Modeling Concept Dynamics for Large Scale Music Search
Continuing advances in data storage and communication technologies have led to an explosive growth in digital music collections. To cope with their increasing scale, we need effective Music Information Retrieval (MIR) capabilities like tagging, concept search and clustering. Integral to MIR is a fra...
Saved in:
Main Authors: | , , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2012
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/1647 https://ink.library.smu.edu.sg/context/sis_research/article/2646/viewcontent/p455_shen.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-2646 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-26462017-07-11T13:34:18Z Modeling Concept Dynamics for Large Scale Music Search SHEN, Jialie PANG, Hwee Hwa WANG, Meng YAN, Shuicheng Continuing advances in data storage and communication technologies have led to an explosive growth in digital music collections. To cope with their increasing scale, we need effective Music Information Retrieval (MIR) capabilities like tagging, concept search and clustering. Integral to MIR is a framework for modelling music documents and generating discriminative signatures for them. In this paper, we introduce a multimodal, layered learning framework called DMCM. Distinguished from the existing approaches that encode music as an ensemble of order-less feature vectors, our framework extracts from each music document a variety of acoustic features, and translates them into low-level encodings over the temporal dimension. From them, DMCM elucidates the concept dynamics in the music document, representing them with a novel music signature scheme called Stochastic Music Concept Histogram (SMCH) that captures the probability distribution over all the concepts. Experiment results with two large music collections confirm the advantages of the proposed framework over existing methods on various MIR tasks. 2012-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1647 info:doi/10.1145/2348283.2348346 https://ink.library.smu.edu.sg/context/sis_research/article/2646/viewcontent/p455_shen.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 Music Information Retrieval Similarity Measure Music Concepts Databases and Information Systems Numerical Analysis and Scientific Computing |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
Music Information Retrieval Similarity Measure Music Concepts Databases and Information Systems Numerical Analysis and Scientific Computing |
spellingShingle |
Music Information Retrieval Similarity Measure Music Concepts Databases and Information Systems Numerical Analysis and Scientific Computing SHEN, Jialie PANG, Hwee Hwa WANG, Meng YAN, Shuicheng Modeling Concept Dynamics for Large Scale Music Search |
description |
Continuing advances in data storage and communication technologies have led to an explosive growth in digital music collections. To cope with their increasing scale, we need effective Music Information Retrieval (MIR) capabilities like tagging, concept search and clustering. Integral to MIR is a framework for modelling music documents and generating discriminative signatures for them. In this paper, we introduce a multimodal, layered learning framework called DMCM. Distinguished from the existing approaches that encode music as an ensemble of order-less feature vectors, our framework extracts from each music document a variety of acoustic features, and translates them into low-level encodings over the temporal dimension. From them, DMCM elucidates the concept dynamics in the music document, representing them with a novel music signature scheme called Stochastic Music Concept Histogram (SMCH) that captures the probability distribution over all the concepts. Experiment results with two large music collections confirm the advantages of the proposed framework over existing methods on various MIR tasks. |
format |
text |
author |
SHEN, Jialie PANG, Hwee Hwa WANG, Meng YAN, Shuicheng |
author_facet |
SHEN, Jialie PANG, Hwee Hwa WANG, Meng YAN, Shuicheng |
author_sort |
SHEN, Jialie |
title |
Modeling Concept Dynamics for Large Scale Music Search |
title_short |
Modeling Concept Dynamics for Large Scale Music Search |
title_full |
Modeling Concept Dynamics for Large Scale Music Search |
title_fullStr |
Modeling Concept Dynamics for Large Scale Music Search |
title_full_unstemmed |
Modeling Concept Dynamics for Large Scale Music Search |
title_sort |
modeling concept dynamics for large scale music search |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2012 |
url |
https://ink.library.smu.edu.sg/sis_research/1647 https://ink.library.smu.edu.sg/context/sis_research/article/2646/viewcontent/p455_shen.pdf |
_version_ |
1770571389096427520 |