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VaryNote: A Method to Automatically Vary the Number of Notes in Symbolic Music.
Juan M. Huerta, Bo
Liu, and Peter Stone.
In The 16th International Symposium on Computer
Music Multidisciplinary Research, (CMMR), Springer, November 2023.
Automatically varying the number of notes in symbolic music has various applications in assisting music creators to embellish simple tunes or to reduce complex music to its core idea. In this paper, we formulate the problem of varying the number of notes while preserving the essence of the original music. Our method, VaryNote, adopts an autoencoder architecture in combination with a masking mechanism to control the number of notes. To train the weights of the pitch autoencoder we present a novel surrogate divergence, combining the loss of pitch reconstructions with chord predictions end-to-end. We evaluate our results by plotting chord recognition accuracy with increasing and decreasing numbers of notes, analyzing absolute and relative musical features with a probabilistic framework, and by conducting human surveys. The human survey results indicate humans prefer VaryNote output (with 1.5, 1.9 times notes) over the original music, suggesting that it can be a useful tool in music generation applications.
@InProceedings{CMMR13-Huerta,
author = {Juan M. Huerta and Bo Liu and Peter Stone},
title = {VaryNote: A Method to Automatically Vary the Number of Notes in
Symbolic Music},
booktitle = {The 16th International Symposium on Computer Music Multidisciplinary Research, ({CMMR})},
series = {Lecture Notes in Computer Science},
publisher = {Springer},
location = {Tokyo, Japan},
month = {November},
year = {2023},
abstract = {
Automatically varying the number of notes in symbolic music has various
applications in assisting music creators to embellish simple tunes or to
reduce complex music to its core idea. In this paper, we formulate the
problem of varying the number of notes while preserving the essence of
the original music. Our method, VaryNote, adopts an autoencoder
architecture in combination with a masking mechanism to control the
number of notes. To train the weights of the pitch autoencoder we present
a novel surrogate divergence, combining the loss of pitch reconstructions
with chord predictions end-to-end. We evaluate our results by plotting
chord recognition accuracy with increasing and decreasing numbers of
notes, analyzing absolute and relative musical features with a
probabilistic framework, and by conducting human surveys. The human
survey results indicate humans prefer VaryNote output (with 1.5, 1.9
times notes) over the original music, suggesting that it can be a
useful tool in music generation applications.
},
wwwnote={<a
href="https://youtube.com/live/hQp9IW_w11I?feature=share&t=2890s">the conference presentation</a>},
}
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