Abstract
The article presents an approach to environmental sound recognition that uses selected methods from the field of digital image processing and recognition. The proposed technique adopts the assumption that an audio signal can be converted into a visual representation, and processed further, as an image. At the first stage the audio data are converted into rectangular matrices called feature maps. Then a two-step approach is applied: the construction of a representative database of reference samples and the identification of test samples. The process of building the database employs two-dimensional linear discriminant analysis. Then the recognition operation is carried out in a reduced feature space that has been obtained by two-dimensional Karhunen–Loeve projection. At the classification stage, a minimum distance classifier is applied to different features. As it is shown, the results are very encouraging and can be a base for many practical audio applications.
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Abe, M., Matsumoto, J., Nishiguchi, M.: Content-based classification of audio signals using source and structure modelling. In: Proceedings of the IEEE Pacific Conference on Multimedia, pp. 280–283 (2000)
Cantrell, C.D.: Modern Mathematical Methods for Physicists and Engineers. Cambridge University Press, Cambridge (2000)
Clavel, C., Ehrette, T., Richard, G.: Events detection for an audio-based surveillance system. IEEE Int. Conf. Multimed. Expo, ICME 2005, 1306–1309 (2005)
Davis, S., Mermelstein, P.: Comparison of parametric representation for monosyllabic word recognition in continuously spoken sentences. IEEE Trans. ASSP 28(4), 357–366 (1980)
Dennis, J., Tran, H.D., Li, H.L.: Spectrogram image feature for sound event classification in mismatched conditions. IEEE Signal Process. Lett. 18(2), 130–133 (2011)
Forczmański, P.: Evaluation of singer’s voice quality by means of visual pattern recognition. J. Voice. doi:10.1016/j.jvoice.2015.03.001 (2015, in press)
Forczmański, P., Frejlichowski, D.: Classification of elementary stamp shapes by means of reduced point distance histogram representation. Mach. Learn. Data Min. Pattern Recognit., LNCS 7376, 603–616 (2012)
Geiger, J.T., Schuller, B., Rigoll, G.: Large-scale audio feature extraction and SVM for acoustic scene classification. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 1–4 (2013)
Jiang, H., Bai, J., Zhang, S., Xu, B.: SVM-based audio scene classification, natural language processing and knowledge engineering. In: Proceedings of 2005 IEEE International Conference on IEEE NLP-KE’05, pp. 131–136 (2005)
Kukharev, G., Forczmański, P.: Face recognition by means of two-dimensional direct linear discriminant analysis. In: Proceedings of the 8th International Conference PRIP 2005 Pattern Recognition and Information Processing. Republic of Belarus, Minsk, pp. 280–283 (2005)
Maka, T.: Environmental background sounds classification based on properties of feature contours. In: 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE, Amsterdam, LNCS, vol. 7906, pp. 602–609 (2013)
Okarma, K., Forczmański, P.: 2DLDA-based texture recognition in the aspect of objective image quality assessment. Ann. Univ. Mariae Curie-Sklodowska. Sectio AI Informatica 8(1), 99–110 (2008)
Paraskevas, I., Chilton, E.: Audio classification using acoustic images for retrieval from multimedia databases. In: 4th EURASIP Conference on Video/Image Processing and Multimedia Communications. IEEE, vol. 1, pp. 187–192 (2003)
Paraskevas, I., Potirakis, S.M., Rangoussi, M.: Natural soundscapes and identification of environmental sounds: a pattern recognition approach. In: 16th International Conference on Digital Signal Processing, pp. 5–7, 1–6 July 2009
Pinkowski, B.: Principal component analysis of speech spectrogram images. Pattern Recognit. 30(5), 777–787 (1997)
Rabiner, L., Schafer, W.: Theory and Applications of Digital Speech Processing. Prentice-Hall, Englewood Cliffs (2010)
Rafii, Z., Coover, B., Han, J.: An audio fingerprinting system for live version identification using image processing techniques. In: IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), pp. 644–648 (2014)
Smith III, J.O.: Spectral Audio Processing. W3K Publishing, Stanford (2011)
Wichern, G., Xue, J., Thornburg, H., Mechtley, B., Spanias, A.: Segmentation, indexing, and retrieval for environmental and natural sounds. IEEE Trans. Audio Speech Lang. Process. 18(3), 688–707 (2010)
Yu, G., Slotine, J.: Audio classification from time-frequency texture. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP. Taipei, Taiwan, pp. 1677–1680 (2009)
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Maka, T., Forczmański, P. (2016). Environmental Sounds Recognition Based on Image Processing Methods. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_68
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DOI: https://doi.org/10.1007/978-3-319-26227-7_68
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