the majority of users responded that
they could tolerate this music in various occasions.Comment: 7 pages, 5 figure
CHR(PRISM)-based Probabilistic Logic Learning
PRISM is an extension of Prolog with probabilistic predicates and built-in
support for expectation-maximization learning. Constraint Handling Rules (CHR)
is a high-level programming language based on multi-headed multiset rewrite
rules.
In this paper, we introduce a new probabilistic logic formalism, called
CHRiSM, based on a combination of CHR and PRISM. It can be used for high-level
rapid prototyping of complex statistical models by means of "chance rules". The
underlying PRISM system can then be used for several probabilistic inference
tasks, including probability computation and parameter learning. We define the
CHRiSM language in terms of syntax and operational semantics, and illustrate it
with examples. We define the notion of ambiguous programs and define a
distribution semantics for unambiguous programs. Next, we describe an
implementation of CHRiSM, based on CHR(PRISM). We discuss the relation between
CHRiSM and other probabilistic logic programming languages, in particular PCHR.
Finally we identify potential application domains
Neural Translation of Musical Style
Music is an expressive form of communication often used to convey emotion in
scenarios where "words are not enough". Part of this information lies in the
musical composition where well-defined language exists. However, a significant
amount of information is added during a performance as the musician interprets
the composition. The performer injects expressiveness into the written score
through variations of different musical properties such as dynamics and tempo.
In this paper, we describe a model that can learn to perform sheet music. Our
research concludes that the generated performances are indistinguishable from a
human performance, thereby passing a test in the spirit of a "musical Turing
test"
Overview of VideoCLEF 2008: Automatic generation of topic-based feeds for dual language audio-visual content
The VideoCLEF track, introduced in 2008, aims to develop and evaluate tasks related to analysis of and access to multilingual multimedia content. In its first year, VideoCLEF piloted the Vid2RSS task, whose main subtask was the classification of dual language video (Dutchlanguage
television content featuring English-speaking experts and studio guests). The task offered two additional discretionary subtasks: feed translation and automatic keyframe extraction. Task participants were supplied with Dutch archival metadata, Dutch speech transcripts,
English speech transcripts and 10 thematic category labels, which they were required to assign to the test set videos. The videos were grouped by class label into topic-based RSS-feeds, displaying title, description and keyframe for each video. Five groups participated in the 2008 VideoCLEF track. Participants were required to collect their own training data; both Wikipedia and general web content were used. Groups deployed various classifiers (SVM, Naive Bayes and k-NN) or treated the problem as an information retrieval task. Both the Dutch speech transcripts and the archival metadata performed well as sources of indexing features, but no group succeeded in exploiting combinations of feature sources to significantly enhance performance. A small scale fluency/adequacy evaluation of the translation task output revealed the translation to be of sufficient quality to make it valuable to a non-Dutch speaking English speaker. For keyframe extraction, the strategy chosen was
to select the keyframe from the shot with the most representative speech transcript content. The automatically selected shots were shown, with a small user study, to be competitive with manually selected shots. Future years of VideoCLEF will aim to expand the corpus and the class label list, as well as to extend the track to additional tasks
Towards Transformational Creation of Novel Songs
We study transformational computational creativity in the context of writing songs and describe an implemented system that is able to modify its own goals and operation. With this, we contribute to three aspects of computational creativity and song generation: (1) Application-wise, songs are an interesting and challenging target for creativity, as they require the production of complementary music and lyrics. (2) Technically, we approach the problem of creativity and song generation using constraint programming. We show how constraints can be used declaratively to define a search space of songs so that a standard constraint solver can then be used to generate songs. (3) Conceptually, we describe a concrete architecture for transformational creativity where the creative (song writing) system has some responsibility for setting its own search space and goals. In the proposed architecture, a meta-level control component does this transparently by manipulating the constraints at runtime based on self-reflection of the system. Empirical experiments suggest the system is able to create songs according to its own taste.Peer reviewe
AI Methods in Algorithmic Composition: A Comprehensive Survey
Algorithmic composition is the partial or total automation of the process of music composition
by using computers. Since the 1950s, different computational techniques related to
Artificial Intelligence have been used for algorithmic composition, including grammatical
representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint
programming and evolutionary algorithms. This survey aims to be a comprehensive
account of research on algorithmic composition, presenting a thorough view of the field for
researchers in Artificial Intelligence.This study was partially supported by a grant for the MELOMICS project
(IPT-300000-2010-010) from the Spanish Ministerio de Ciencia e Innovación, and a grant for
the CAUCE project (TSI-090302-2011-8) from the Spanish Ministerio de Industria, Turismo
y Comercio. The first author was supported by a grant for the GENEX project (P09-TIC-
5123) from the Consejería de Innovación y Ciencia de Andalucía
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