6,240 research outputs found
Query-based Deep Improvisation
In this paper we explore techniques for generating new music using a
Variational Autoencoder (VAE) neural network that was trained on a corpus of
specific style. Instead of randomly sampling the latent states of the network
to produce free improvisation, we generate new music by querying the network
with musical input in a style different from the training corpus. This allows
us to produce new musical output with longer-term structure that blends aspects
of the query to the style of the network. In order to control the level of this
blending we add a noisy channel between the VAE encoder and decoder using
bit-allocation algorithm from communication rate-distortion theory. Our
experiments provide new insight into relations between the representational and
structural information of latent states and the query signal, suggesting their
possible use for composition purposes
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Remembering the future: An overview of co-evolution in musical improvisation
Musical improvisation is driven mainly by the unconscious mind, engaging the dialogic imagination to reference the entire cultural heritage of an improvisor in a single flash. This paper introduces a case study of evolutionary computation techniques, in particular genetic co-evolution, as applied to the frequency domain using MPEG7 techniques, in order to create an artificial agent that mediates between an improvisor and her unconscious mind, to probe and unblock improvisatory action in live music performance or practice
A New Dataset for Amateur Vocal Percussion Analysis
The imitation of percussive instruments via the human voice is a natural way
for us to communicate rhythmic ideas and, for this reason, it attracts the
interest of music makers. Specifically, the automatic mapping of these vocal
imitations to their emulated instruments would allow creators to realistically
prototype rhythms in a faster way. The contribution of this study is two-fold.
Firstly, a new Amateur Vocal Percussion (AVP) dataset is introduced to
investigate how people with little or no experience in beatboxing approach the
task of vocal percussion. The end-goal of this analysis is that of helping
mapping algorithms to better generalise between subjects and achieve higher
performances. The dataset comprises a total of 9780 utterances recorded by 28
participants with fully annotated onsets and labels (kick drum, snare drum,
closed hi-hat and opened hi-hat). Lastly, we conducted baseline experiments on
audio onset detection with the recorded dataset, comparing the performance of
four state-of-the-art algorithms in a vocal percussion context
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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
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