Texture Classification Using Spectral Entropy of Acoustic Signal Generated by a Human Echolocator

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.

Characterizing Complex Spatiotemporal Patterns from Entropy Measures

“…The silhouette score is a criterion for defining if a set has been well clusterized [ 23 ]. Given an element in a cluster , this metric is computed as follows [ 3 , 24 ]: where is the average dissimilarity, which is the average distance of to all other elements in the cluster , and is the average distance to the elements of other clusters. The greater the value, the better performance of the clustering algorithm because it has produced groups with low dissimilarities and large distances between clusters.…”

“…The silhouette score is a criterion for defining if a set has been well clusterized [23]. Given an element x i in a cluster π k , this metric is computed as follows [3,24]:…”

“…Entropy, derived from the probability distribution of the states of a process or system, can be interpreted as a quantitative measure of randomness or disorder, offering deep insights into the underlying dynamics of several complex systems (see, for instance, Refs. [ 1 , 2 , 3 , 4 , 5 , 6 ]).…”

Characterizing Spatiotemporal Complex Patterns from Entropy Measures

“…The silhouette score is a criterion for defining if a set has been well-clusterized [34]. Given an element x i in a cluster π k , this metric is computed as follows [11,12]:…”

“…Entropy, derived from the probability distribution of the states of a process or system, can be interpreted as a quantitative measure of randomness or disorder, offering deep insights into the underlying dynamics of several complex systems (see, for instance, Refs. [2,7,12,[23][24][25]).…”