Background Image
Previous Page  10 / 13 Next Page
Information
Show Menu
Previous Page 10 / 13 Next Page
Page Background

В практических компьютерных приложениях рассмотренный ме-

тод может быть полезен для организации и упорядочения аудиодан-

ных (например, для автоматического создания аудиоархивов), для ана-

лиза и визуализации сходства в характеристиках речи при проведении

различных исследований (идентификация человека по голосу, обнару-

жение похожих голосов и т.д.).

ЛИТЕРАТУРА

1.

Tzanetakis G.

,

Cook P.

Musical genre classification of audio signals // IEEE

Transactions on Speech and Audio Processing. 2002. Vol. 10. P. 293–302.

2.

Guo G.

,

Li S.Z.

Content-based audio classification and retrieval by support vector

machines // IEEE Transactions on Neural Networks. 2003. Vol. 14. P. 209–215.

3.

Li T.

,

Ogihara M.

,

Li Q.

A comparative study on content-based music genre

classification // SIGIR03. 2003. P. 282–289.

4.

Bagci U.

,

Erzin E.

Automatic Classification of Musical Genres Using Inter-Genre

Similarity // IEEE Signal Processing Letters. 2007. Vol. 14. P. 521–524.

5.

Toward

multi-modal music emotion classification / Y.H. Yang et al. // Proceedings of

the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information

Processing. 2008. P. 70–79.

6.

Zlatintsi A.

,

Maragos P.

Multiscale fractal analysis of musical instrument signals

with application to recognition // IEEE Transactions on Audio, Speech and Language

Processing. 2013. Vol. 21. P. 737–748.

7.

McFee B.

,

Barrington L.

,

Lanckriet G.R.G.

Learning content similarity for music

recommendation// IEEE Transactions on Audio, Speech and Language Processing.

2012. Vol. 20. P. 2207–2218.

8.

Predictability

of music descriptor time series and its application to cover song

detection / Y. Serra et al. // IEEE Transactions on Audio, Speech and Language

Processing. 2012. Vol. 20. P. 514–525.

9.

Manders A.J.

,

Simpson D.M.

,

Bell S.L.

Objective prediction of the sound quality of

music processed by an adaptive feedback canceller // IEEE Transactions on Audio,

Speech and Language Processing. 2012. Vol. 20. P. 1734–1745.

10.

Downie D.

The music information retrieval evaluation exchange (2005–2007):

A window into music information retrieval research // Acoustical Science and

Technology. 2008. Vol. 29. P. 247–255.

11.

Casey M.

et al. Content-based music information retrieval: Current directions and

future challenges // Proceedings of the IEEE. 2008. Vol. 96. P. 668–695.

12.

George J.

,

Shamir L.

Computer analysis of similarities between albums in popular

music // Pattern Recognition Letters. 2014. Vol. 45. P. 78–84.

13.

Wndchrm

— an open source utility for biological image analysis /

L. Shamir et al. // Source Code For Biology And Medicine. 2008. URL:

http://www.scfbm.org/content/3/1/13

(дата обращения: 01.10.2014).

14.

Shamir L.

Evaluation of face datasets as tools for assessing the performance of

face recognition methods // International Journal of Computer Vision. 2008. Vol. 79.

P. 225–230.

15.

WND-CHARM

: Multipurpose image classification using compound image

transforms / N. Orlov et al. // Pattern Recognition Letters. 2008. Vol. 29. P. 1684–

1693.

16.

Deshpande H.

,

Singh R.

,

Nam U.

Classification of music signals in the visual

domain // Proceedings of the COST G-6 Conference on Digital Audio Effects

(DAFX-01). 2001. Vol. 1. P. 1–10.

136 ISSN 0236-3933. Вестник МГТУ им. Н.Э. Баумана. Сер. “Приборостроение”. 2015. № 3