Spectral analysis techniques for acoustic fingerprints recognition

Title	Spectral analysis techniques for acoustic fingerprints recognition
Publication Type	Conference Paper
Year of Publication	2014
Authors	Zurek, E.E., Gamarra, A.M.R., Escorcia, G.J.R., Gutierrez, C., Bayona, H., Perez, R., Garcia, X.
Conference Name	Image, Signal Processing and Artificial Vision (STSIVA), 2014 XIX Symposium on
Date Published	Sept
Keywords	Acoustic Fingerprint, acoustic fingerprints recognition, acoustic noise, Acoustic signal processing, Acoustics, ANN, artificial neural network, Artificial neural networks, audio signal, audio signals, Boats, feature extraction, FFT, filtering system, fingerprint identification, Fingerprint recognition, Finite impulse response filters, frequency 60 Hz, k-nearest neighbors, KNN, neural nets, noise reduction, noise source, PCA, principal component analysis, principal components analysis, signal spectral characteristics, Spectral analysis, Spectrogram, vessel recognition
Abstract	This article presents results of the recognition process of acoustic fingerprints from a noise source using spectral characteristics of the signal. Principal Components Analysis (PCA) is applied to reduce the dimensionality of extracted features and then a classifier is implemented using the method of the k-nearest neighbors (KNN) to identify the pattern of the audio signal. This classifier is compared with an Artificial Neural Network (ANN) implementation. It is necessary to implement a filtering system to the acquired signals for 60Hz noise reduction generated by imperfections in the acquisition system. The methods described in this paper were used for vessel recognition.
URL	http://ieeexplore.ieee.org/document/7010154/
DOI	10.1109/STSIVA.2014.7010154
Citation Key	7010154