DoA Reliability for Distributed Acoustic Tracking

Abstract

Distributed acoustic tracking estimates the trajectories of source positions using an acoustic sensor network. As it is often difficult to estimate the source-sensor range from individual nodes, the source positions have to be inferred from the direction-of-arrival (DoA) estimates. Due to reverberation and noise, the sound field becomes increasingly diffuse with increasing source-sensor distance, leading to a decreased Direction of Arrival (DoA)-estimation accuracy. To distinguish between accurate and uncertain DoA estimates, this letter proposes to incorporate the coherent-to-diffuse ratio as a measure of DoA reliability for singlesource tracking. It is shown that the source positions, therefore, can be probabilistically triangulated by exploiting the spatial diversity of all nodes.

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

“…Analyzing the papers in Table S3, it can be observed that, according to the authors of these papers, all of the studies focused on smart homes. The authors of these scientific articles made use in their analyses of different types of sensors, including: biomedical sensors [11]; ambient data sensors [11,34,68]; acoustic sensor networks [67]; WiFi-enabled sensors [36]; Passive Infrared (PIR) sensors [30,34]; binary sensors [31,69]; and motion sensors [30,70].…”

“…Meanwhile, several of the above-mentioned scientific papers that use the Naïve Bayes integrated with sensor devices in Smart Buildings also addressed issues regarding assisted living [11,30,31,34,36]. Other reasons for applying the Naïve Bayes method with sensors in smart buildings include obtaining accurate information regarding the positions of surrounding objects, an aspect especially useful for autonomous systems and smart devices [67] or in developing an Internet of Things (IoT)-based fully automated nutrition monitoring system [36].…”

“…With respect to the devised methods, in [11], the authors made use of a hybrid approach based on the Naïve Bayes (NB) Algorithm and the Whale Optimization Algorithm (WOA), subsequently presenting a comparison among six classifiers: Decision tree (J48), Random Forest (RF), Ripper (JRip), Naïve Bayes (NB), Nearest Neighbor (IBK), Support Vector Machine (SVM). In [67], the authors implemented the Bayesian filter in order to estimate the trajectories of source positions using an acoustic sensor network. In [68], a comparison of the supervised learning models was presented: Naïve Bayes (NB), C4.5 Decision Tree, Logistic Regression, K-Nearest Neighbor, and Random Forest were used in order to detect and estimate occupancy in smart homes.…”

Tracking Multiple Audio Sources With the von Mises Distribution and Variational EM

“…In this case source directions should however be modeled as circular random variables, e.g. the wrapped Gaussian distribution [5], or the von Mises distribution [6], [7].…”

Static Visual Spatial Priors for DoA Estimation

“…We report both average errors, as well as average bin accuracies defined as a percentage of predictions falling into a bin of assumed width on either side of the ground truth DoA (±5 degrees unless stated otherwise). DoAs with automatically extracted visual priors reduced average errors by an absolute 15.5 • and increased bin accuracy by16.2% relative (on Dev and Test sets, note CV priors are not available for Syn.). This effect was relatively stronger for Test-Noise condition, where automatically derived priors offered 25.9% rel.…”