ANALYSIS ON MUSIC CONTENT CORRELATION BASED ON AUDIO CHARACTERISTICS WITH K-MEANS

Music is one of the forms of culture that have exists in the human civilizations since millenniums ago. Emergence of a new era, the growth of technologies, and human’s creativity results in the creation of many variants of music. In fact, it is so variant that people start to create a new term ca...

Full description

Saved in:
Bibliographic Details
Main Author: William Chandra, Maha
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/75332
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:75332
spelling id-itb.:753322023-07-26T16:39:31ZANALYSIS ON MUSIC CONTENT CORRELATION BASED ON AUDIO CHARACTERISTICS WITH K-MEANS William Chandra, Maha Indonesia Final Project audio characteristics, genre, K-means, clustering, recommender system INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/75332 Music is one of the forms of culture that have exists in the human civilizations since millenniums ago. Emergence of a new era, the growth of technologies, and human’s creativity results in the creation of many variants of music. In fact, it is so variant that people start to create a new term called genre to classify music. However, this classification is done by human in a subjective manner. Occasional dispute and disagreement regarding a genre of a certain song occurs between both layman or musical experts. This issue sprouts an idea to categorize music in an objective way instead of a subjective one. In order to do that, experiment will be done to group a set of similar music content by their audio characteristics. Using a dataset of music that have been sound processed beforehand, the dataset will have its audio characteristics in a quantitative value. This audio characteristics will be used to group similar songs together in a cluster using K-means clustering algorithm. Humans tends to determine a genre of a certain music by the atmosphere or the vibe when they hear the music. Hypothetically, the audio characteristics take part in building that atmosphere. Therefore, this research will attempt to prove if grouping a set of songs into clusters depending on their audio characteristics and whether the genre labelling made by human is accurate enough. Moreover, the clustering model will also be used to build a recommender system that will output music with the closest distance in the same cluster in relation to the input. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description Music is one of the forms of culture that have exists in the human civilizations since millenniums ago. Emergence of a new era, the growth of technologies, and human’s creativity results in the creation of many variants of music. In fact, it is so variant that people start to create a new term called genre to classify music. However, this classification is done by human in a subjective manner. Occasional dispute and disagreement regarding a genre of a certain song occurs between both layman or musical experts. This issue sprouts an idea to categorize music in an objective way instead of a subjective one. In order to do that, experiment will be done to group a set of similar music content by their audio characteristics. Using a dataset of music that have been sound processed beforehand, the dataset will have its audio characteristics in a quantitative value. This audio characteristics will be used to group similar songs together in a cluster using K-means clustering algorithm. Humans tends to determine a genre of a certain music by the atmosphere or the vibe when they hear the music. Hypothetically, the audio characteristics take part in building that atmosphere. Therefore, this research will attempt to prove if grouping a set of songs into clusters depending on their audio characteristics and whether the genre labelling made by human is accurate enough. Moreover, the clustering model will also be used to build a recommender system that will output music with the closest distance in the same cluster in relation to the input.
format Final Project
author William Chandra, Maha
spellingShingle William Chandra, Maha
ANALYSIS ON MUSIC CONTENT CORRELATION BASED ON AUDIO CHARACTERISTICS WITH K-MEANS
author_facet William Chandra, Maha
author_sort William Chandra, Maha
title ANALYSIS ON MUSIC CONTENT CORRELATION BASED ON AUDIO CHARACTERISTICS WITH K-MEANS
title_short ANALYSIS ON MUSIC CONTENT CORRELATION BASED ON AUDIO CHARACTERISTICS WITH K-MEANS
title_full ANALYSIS ON MUSIC CONTENT CORRELATION BASED ON AUDIO CHARACTERISTICS WITH K-MEANS
title_fullStr ANALYSIS ON MUSIC CONTENT CORRELATION BASED ON AUDIO CHARACTERISTICS WITH K-MEANS
title_full_unstemmed ANALYSIS ON MUSIC CONTENT CORRELATION BASED ON AUDIO CHARACTERISTICS WITH K-MEANS
title_sort analysis on music content correlation based on audio characteristics with k-means
url https://digilib.itb.ac.id/gdl/view/75332
_version_ 1823652660182515712