A Neural Network for Generating Noise Music

Manni CHEN; PerMagnus Lindborg

A Neural Network for Generating Noise Music

Manni CHEN^*, PerMagnus Lindborg

^*Corresponding author for this work

School of Creative Media

Research output: Conference Papers › RGC 32 - Refereed conference paper (without host publication) › peer-review

Abstract

Generating music with pitch and rhythm has received ample attention from researchers. By contrast, noise music as a special genre is largely ignored so far. This paper aims to generate noise music in ‘Merzbow style’ using a neural network called variational autoencoder (VAE). The results show that our proof-of-concept system can generate noise music.

Original language	English
Number of pages	3
Publication status	Published - Dec 2023
Event	19th Workshop on Computer Music and Audio Technology (WOCMAT 2023): Conference & Taiwan Computer Music Concert - Kainan University, Taiwan Duration: 21 Dec 2023 → 22 Dec 2023 https://sites.google.com/view/2023-wocmat-workshop-on-c/home%E9%A6%96%E9%A0%81

Conference

Conference	19th Workshop on Computer Music and Audio Technology (WOCMAT 2023)
Country/Territory	Taiwan
Period	21/12/23 → 22/12/23
Internet address	https://sites.google.com/view/2023-wocmat-workshop-on-c/home%E9%A6%96%E9%A0%81

Bibliographical note

Information for this record is supplemented by the author(s) concerned.

Research Keywords

noise music generation
neural network
noise music
autoencoder
music generation

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@conference{1a69891c4f934c9d8473e253d0bd6b91,

title = "A Neural Network for Generating Noise Music",

abstract = "Generating music with pitch and rhythm has received ample attention from researchers. By contrast, noise music as a special genre is largely ignored so far. This paper aims to generate noise music in {\textquoteleft}Merzbow style{\textquoteright} using a neural network called variational autoencoder (VAE). The results show that our proof-of-concept system can generate noise music.",

keywords = "noise music generation, neural network, noise music, autoencoder, music generation",

author = "Manni CHEN and PerMagnus Lindborg",

note = "Information for this record is supplemented by the author(s) concerned.; 19th Workshop on Computer Music and Audio Technology (WOCMAT 2023) : Conference & Taiwan Computer Music Concert ; Conference date: 21-12-2023 Through 22-12-2023",

year = "2023",

month = dec,

language = "English",

url = "https://sites.google.com/view/2023-wocmat-workshop-on-c/home%E9%A6%96%E9%A0%81",

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TY - CONF

T1 - A Neural Network for Generating Noise Music

AU - CHEN, Manni

AU - Lindborg, PerMagnus

N1 - Information for this record is supplemented by the author(s) concerned.

PY - 2023/12

Y1 - 2023/12

N2 - Generating music with pitch and rhythm has received ample attention from researchers. By contrast, noise music as a special genre is largely ignored so far. This paper aims to generate noise music in ‘Merzbow style’ using a neural network called variational autoencoder (VAE). The results show that our proof-of-concept system can generate noise music.

AB - Generating music with pitch and rhythm has received ample attention from researchers. By contrast, noise music as a special genre is largely ignored so far. This paper aims to generate noise music in ‘Merzbow style’ using a neural network called variational autoencoder (VAE). The results show that our proof-of-concept system can generate noise music.

KW - noise music generation

KW - neural network

KW - noise music

KW - autoencoder

KW - music generation

UR - https://sites.google.com/view/2023-wocmat-workshop-on-c/home%E9%A6%96%E9%A0%81

UR - https://sites.google.com/view/2023-wocmat-workshop-on-c/paper-session-schedule%E8%AB%96%E6%96%87%E7%99%BC%E8%A1%A8%E8%A1%8C%E7%A8%8B

M3 - RGC 32 - Refereed conference paper (without host publication)

T2 - 19th Workshop on Computer Music and Audio Technology (WOCMAT 2023)

Y2 - 21 December 2023 through 22 December 2023

ER -

A Neural Network for Generating Noise Music

Abstract

Conference

Bibliographical note

Research Keywords

Other Access Options

Permanent Link

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