Content-based singer classification on compressed domain audio data

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• 作者：Tsung-Han Tsai (1)
  Yu-Siang Huang (1)
  Pei-Yun Liu (1)
  De-Ming Chen (1)
  
  1. Department of Electrical Engineering ; National Central University ; No.300 ; Jhongda Rd. ; Jhongli City ; Taoyuan County ; 32001 ; Taiwan ; People鈥檚 Republic of China
  
• 关键词：MP3 ; MDCT ; MFCC ; GMM
• 刊名：Multimedia Tools and Applications
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：74
• 期：4
• 页码：1489-1509
• 全文大小：1,590 KB
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• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721

文摘

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.