Spectral analysis techniques for acoustic fingerprints recognition
Eduardo E. Zurek, Margarita Gamarra, G. Jose R. Escorcia, Carlos Gutierrez, Henry Bayona, Roxana Perez, Xavier Garcia · 2014
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.