entropy Article Texture Classification Using Spectral Entropy of Acoustic Signal Generated by a Human Echolocator Raja Syamsul Azmir Raja Abdullah 1,*, Nur Luqman Saleh 1, Sharifah Mumtazah Syed Abdul Rahman 1 , Nur Syazmira Zamri 1 and Nur Emileen Abdul Rashid 2 1 Wireless and Photonic Network Research Centre (WiPNET), Faculty of Engineering, University Putra Malaysia (UPM), Serdang 43400, Malaysia; [email protected] (N.L.S.); [email protected] (S.M.S.A.R.); [email protected] (N.S.Z.) 2 Microwave Research Institute, UniversitiTeknologi MARA (UiTM), Shah Alam 40450, Selangor, Malaysia; [email protected] * Correspondence: [email protected]; Tel.: +603-976-943-47 Received: 12 August 2019; Accepted: 29 September 2019; Published: 2 October 2019 Abstract: Human echolocation is a biological process wherein the human emits a punctuated acoustic signal, and the ear analyzes the echo in order to perceive the surroundings. The peculiar acoustic signal is normally produced by clicking inside the mouth. This paper utilized this unique acoustic signal from a human echolocator as a source of transmitted signal in a synthetic human echolocation technique. Thus, the aim of the paper was to extract information from the echo signal and develop a classification scheme to identify signals reflected from different textures at various distance. The scheme was based on spectral entropy extracted from Mel-scale filtering output in the Mel-frequency cepstrum coefficient of a reflected echo signal. The classification process involved data mining, features extraction, clustering, and classifier validation. The reflected echo signals were obtained via an experimental setup resembling a human echolocation scenario, configured for synthetic data collection. Unlike in typical speech signals, extracted entropy from the formant characteristics was likely not visible for the human mouth-click signals. Instead, multiple peak spectral features derived from the synthesis signal of the mouth-click were assumed as the entropy obtained from the Mel-scale filtering output. To realize the classification process, K-means clustering and K-nearest neighbor processes were employed. Moreover, the impacts of sound propagation toward the extracted spectral entropy used in the classification outcome were also investigated. The outcomes of the classifier performance herein indicated that spectral entropy is essential for human echolocation. Keywords: spectral entropy; acoustic signal; human echolocation; classification; MFCC 1. Introduction The phrase “echolocation” was initially used by Griffin to describe bats’ ability to safely navigate and locate their prey using ultrasound call signals [1]. What is less known is that a group of people (often blind people) known as human echolocators have adapted to visualize their surrounding using a similar concept. Human echolocation is the ability to perceive one’s surroundings by listening to the echoes of the active emission of sound signals reflected from an obstacle. Recent studies have reported that these people are able to visualize their surrounding by “seeing through sound”. They exhibit exceptional performance in defining their space, even able to accurately discriminate the profile of objects. This ability has elicited inquiries among scholars on how the space and objects can be recognized using mouth-click signals. Although a series of studies have focused on this question, most works have focused on the perceptual concept rather than technical explanations. It is known that Entropy 2019, 21, 963; doi:10.3390/e21100963 www.mdpi.com/journal/entropy Entropy 2019, 21, 963 2 of 20 Entropy 2019, 21, 963 2 of 20 orderhuman to translate echolocators meaningful depend cues on theirfrom auditorymouth-click systems signals in and order turn to them translate into meaningfulvisual perception, cues from as illustratedmouth-click in Figure signals 1 and[1]. turn them into visual perception, as illustrated in Figure1[1]. FigureFigure 1. 1.TheThe human human echolocation echolocation concept. concept. It Itis isessential essential for for humans humans to to recognize recognize the the ac acousticoustic signals signals entering entering their their ear, ear, mainly mainly for for communicationcommunication and and recognition. recognition. 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