Feature analysis for audio classification

In this work we analyze and implement several audio features. We emphasize our analysis on the ZCR feature and propose a modification making it more robust when signals are near zero. They are all used to discriminate the following audio classes: music, speech, environmental sound. An SVM classifier...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autor principal: Bengolea, G.
Otros Autores: Acevedo, D., Rais, M., Mejail, M., Hancock E., Bayro-Corrochano E.
Formato: Acta de conferencia Capítulo de libro
Lenguaje:Inglés
Publicado: Springer Verlag 2014
Acceso en línea:Registro en Scopus
Handle
Registro en la Biblioteca Digital
Aporte de:Registro referencial: Solicitar el recurso aquí
LEADER 05697caa a22006497a 4500
001 PAPER-23937
003 AR-BaUEN
005 20230518205542.0
008 190411s2014 xx ||||fo|||| 00| 0 eng|d
024 7 |2 scopus  |a 2-s2.0-84949143550 
040 |a Scopus  |b spa  |c AR-BaUEN  |d AR-BaUEN 
100 1 |a Bengolea, G. 
245 1 0 |a Feature analysis for audio classification 
260 |b Springer Verlag  |c 2014 
270 1 0 |m Bengolea, G.; Departamento de Computación, Universidad de Buenos AiresArgentina 
506 |2 openaire  |e Política editorial 
504 |a Chai, W., Semantic segmentation and summarization of music: Methods based on tonality and recurrent structure (2006) IEEE Signal Proc. Mag, 23 (2), pp. 124-132 
504 |a Chen, S.L., Gunduz Ozsu, M.T., Mixed type audio classification with support vector machine (2006) IEEE International Conference on Multimedia and Expo, pp. 781-784. , (July 
504 |a Furui, S., Kikuchi, T., Shinnaka, Y., Hori, C., Speech-to-text and speech-to-speech summarization of spontaneous speech (2004) IEEE Transactions on Speech and Audio Processing, 12 (4), pp. 401-408 
504 |a Johnson, S.E., Woodland, P.C., A method for direct audio search with applications to indexing and retrieval (2000) IEEE International Conference on Acoustics Speech, and Signal Processing, ICASSP 2000, 3, pp. 1427-1430 
504 |a Lu, Z.S.H.-J.Z., Li, L., Content-based audio segmentation using support vector machines (2001) IEEE International Conference on Multimedia and Expo ICME 2001, pp. 749-752. , (August 
504 |a Lu, L., Zhang, H.-J., Jiang, H., Content analysis for audio classification and segmentation (2002) IEEE Trans. on Speech and Audio Processing, 10 (7), pp. 504-516 
504 |a Panagiotakis, C., Tziritas, G., A speech/music discriminator based on rms and zero-crossings (2005) IEEE Transactions on Multimedia, 7 (1), pp. 155-166 
504 |a Park, A., Hazen, T.J., Glass, J.R., Automatic processing of audio lectures for information retrieval: Vocabulary selection and language modeling (2005) IEEE Int'l Conf. on Acoustics Speech, and Signal Proc 
504 |a Sadjadi, S., Hansen, J., Unsupervised speech activity detection using voicing measures and perceptual spectral flux (2013) IEEE Signal Proc. Letters, 20 (3), pp. 197-200 
504 |a Saunders, J., Real-time discrimination of broadcast speech/music (1996) IEEE Int'l Conf. on Acoustics Speech, and Signal Proc, 2, pp. 993-996 
504 |a Vapnik, V.N., (1995) The Nature of Statistical Learning Theory, , Springer-Verlag New York, Inc, New York 
504 |a Zhang, C.-C.J., Kuo, T., Audio content analysis for online audiovisual data segmentation and classification (2001) IEEE Transactions on Speech and Audio Processing, 9 (4), pp. 441-457A4 - Chilean Association for Pattern Recognition (AChiRP); CINVESTAV, Campus Guadalajara; Cuban Association for Pattern Recognition (ACRP); INTEL Education; International Association for Pattern Recognition (IAPR); Mexican Association for Computer Vision; Neurocomputing and Robotics (MACVNR); Portuguese Association for Pattern Recognition (APRP); Spanish Association for Pattern Recogntion and Image Analysis (AERFAI); Special Interest Group of the Brazilian Computer Society (SIGPR-SBC) 
520 3 |a In this work we analyze and implement several audio features. We emphasize our analysis on the ZCR feature and propose a modification making it more robust when signals are near zero. They are all used to discriminate the following audio classes: music, speech, environmental sound. An SVM classifier is used as a classification tool, which has proven to be efficient for audio classification. By means of a selection heuristic we draw conclusions of how they may be combined for fast classification. © Springer International Publishing Switzerland 2014.  |l eng 
593 |a Departamento de Computación, Universidad de Buenos Aires, Argentina 
593 |a Dpt. Matemàtiques i Informàtica / CMLA, Universitat de les Illes Balears / ENS Cachan, Spain 
593 |a Dpt. Matemàtiques i Informàtica / CMLA, Universitat de les Illes Balears / ENS Cachan, France 
690 1 0 |a COMPUTER VISION 
690 1 0 |a PATTERN RECOGNITION 
690 1 0 |a AUDIO CLASS 
690 1 0 |a AUDIO CLASSIFICATION 
690 1 0 |a AUDIO FEATURES 
690 1 0 |a CLASSIFICATION TOOL 
690 1 0 |a ENVIRONMENTAL SOUNDS 
690 1 0 |a FAST CLASSIFICATION 
690 1 0 |a FEATURE ANALYSIS 
690 1 0 |a SVM CLASSIFIERS 
690 1 0 |a AUDIO ACOUSTICS 
700 1 |a Acevedo, D. 
700 1 |a Rais, M. 
700 1 |a Mejail, M. 
700 1 |a Hancock E. 
700 1 |a Bayro-Corrochano E. 
711 2 |d 2 November 2014 through 5 November 2014  |g Código de la conferencia: 109889 
773 0 |d Springer Verlag, 2014  |g v. 8827  |h pp. 239-246  |p Lect. Notes Comput. Sci.  |n Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  |x 03029743  |w (AR-BaUEN)CENRE-983  |z 9783319125671  |t 19th Iberoamerican Congress on Pattern Recognition, CIARP 2014 
856 4 1 |u https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949143550&partnerID=40&md5=05d5d735ed82f0a3ba689bb7958d077d  |y Registro en Scopus 
856 4 0 |u https://hdl.handle.net/20.500.12110/paper_03029743_v8827_n_p239_Bengolea  |y Handle 
856 4 0 |u https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v8827_n_p239_Bengolea  |y Registro en la Biblioteca Digital 
961 |a paper_03029743_v8827_n_p239_Bengolea  |b paper  |c PE 
962 |a info:eu-repo/semantics/article  |a info:ar-repo/semantics/artículo  |b info:eu-repo/semantics/publishedVersion 
963 |a VARI 
999 |c 84890