52,478 research outputs found
The Analogue Computer as a Voltage-Controlled Synthesiser
This paper re-appraises the role of analogue computers within electronic and
computer music and provides some pointers to future areas of research. It
begins by introducing the idea of analogue computing and placing in the context
of sound and music applications. This is followed by a brief examination of the
classic constituents of an analogue computer, contrasting these with the
typical modular voltage-controlled synthesiser. Two examples are presented,
leading to a discussion on some parallels between these two technologies. This
is followed by an examination of the current state-of-the-art in analogue
computation and its prospects for applications in computer and electronic
music
Beyond Markov Chains, Towards Adaptive Memristor Network-based Music Generation
We undertook a study of the use of a memristor network for music generation,
making use of the memristor's memory to go beyond the Markov hypothesis. Seed
transition matrices are created and populated using memristor equations, and
which are shown to generate musical melodies and change in style over time as a
result of feedback into the transition matrix. The spiking properties of simple
memristor networks are demonstrated and discussed with reference to
applications of music making. The limitations of simulating composing memristor
networks in von Neumann hardware is discussed and a hardware solution based on
physical memristor properties is presented.Comment: 22 pages, 13 pages, conference pape
Dance-the-music : an educational platform for the modeling, recognition and audiovisual monitoring of dance steps using spatiotemporal motion templates
In this article, a computational platform is presented, entitled “Dance-the-Music”, that can be used in a dance educational context to explore and learn the basics of dance steps. By introducing a method based on spatiotemporal motion templates, the platform facilitates to train basic step models from sequentially repeated dance figures performed by a dance teacher. Movements are captured with an optical motion capture system. The teachers’ models can be visualized from a first-person perspective to instruct students how to perform the specific dance steps in the correct manner. Moreover, recognition algorithms-based on a template matching method can determine the quality of a student’s performance in real time by means of multimodal monitoring techniques. The results of an evaluation study suggest that the Dance-the-Music is effective in helping dance students to master the basics of dance figures
Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes
I argue that data becomes temporarily interesting by itself to some
self-improving, but computationally limited, subjective observer once he learns
to predict or compress the data in a better way, thus making it subjectively
simpler and more beautiful. Curiosity is the desire to create or discover more
non-random, non-arbitrary, regular data that is novel and surprising not in the
traditional sense of Boltzmann and Shannon but in the sense that it allows for
compression progress because its regularity was not yet known. This drive
maximizes interestingness, the first derivative of subjective beauty or
compressibility, that is, the steepness of the learning curve. It motivates
exploring infants, pure mathematicians, composers, artists, dancers, comedians,
yourself, and (since 1990) artificial systems.Comment: 35 pages, 3 figures, based on KES 2008 keynote and ALT 2007 / DS 2007
joint invited lectur
Piano Genie
We present Piano Genie, an intelligent controller which allows non-musicians
to improvise on the piano. With Piano Genie, a user performs on a simple
interface with eight buttons, and their performance is decoded into the space
of plausible piano music in real time. To learn a suitable mapping procedure
for this problem, we train recurrent neural network autoencoders with discrete
bottlenecks: an encoder learns an appropriate sequence of buttons corresponding
to a piano piece, and a decoder learns to map this sequence back to the
original piece. During performance, we substitute a user's input for the
encoder output, and play the decoder's prediction each time the user presses a
button. To improve the intuitiveness of Piano Genie's performance behavior, we
impose musically meaningful constraints over the encoder's outputs.Comment: Published as a conference paper at ACM IUI 201
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