Self-localization in Ad Hoc Indoor Acoustic Networks

The increasing use of mobile technology in everyday life has aroused interest into developing new ways of utilizing the data collected by devices such as mobile phones and wearable devices. Acoustic sensors can be used to localize sound sources if the positions of spatially separate sensors are known or can be determined. However, the process of determining the 3D coordinates by manual measurements is tedious especially with increasing number of sensors. Therefore, the localization process has to be automated. Satellite based positioning is imprecise for many applications and requires line-of-sight to the sky. This thesis studies localization methods for wireless acoustic sensor networks and the process is called self-localization.This thesis focuses on self-localization from sound, and therefore the term acoustic is used. Furthermore, the development of the methods aims at utilizing ad hoc sensor networks, which means that the sensors are not necessarily installed in the premises like meeting rooms and other purpose-built spaces, which often have dedicated audio hardware for spatial audio applications. Instead of relying on such spaces and equipment, mobile devices are used, which are combined to form sensor networks.For instance, a few mobile phones laid on a table can be used to create a sensor network built for an event and it is inherently dismantled once the event is over, which explains the use of the term ad hoc. Once positions of the devices are estimated, the network can be used for spatial applications such as sound source localization and audio enhancement via spatial filtering. The main purpose of this thesis is to present the methods for self-localization of such an ad hoc acoustic sensor network. Using off-the-shelf ad hoc devices to establish sensor networks enables implementation of many spatial algorithms basically in any environment.Several acoustic self-localization methods have been introduced over the years. However, they often rely on specialized hardware and calibration signals. This thesis presents methods that are passive and utilize environmental sounds such as speech from which, by using time delay estimation, the spatial information of the sensor network can be determined. Many previous self-localization methods assume that audio captured by the sensors is synchronized. This assumption cannot be made in an ad hoc sensor network, since the different sensors are unaware of each other without specific signaling that is not available without special arrangement.The methods developed in this thesis are evaluated with simulations and real data recordings. Scenarios in which the targets of positioning are stationary and in motion are studied. The real world recordings are made in closed spaces such as meeting rooms. The targets are approximately 1 – 5 meters apart. The positioning accuracy is approximately five centimeters in a stationary scenario, and ten centimeters in a moving-target scenario on average. The most important result of this thesis is presenting the first self-localization method that uses environmental sounds and off-the-shelf unsynchronized devices, and allows the targets of self-localization to move

Simultaneous ranging and self-positioning in unsynchronized wireless acoustic sensor networks

Automatic ranging and self-positioning is a very desirable property in wireless acoustic sensor networks (WASNs) where nodes have at least one microphone and one loudspeaker. However, due to environmental noise, interference and multipath effects, audio-based ranging is a challenging task. This paper presents a fast ranging and positioning strategy that makes use of the correlation properties of pseudo-noise (PN) sequences for estimating simultaneously relative time-of-arrivals (TOAs) from multiple acoustic nodes. To this end, a proper test signal design adapted to the acoustic node transducers is proposed. In addition, a novel self-interference reduction method and a peak matching algorithm are introduced, allowing for increased accuracy in indoor environments. Synchronization issues are removed by following a BeepBeep strategy, providing range estimates that are converted to absolute node positions by means of multidimensional scaling (MDS). The proposed approach is evaluated both with simulated and real experiments under different acoustical conditions. The results using a real network of smartphones and laptops confirm the validity of the proposed approach, reaching an average ranging accuracy below 1 centimeter.This work was supported by the Spanish Ministry of Economy and Competitiveness under Grant TIN2015-70202-P, TEC2012-37945-C02-02 and FEDER funds

Acoustical Ranging Techniques in Embedded Wireless Sensor Networked Devices

Location sensing provides endless opportunities for a wide range of applications in GPS-obstructed environments; where, typically, there is a need for higher degree of accuracy. In this article, we focus on robust range estimation, an important prerequisite for fine-grained localization. Motivated by the promise of acoustic in delivering high ranging accuracy, we present the design, implementation and evaluation of acoustic (both ultrasound and audible) ranging systems.We distill the limitations of acoustic ranging; and present efficient signal designs and detection algorithms to overcome the challenges of coverage, range, accuracy/resolution, tolerance to Doppler’s effect, and audible intensity. We evaluate our proposed techniques experimentally on TWEET, a low-power platform purpose-built for acoustic ranging applications. Our experiments demonstrate an operational range of 20 m (outdoor) and an average accuracy 2 cm in the ultrasound domain. Finally, we present the design of an audible-range acoustic tracking service that encompasses the benefits of a near-inaudible acoustic broadband chirp and approximately two times increase in Doppler tolerance to achieve better performance

Implementation of an Autonomous Impulse Response Measurement System

Data collection is crucial for researchers, as it can provide important insights for describing phenomena. In acoustics, acoustic phenomena are characterized by Room Impulse Responses (RIRs) occurring when sound propagates in a room. Room impulse responses are needed in vast quantities for various reasons, including the prediction of acoustical parameters and the rendering of virtual acoustical spaces. Recently, mobile robots navigating within indoor spaces have become increasingly used to acquire information about its environment. However, little research has attempted to utilize robots for the collection of room acoustic data. This thesis presents an adaptable automated system to measure room impulse responses in multi-room environments, using mobile and stationary measurement platforms. The system, known as Autonomous Impulse Response Measurement System (AIRMS), is divided into two stages: data collection and post-processing. To automate data collection, a mobile robotic platform was developed to perform acoustic measurements within a room. The robot was equipped with spatial microphones, multiple loudspeakers and an indoor localization system, which reported real time location of the robot. Additionally, stationary platforms were installed in specific locations inside and outside the room. The mobile and stationary platforms wirelessly communicated with one another to perform the acoustical tests systematically. Since a major requirement of the system is adaptability, researchers can define the elements of the system according to their needs, including the mounted equipment and the number of platforms. Post-processing included extraction of sine sweeps and the calculation of impulse responses. Extraction of the sine sweeps refers to the process of framing every acoustical test signal from the raw recordings. These signals are then processed to calculate the room impulse responses. The automatically collected information was complemented with manually produced data, which included rendering of a 3D model of the room, a panoramic picture. The performance of the system was tested under two conditions: a single-room and a multiroom setting. Room impulse responses were calculated for each of the test conditions, representing typical characteristics of the signals and showing the effects of proximity from sources and receivers, as well as the presence of boundaries. This prototype produces RIR measurements in a fast and reliable manner. Although some shortcomings were noted in the compact loudspeakers used to produce the sine sweeps and the accuracy of the indoor localization system, the proposed autonomous measurement system yielded reasonable results. Future work could expand the amount of impulse response measurements in order to further refine the artificial intelligence algorithms

Self-Localization of Ad-Hoc Arrays Using Time Difference of Arrivals

This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/K007491/1

Deep Room Recognition Using Inaudible Echos

Recent years have seen the increasing need of location awareness by mobile applications. This paper presents a room-level indoor localization approach based on the measured room's echos in response to a two-millisecond single-tone inaudible chirp emitted by a smartphone's loudspeaker. Different from other acoustics-based room recognition systems that record full-spectrum audio for up to ten seconds, our approach records audio in a narrow inaudible band for 0.1 seconds only to preserve the user's privacy. However, the short-time and narrowband audio signal carries limited information about the room's characteristics, presenting challenges to accurate room recognition. This paper applies deep learning to effectively capture the subtle fingerprints in the rooms' acoustic responses. Our extensive experiments show that a two-layer convolutional neural network fed with the spectrogram of the inaudible echos achieve the best performance, compared with alternative designs using other raw data formats and deep models. Based on this result, we design a RoomRecognize cloud service and its mobile client library that enable the mobile application developers to readily implement the room recognition functionality without resorting to any existing infrastructures and add-on hardware. Extensive evaluation shows that RoomRecognize achieves 99.7%, 97.7%, 99%, and 89% accuracy in differentiating 22 and 50 residential/office rooms, 19 spots in a quiet museum, and 15 spots in a crowded museum, respectively. Compared with the state-of-the-art approaches based on support vector machine, RoomRecognize significantly improves the Pareto frontier of recognition accuracy versus robustness against interfering sounds (e.g., ambient music).Comment: 29 page

A survey on acoustic positioning systems for location-based services

Positioning systems have become increasingly popular in the last decade for location-based services, such as navigation, and asset tracking and management. As opposed to outdoor positioning, where the global navigation satellite system became the standard technology, there is no consensus yet for indoor environments despite the availability of different technologies, such as radio frequency, magnetic field, visual light communications, or acoustics. Within these options, acoustics emerged as a promising alternative to obtain high-accuracy low-cost systems. Nevertheless, acoustic signals have to face very demanding propagation conditions, particularly in terms of multipath and Doppler effect. Therefore, even if many acoustic positioning systems have been proposed in the last decades, it remains an active and challenging topic. This article surveys the developed prototypes and commercial systems that have been presented since they first appeared around the 1980s to 2022. We classify these systems into different groups depending on the observable that they use to calculate the user position, such as the time-of-flight, the received signal strength, or the acoustic spectrum. Furthermore, we summarize the main properties of these systems in terms of accuracy, coverage area, and update rate, among others. Finally, we evaluate the limitations of these groups based on the link budget approach, which gives an overview of the system's coverage from parameters such as source and noise level, detection threshold, attenuation, and processing gain.Agencia Estatal de InvestigaciónResearch Council of Norwa