Blind as a bat: audible echolocation on small robots

For safe and efficient operation, mobile robots need to perceive their environment, and in particular, perform tasks such as obstacle detection, localization, and mapping. Although robots are often equipped with microphones and speakers, the audio modality is rarely used for these tasks. Compared to the localization of sound sources, for which many practical solutions exist, algorithms for active echolocation are less developed and often rely on hardware requirements that are out of reach for small robots. We propose an end-to-end pipeline for sound-based localization and mapping that is targeted at, but not limited to, robots equipped with only simple buzzers and low-end microphones. The method is model-based, runs in real time, and requires no prior calibration or training. We successfully test the algorithm on the e-puck robot with its integrated audio hardware, and on the Crazyflie drone, for which we design a reproducible audio extension deck. We achieve centimeter-level wall localization on both platforms when the robots are static during the measurement process. Even in the more challenging setting of a flying drone, we can successfully localize walls, which we demonstrate in a proof-of-concept multi-wall localization and mapping demo.Comment: 8 pages, 10 figures, published in IEEE Robotics and Automation Letter

Proceedings of the EAA Spatial Audio Signal Processing symposium: SASP 2019

International audienc

Sensors and Systems for Indoor Positioning

This reprint is a reprint of the articles that appeared in Sensors' (MDPI) Special Issue on “Sensors and Systems for Indoor Positioning". The published original contributions focused on systems and technologies to enable indoor applications

Acoustic Sensing: Mobile Applications and Frameworks

Acoustic sensing has attracted significant attention from both academia and industry due to its ubiquity. Since smartphones and many IoT devices are already equipped with microphones and speakers, it requires nearly zero additional deployment cost. Acoustic sensing is also versatile. For example, it can detect obstacles for distracted pedestrians (BumpAlert), remember indoor locations through recorded echoes (EchoTag), and also understand the touch force applied to mobile devices (ForcePhone). In this dissertation, we first propose three acoustic sensing applications, BumpAlert, EchoTag, and ForcePhone, and then introduce a cross-platform sensing framework called LibAS. LibAS is designed to facilitate the development of acoustic sensing applications. For example, LibAS can let developers prototype and validate their sensing ideas and apps on commercial devices without the detailed knowledge of platform-dependent programming. LibAS is shown to require less than 30 lines of code in Matlab to implement the prototype of ForcePhone on Android/iOS/Tizen/Linux devices.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143971/1/yctung_1.pd

A Survey of Sound Source Localization Methods in Wireless Acoustic Sensor Networks

Wireless acoustic sensor networks (WASNs) are formed by a distributed group of acoustic-sensing devices featuring audio playing and recording capabilities. Current mobile computing platforms offer great possibilities for the design of audio-related applications involving acoustic-sensing nodes. In this context, acoustic source localization is one of the application domains that have attracted the most attention of the research community along the last decades. In general terms, the localization of acoustic sources can be achieved by studying energy and temporal and/or directional features from the incoming sound at different microphones and using a suitable model that relates those features with the spatial location of the source (or sources) of interest. This paper reviews common approaches for source localization in WASNs that are focused on different types of acoustic features, namely, the energy of the incoming signals, their time of arrival (TOA) or time difference of arrival (TDOA), the direction of arrival (DOA), and the steered response power (SRP) resulting from combining multiple microphone signals. Additionally, we discuss methods not only aimed at localizing acoustic sources but also designed to locate the nodes themselves in the network. Finally, we discuss current challenges and frontiers in this field

Localization using Distance Geometry : Minimal Solvers and Robust Methods for Sensor Network Self-Calibration

In this thesis, we focus on the problem of estimating receiver and sender node positions given some form of distance measurements between them. This kind of localization problem has several applications, e.g., global and indoor positioning, sensor network calibration, molecular conformations, data visualization, graph embedding, and robot kinematics. More concretely, this thesis makes contributions in three different areas.First, we present a method for simultaneously registering and merging maps. The merging problem occurs when multiple maps of an area have been constructed and need to be combined into a single representation. If there are no absolute references and the maps are in different coordinate systems, they also need to be registered. In the second part, we construct robust methods for sensor network self-calibration using both Time of Arrival (TOA) and Time Difference of Arrival (TDOA) measurements. One of the difficulties is that corrupt measurements, so-called outliers, are present and should be excluded from the model fitting. To achieve this, we use hypothesis-and-test frameworks together with minimal solvers, resulting in methods that are robust to noise, outliers, and missing data. Several new minimal solvers are introduced to accommodate a range of receiver and sender configurations in 2D and 3D space. These solvers are formulated as polynomial equation systems which are solvedusing methods from algebraic geometry.In the third part, we focus specifically on the problems of trilateration and multilateration, and we present a method that approximates the Maximum Likelihood (ML) estimator for different noise distributions. The proposed approach reduces to an eigendecomposition problem for which there are good solvers. This results in a method that is faster and more numerically stable than the state-of-the-art, while still being easy to implement. Furthermore, we present a robust trilateration method that incorporates a motion model. This enables the removal of outliers in the distance measurements at the same time as drift in the motion model is canceled

From active to passive spatial acoustic sensing and applications

The active acoustic sensing system emits modulated acoustic waves and analyzes reflection signals. It is dominant in acoustic spatial sensing. On the other side, the passive acoustic sensing system receives and investigates nature sounds directly. It is good at semantic tasks but has weak performance on spatial sensing. In this dissertation, we manage to bridge three gaps in existing systems. They are the gap between the assumption of signal processing algorithms and the real acoustic environment, the gap between powerful active spatial sensing and limited passive spatial sensing, and the gap between the semantic features and spatial information. We evolve the acoustic sensing system design and extend the functionalities by three novel systems. First, we develop a fully active spatial sensing system DeepRange which can adapt to the real environment easily. We develop an effective mechanism to generate synthetic training data that captures noise, speaker/mic distortion, and interference in the signals. It removes the need of collecting a large volume of data. We then design a deep range neural network (DRNet) to estimate the distance from raw acoustic signals. It is inspired by signal processing that an ultra-long convolution kernel size helps to combat noise and interference. The model is fully trained over synthetic data, but it can achieve sub-centimeter error robustly in real data despite various environments, background noise, interference, and mobile phone models. Second, we develop a fused active and passive spatial sensing system for speech separation noted as Spatial Aware Multi-task learning-based Separation (SAMS). We leverage both active sensing and passive sensing to improve AoA estimation and jointly optimize the semantic task and the spatial task. SAMS estimates the spatial location and extracts speech for the target user during teleconferencing simultaneously. We first generate fine-grained spatial embeddings from the user’s voice and inaudible tracking sound, which contains the user’s position and rich multipath information. Furthermore, we develop a deep neural network with multi-task learning to jointly optimize source separation and location. We significantly speed up inference to provide a real-time guarantee. Finally, we deeply fuse the semantic features and spatial cues to combat the interference and noise in the real environment as well as enable depth sensing in a fully passive setup. Inspired by the ”flash-to-bang” phenomenon (i.e.hearing the thunder after seeing the lightning), we propose FBDepth to measure the depth of the sound source. We formulate the problem as an audio-visual event localization task for collision events. Specifically, FBDepth first aligns correspondence between the video track and audio track to locate the target object and target sound in a coarse granularity. Based on the observation of moving objects’ trajectories, it proposes to estimate the intersection of optical flow before and after the collision to locate video events in time. It feeds the estimated timestamp of the video event and the other modalities for the final depth estimation. We use a mobile phone to collect the 3.6K+ video clips involving 24 different objects at up to 60m. FBDepth shows superior performance especially at a long range compared to monocular and stereo methods.Computer Science

Effects of errorless learning on the acquisition of velopharyngeal movement control

Session 1pSC - Speech Communication: Cross-Linguistic Studies of Speech Sound Learning of the Languages of Hong Kong (Poster Session)The implicit motor learning literature suggests a benefit for learning if errors are minimized during practice. This study investigated whether the same principle holds for learning velopharyngeal movement control. Normal speaking participants learned to produce hypernasal speech in either an errorless learning condition (in which the possibility for errors was limited) or an errorful learning condition (in which the possibility for errors was not limited). Nasality level of the participants’ speech was measured by nasometer and reflected by nasalance scores (in %). Errorless learners practiced producing hypernasal speech with a threshold nasalance score of 10% at the beginning, which gradually increased to a threshold of 50% at the end. The same set of threshold targets were presented to errorful learners but in a reversed order. Errors were defined by the proportion of speech with a nasalance score below the threshold. The results showed that, relative to errorful learners, errorless learners displayed fewer errors (50.7% vs. 17.7%) and a higher mean nasalance score (31.3% vs. 46.7%) during the acquisition phase. Furthermore, errorless learners outperformed errorful learners in both retention and novel transfer tests. Acknowledgment: Supported by The University of Hong Kong Strategic Research Theme for Sciences of Learning © 2012 Acoustical Society of Americapublished_or_final_versio