Towards effective content-based music retrieval with multiple acoustic feature combination
In this paper, we present a new approach to constructing music descriptors to support efficient content-based music retrieval and classification. The system applies multiple musical properties combined with a hybrid architecture based on principal component analysis (PCA) and a multilayer perceptron...
Saved in:
Main Authors: | , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2006
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/3546 https://ink.library.smu.edu.sg/context/sis_research/article/4547/viewcontent/EffContentBased_MusicRetrievalAcoustics_2006.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-4547 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-45472017-03-27T03:51:12Z Towards effective content-based music retrieval with multiple acoustic feature combination SHEN, Jialie Shepherd, John NGU, Ann H. H. In this paper, we present a new approach to constructing music descriptors to support efficient content-based music retrieval and classification. The system applies multiple musical properties combined with a hybrid architecture based on principal component analysis (PCA) and a multilayer perceptron neural network. This architecture enables straightforward incorporation of multiple musical feature vectors, based on properties such as timbral texture, pitch, and rhythm structure, into a single low-dimensioned vector that is more effective for classification than the larger individual feature vectors. The use of supervised training enables incorporation of human musical perception that further enhances the classification process. We compare our approach with state of the art techniques and demonstrate its effectiveness on content-based music retrieval. In addition, extensive experimental study illustrates its effectiveness and robustness against various kinds of audio alteration. 2006-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3546 info:doi/10.1109/TMM.2006.884618 https://ink.library.smu.edu.sg/context/sis_research/article/4547/viewcontent/EffContentBased_MusicRetrievalAcoustics_2006.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Classification Multimedia database Music retrieval Computer Sciences Databases and Information Systems |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
Classification Multimedia database Music retrieval Computer Sciences Databases and Information Systems |
spellingShingle |
Classification Multimedia database Music retrieval Computer Sciences Databases and Information Systems SHEN, Jialie Shepherd, John NGU, Ann H. H. Towards effective content-based music retrieval with multiple acoustic feature combination |
description |
In this paper, we present a new approach to constructing music descriptors to support efficient content-based music retrieval and classification. The system applies multiple musical properties combined with a hybrid architecture based on principal component analysis (PCA) and a multilayer perceptron neural network. This architecture enables straightforward incorporation of multiple musical feature vectors, based on properties such as timbral texture, pitch, and rhythm structure, into a single low-dimensioned vector that is more effective for classification than the larger individual feature vectors. The use of supervised training enables incorporation of human musical perception that further enhances the classification process. We compare our approach with state of the art techniques and demonstrate its effectiveness on content-based music retrieval. In addition, extensive experimental study illustrates its effectiveness and robustness against various kinds of audio alteration. |
format |
text |
author |
SHEN, Jialie Shepherd, John NGU, Ann H. H. |
author_facet |
SHEN, Jialie Shepherd, John NGU, Ann H. H. |
author_sort |
SHEN, Jialie |
title |
Towards effective content-based music retrieval with multiple acoustic feature combination |
title_short |
Towards effective content-based music retrieval with multiple acoustic feature combination |
title_full |
Towards effective content-based music retrieval with multiple acoustic feature combination |
title_fullStr |
Towards effective content-based music retrieval with multiple acoustic feature combination |
title_full_unstemmed |
Towards effective content-based music retrieval with multiple acoustic feature combination |
title_sort |
towards effective content-based music retrieval with multiple acoustic feature combination |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2006 |
url |
https://ink.library.smu.edu.sg/sis_research/3546 https://ink.library.smu.edu.sg/context/sis_research/article/4547/viewcontent/EffContentBased_MusicRetrievalAcoustics_2006.pdf |
_version_ |
1770573300084244480 |