Artículo

Bengolea, G.; Acevedo, D.; Rais, M.; Mejail, M.; Hancock E.; Bayro-Corrochano E. "Feature analysis for audio classification" (2014) 19th Iberoamerican Congress on Pattern Recognition, CIARP 2014. 8827:239-246
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Abstract:

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.

Registro:

Documento: Artículo
Título:Feature analysis for audio classification
Autor:Bengolea, G.; Acevedo, D.; Rais, M.; Mejail, M.; Hancock E.; Bayro-Corrochano E.
Filiación:Departamento de Computación, Universidad de Buenos Aires, Argentina
Dpt. Matemàtiques i Informàtica / CMLA, Universitat de les Illes Balears / ENS Cachan, Spain
Dpt. Matemàtiques i Informàtica / CMLA, Universitat de les Illes Balears / ENS Cachan, France
Palabras clave:Computer vision; Pattern recognition; Audio class; Audio classification; Audio features; Classification tool; Environmental sounds; Fast classification; Feature analysis; SVM classifiers; Audio acoustics
Año:2014
Volumen:8827
Página de inicio:239
Página de fin:246
Título revista:19th Iberoamerican Congress on Pattern Recognition, CIARP 2014
Título revista abreviado:Lect. Notes Comput. Sci.
ISSN:03029743
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v8827_n_p239_Bengolea

Referencias:

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Panagiotakis, C., Tziritas, G., A speech/music discriminator based on rms and zero-crossings (2005) IEEE Transactions on Multimedia, 7 (1), pp. 155-166
  • 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
  • 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
  • Saunders, J., Real-time discrimination of broadcast speech/music (1996) IEEE Int'l Conf. on Acoustics Speech, and Signal Proc, 2, pp. 993-996
  • Vapnik, V.N., (1995) The Nature of Statistical Learning Theory, , Springer-Verlag New York, Inc, New York
  • 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)

Citas:

---------- APA ----------
Bengolea, G., Acevedo, D., Rais, M., Mejail, M., Hancock E. & Bayro-Corrochano E. (2014) . Feature analysis for audio classification. 19th Iberoamerican Congress on Pattern Recognition, CIARP 2014, 8827, 239-246.
Recuperado de https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v8827_n_p239_Bengolea [ ]
---------- CHICAGO ----------
Bengolea, G., Acevedo, D., Rais, M., Mejail, M., Hancock E., Bayro-Corrochano E. "Feature analysis for audio classification" . 19th Iberoamerican Congress on Pattern Recognition, CIARP 2014 8827 (2014) : 239-246.
Recuperado de https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v8827_n_p239_Bengolea [ ]
---------- MLA ----------
Bengolea, G., Acevedo, D., Rais, M., Mejail, M., Hancock E., Bayro-Corrochano E. "Feature analysis for audio classification" . 19th Iberoamerican Congress on Pattern Recognition, CIARP 2014, vol. 8827, 2014, pp. 239-246.
Recuperado de https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v8827_n_p239_Bengolea [ ]
---------- VANCOUVER ----------
Bengolea, G., Acevedo, D., Rais, M., Mejail, M., Hancock E., Bayro-Corrochano E. Feature analysis for audio classification. Lect. Notes Comput. Sci. 2014;8827:239-246.
Available from: https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v8827_n_p239_Bengolea [ ]