Content-based singer classification on compressed domain audio data

NCUIR > College of Electrical Engineering & Computer Science > Department of Electrical Engineering > journal & Dissertation > Item 987654321/106807

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106807

Title:	Content-based singer classification on compressed domain audio data
Authors:	蔡宗漢;Tsai, Tsung-Han;Huang, Yu-Siang;Liu, Pei-Yun;Chen, De-Ming
Contributors:	資訊電機學院電機工程學系
Keywords:	Analysis;Approximation;Audio data;Classification;Compressed;Computer Communication Networks;Computer Science;Data Structures and Information Theory;Digital music;Identification;Information retrieval;Mathematical analysis;Mathematical models;MP3;Multimedia;Multimedia computer applications;Multimedia Information Systems;Music;Musical instruments;Musical performances;Musicians & conductors;Pattern recognition;Singers;Special Purpose and Application-Based Systems;Studies;Vectors (mathematics)
Date:	2014-01-01
Issue Date:	2026-04-23 13:43:50 (UTC+8)
Publisher:	Springer Netherlands;Boston: Springer US
Abstract:	摘要： In this paper, we proposed a singer identification approach to automatically identify the singer of an unknown MP3 audio data. Differing from previous researches for singer identification in MP3 compressed domain, we use Mel-Frequency Cepstral Coefficients (MFCC) as the feature instead of MDCT (modified discrete cosine transform) coefficients. Although MFCC is often used in music classification and speaker recognition, it cannot be directly obtained from compressed music data such as MP3 format. We introduce a modified method for calculating MFCC vector in MP3 compressed domain. For describing the distribution of MFCC vector, the Gaussian mixture model (GMM) is applied. To find the nearest singer, we use maximum likelihood classification (MLC) to allot each input MFCC vector to its nearest group. The experimental result verifies the feasibility of the proposed approach. 其他題名： Multimed Tools Appl 出版者： Boston: Springer US 出版日期： 2015-02-01 出處： Multimedia tools and applications, 2015-02, Vol.74 (4), p.1489-1509 資源來源： ABI/INFORM Collection 版權： Springer Science+Business Media New York 2014 版權： Springer Science+Business Media New York 2015 識別號： ISSN: 1380-7501 識別號： EISSN: 1573-7721 識別號： DOI: 10.1007/s11042-014-2189-6
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	18	View/Open

社群 sharing

Loading...