17,081 research outputs found
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"
Lisp, Jazz, Aikido -- Three Expressions of a Single Essence
The relation between Science (what we can explain) and Art (what we can't)
has long been acknowledged and while every science contains an artistic part,
every art form also needs a bit of science. Among all scientific disciplines,
programming holds a special place for two reasons. First, the artistic part is
not only undeniable but also essential. Second, and much like in a purely
artistic discipline, the act of programming is driven partly by the notion of
aesthetics: the pleasure we have in creating beautiful things. Even though the
importance of aesthetics in the act of programming is now unquestioned, more
could still be written on the subject. The field called "psychology of
programming" focuses on the cognitive aspects of the activity, with the goal of
improving the productivity of programmers. While many scientists have
emphasized their concern for aesthetics and the impact it has on their
activity, few computer scientists have actually written about their thought
process while programming. What makes us like or dislike such and such language
or paradigm? Why do we shape our programs the way we do? By answering these
questions from the angle of aesthetics, we may be able to shed some new light
on the art of programming. Starting from the assumption that aesthetics is an
inherently transversal dimension, it should be possible for every programmer to
find the same aesthetic driving force in every creative activity they
undertake, not just programming, and in doing so, get deeper insight on why and
how they do things the way they do. On the other hand, because our aesthetic
sensitivities are so personal, all we can really do is relate our own
experiences and share it with others, in the hope that it will inspire them to
do the same. My personal life has been revolving around three major creative
activities, of equal importance: programming in Lisp, playing Jazz music, and
practicing Aikido. But why so many of them, why so different ones, and why
these specifically? By introspecting my personal aesthetic sensitivities, I
eventually realized that my tastes in the scientific, artistic, and physical
domains are all motivated by the same driving forces, hence unifying Lisp,
Jazz, and Aikido as three expressions of a single essence, not so different
after all. Lisp, Jazz, and Aikido are governed by a limited set of rules which
remain simple and unobtrusive. Conforming to them is a pleasure. Because Lisp,
Jazz, and Aikido are inherently introspective disciplines, they also invite you
to transgress the rules in order to find your own. Breaking the rules is fun.
Finally, if Lisp, Jazz, and Aikido unify so many paradigms, styles, or
techniques, it is not by mere accumulation but because they live at the
meta-level and let you reinvent them. Working at the meta-level is an
enlightening experience. Understand your aesthetic sensitivities and you may
gain considerable insight on your own psychology of programming. Mine is
perhaps common to most lispers. Perhaps also common to other programming
communities, but that, is for the reader to decide..
Recommended from our members
Harmony and Technology Enhanced Learning
New technologies offer rich opportunities to support education in harmony. In this chapter we consider theoretical perspectives and underlying principles behind technologies for learning and teaching harmony. Such perspectives help in matching existing and future technologies to educational purposes, and to inspire the creative re-appropriation of technologies
Comparative Study of Musical Performance by Machine Learning
This paper deals with the very special domains from computer science viz. Machine learning, g enetic algorithms, rule based systems, music and various intelligent systems . Most of the musicians use m achine l earning approach to improve accuracy of the musical note . Intelligent systems use databases to store monophonic audio recordings performed by the musician of jazz standards. Howeve r, these approach use to obtain a model which explai n and generate performances of expressive music. Rule based approach gives note level information containing time, dynamics and melody alteration . In this paper, we investigate how all these machine learning techniques work . We also compare their featu res and performance with evolutionary approach which will help user to get Rule based incremental model . Finally, output will be in a summarized format which gives reference solution. Comparative analysis shows that methods used by Incremental Rule b ased Appro ach provide full functionality and effectiveness as compared with previous machine learning techniques
Playing with Cases: Rendering Expressive Music with Case-Based Reasoning
This article surveys long-term research on the problem of rendering expressive music by means of AI techniques
with an emphasis on case-based reasoning (CBR). Following a brief overview discussing why people prefer listening to
expressive music instead of nonexpressive synthesized music, we examine a representative selection of well-known approaches
to expressive computer,music performance with an emphasis on AI-related approaches. In the main part of the article we focus
on the existing CBR approaches to the problem of synthesizing expressive music, and particularly on Tempo-Express, a
case-based reasoning system developed at our Institute, for applying musically acceptable tempo transformations to
monophonic audio recordings of musical performances. Finally we briefly describe an ongoing extension of our previous work
consisting of complementing audio information with information about the gestures of the musician. Music is played through
our bodies, therefore capturing the gesture of the performer is a fundamental aspect that has to be taken into account in future
expressive music renderings. This article is based on the >2011 Robert S. Engelmore Memorial Lecture> given by the first
author at AAAI/IAAI 2011.This research is partially supported by the Ministry of Science and Innovation of Spain under the project NEXT-CBR (TIN2009-13692-C03-01) and the Generalitat de Catalunya AGAUR Grant 2009-SGR-1434Peer Reviewe
Logic-based Modelling of Musical Harmony for Automatic Characterisation and Classification
The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the authorMusic like other online media is undergoing an information explosion. Massive online
music stores such as the iTunes Store1 or Amazon MP32, and their counterparts, the streaming
platforms, such as Spotify3, Rdio4 and Deezer5, offer more than 30 million6 pieces of music to
their customers, that is to say anybody with a smart phone. Indeed these ubiquitous devices
offer vast storage capacities and cloud-based apps that can cater any music request. As Paul
Lamere puts it7:
âwe can now have a virtually endless supply of music in our pocket. The âbottomless iPodâ
will have as big an effect on how we listen to music as the original iPod had back in 2001.
But with millions of songs to chose from, we will need help finding music that we want to
hear [...]. We will need new tools that help us manage our listening experience.â
Retrieval, organisation, recommendation, annotation and characterisation of musical data is
precisely what the Music Information Retrieval (MIR) community has been working on for
at least 15 years (Byrd and Crawford, 2002). It is clear from its historical roots in practical
fields such as Information Retrieval, Information Systems, Digital Resources and Digital
Libraries but also from the publications presented at the first International Symposium on Music
Information Retrieval in 2000 that MIR has been aiming to build tools to help people to navigate,
explore and make sense of music collections (Downie et al., 2009). That also includes analytical
tools to suppor
- âŠ