ARTIFICIAL NEURAL NETWORK APPLICATION FOR THE TEMPORAL PROPERTIES OF ACOUSTIC PERCEPTION

Miloš Simonović, Marko Kovandžić, Vlastimir Nikolić, Mihajlo Stojčić, Darko Knežević

DOI Number
https://doi.org/10.22190/FUME190415036S
First page
309
Last page
320

Abstract


Though acoustic perception is well established in literature, it seems to be insufficiently implemented in practice. Experimental results are excellent but a lot of issues arise when it comes to the application in real conditions. Using artificial neural networks makes acoustic signal processing very comfortable from the mathematical point of view. However, a great job has to be done in order to make it possible. The procedure includes data acquisition, filtering, feature extraction and selection. These techniques require much more resources than mere artificial neural networks and this represents a limiting factor for the implementation. The paper investigates the complete procedure of acoustic perception, in terms of time, in order to identify limitations.


Keywords

Perception, Temporal Properties, Localization, Filtering, Neural Networks

Full Text:

PDF

References


McLoughlin, I., Zhang, H., Xie, Z., Song, Y., Xiao, W., Phan, H., 2017, Continuous robust sound event classification using time-frequency features and deep learning, Plos ONE, 12(9), pp 1-19.

Mcadams, S., 1993, Recognition of sound sources and events, Oxford University Press.

Bishop, C., 1995, Neural networks for pattern recognition, Oxford: Oxford University Press.

Michaelides, P.G., Tsionas, E. G., Vouldis, A. T., Konstantakis, K. N., Patrinos, P., 2018, A Semi-Parametric Non-linear Neural Network Filter: Theory and Empirical Evidence, Computational Economics, 51(3), pp 637-675.

Choi, J.Y., Hu, E.R., Perrachione, T.K., 2018, Varying acoustic-phonemic ambiguity reveals that talker normalization is obligatory in speech processing, Attention, Perception & Psychophysics, 80(3), pp. 784-797.

Flasinski, M., 2016, Introduction to Artificial Intelligence, Springer, Cham.

Osamu, I., Masafumi, T., Tetsuya, N., 2003, Sound Source Localization Using a Pinna-based Profile Fitting Method, Ieice Transactions - IEICE, pp. 263-266.

Johnson, M. L., 2015, Systems and methods of processing information regarding weapon fire location using projectile shockwave and muzzle blast times of arrival data, Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=edspgr&AN=edspgr.08995227&site=eds-live (last access: 01.03.2019).

Rowell, C.R., 2014, Three-Dimensional Volcano-Acoustic Source Localization at Karymsky Volcano, Kamchatka, Russia, Journal of Volcanology and Geothermal Research, 283, pp. 101-115.

Martín, S. R., Genescà, M., Romeu, J., Clot, A., 2016, Aircraft localization using a passive acoustic method. Experimental test, Aerospace Science and Technology, 48, pp. 246-253.

Grabowski, K., 2016, Time–distance domain transformation for Acoustic Emission source localization in thin metallic plates, Ultrasonics, 68, pp. 142–149.

Tan, C., 2016, A low-cost centimeter-level acoustic localization system without time synchronization, Measurement, 78, pp. 73–82.

Kovandžić, M., Nikolić, V., Al-Noori, A., Ćirić, I., Simonović, M., 2017, Near field acoustic localization under unfavorable conditions using feedforward neural network for processing time difference of arrival, Expert Systems with Applications, 7(1), pp 138-146.

Park, C., Jeon, J., Kim, Y., 2014, Localization of a sound source in a noisy environment by hyperbolic curves in quefrency domain, Journal Of Sound And Vibration, 333, pp. 5630-5640.

Hing, C.S., 2005, A comparative study of two discrete-time phase delay estimators, IEEE Transactions on Instrumentation and Measurement, 54, pp. 2501-2504.

Khaddour, H., 2011, A Comparison of Algorithms of Sound Source Localization Based on Time Delay Estimation, Elektrorevue, 2(1), pp. 31-37.

Babic, M., Calì, M., Nazarenko, I. et al., 2019, Surface roughness evaluation in hardened materials by pattern recognition using network theory, International Journal on Interactive Design and Manufacturing, 13(1), pp. 211-219.

Fragassa, C., Babic, M., Bergmann, C., Minak, G., 2019, Predicting the tensile behaviour of cast alloys by a pattern recognition analysis on experimental data, Metals, 9(5), 557.

Rojas, R., 1996, Neural Networks, Springer.

George, I., Cousillas, H., Richard, J., Hausberger, M., 2008, A Potential Neural Substrate for Processing Functional Classes of Complex Acoustic Signals, Plos ONE, 3(5), pp 1-10.

Smith, W. S., 1997, Digital signal processing, California Technical Publishing. San Diego.

Dhull, S., Arya, S., Sahu, O.P., 2010, Comparison of time-delay estimation techniques in acoustic environment, International Journal of Computer Applications, 8(9), pp 29–31.




DOI: https://doi.org/10.22190/FUME190415036S

Refbacks

  • There are currently no refbacks.


ISSN: 0354-2025 (Print)

ISSN: 2335-0164 (Online)

COBISS.SR-ID 98732551

ZDB-ID: 2766459-4