The NTAV/SPA 2012 conference was held on 27–29th September 2012 and was organized by the Institute of Electronics, Lodz University of Technology (www.eletel.p.lodz.pl) with the support of the IEEE Polish Section Region 8, Polish Section of the Audio Engineering Society, Department of Acoustics, Wroclaw University of Technology and the Division of Signal Processing and Electronic Systems, Poznan University of Technology.

Sonification is defined as presentation of information by means of non-speech audio. In assistive technologies for the blind, sonification is most often used in electronic travel aids (ETAs) - devices which aid in independent mobility through obstacle detection or help in orientation and navigation. The presented review contains an authored classification of various sonification schemes implemented in the most widely known ETAs. The review covers both those commercially available and those in various stages of research, according to the input used, level of signal processing algorithm used and sonification methods. Additionally, a sonification approach developed in the Naviton project is presented. The prototype utilizes stereovision scene reconstruction, obstacle and surface segmentation and spatial HRTF filtered audio with discrete musical sounds and was successfully tested in a pilot study with blind volunteers in a controlled environment, allowing to localize and navigate around obstacles.

This review article is concerned with metamaterials, i.e. specifically engineered structures with special properties for interaction with sounds. The research on and practical design of these materials have gained momentum in the last decade, when 3D printing techniques provided the possibility to fabricate such geometrically complex structures. We briefly describe the history of research on AMMs and group them into active and passive metamaterials. For each of these groups of AMMs, we discuss the most notable construction achievements and outline the main applications. We conclude this review with a discussion of possible directions for further research and main applications of AMMs such as noise attenuation, acoustic lens, and the cloaking phenomenon.

Keypoint detection is a basic step in many computer vision algorithms aimed at recognition of objects, automatic navigation and analysis of biomedical images. Successful implementation of higher level image analysis tasks, however, is conditioned by reliable detection of characteristic image local regions termed keypoints. A large number of keypoint detection algorithms has been proposed and verified. In this paper we discuss the most important keypoint detection algorithms. The main part of this work is devoted to description of a keypoint detection algorithm we propose that incorporates depth information computed from stereovision cameras or other depth sensing devices. It is shown that filtering out keypoints that are context dependent, e.g. located at boundaries of objects can improve the matching performance of the keypoints which is the basis for object recognition tasks. This improvement is shown quantitatively by comparing the proposed algorithm to the widely accepted SIFT keypoint detector algorithm. Our study is motivated by a development of a system aimed at aiding the visually impaired in space perception and object identification.

Monitoring head movements is important in many aspects of life from medicine and rehabilitation to sports, and VR entertainment. In this study, we used recordings from two sensors, i.e. an accelerometer and a gyroscope, to calculate the angles of movement of the gesturing person’s head. For the yaw motion, we proposed an original algorithm using only these two inertial sensors and the detected motion type obtained from a pre-trained SVM classifier. The combination of the gyroscope data and the detected motion type allowed us to calculate the yaw angle without the need for other sensors, such as a magnetometer or a video camera. To verify the accuracy of our algorithm, we used a robotic arm that simulated head gestures where the angle values were read out from the robot kinematics. The calculated yaw angles differed from the robot’s readings with a mean absolute error of approx. 1 degree and the rate of differences between these values exceeding 5 degrees was significantly below 1 percent except for one outlier at 1.12%. This level of accuracy is sufficient for many applications, such as VR systems, human-system interfaces, or rehabilitation.

An electronic system and an algorithm for estimating pedestrian geographic location in urban terrain is reported in the paper. Different sources of kinematic and positioning data are acquired (i.e.: accelerometer, gyroscope, GPS receiver, raster maps of terrain) and jointly processed by a Monte-Carlo simulation algorithm based on the particle filtering scheme. These data are processed and fused to estimate the most probable geographical location of the user. A prototype system was designed, built and tested with a view to aiding blind pedestrians. It was shown in the conducted field trials that the method yields superior results to sole GPS readouts. Moreover, the estimated location of the user can be effectively sustained when GPS fixes are not available (e.g. tunnels).

The presented review discusses recent research on human echolocation by blind and sighted subjects, aiming to classify and evaluate the methodologies most commonly used when testing active echolocation methods. Most of the reviewed studies compared small groups of both blind and sighted volunteers, although one in four studies used sighted testers only. The most common trial procedure was for volunteers to detect or localize static obstacles, e.g., discs, boards, or walls at distances ranging from a few centimeters to several meters. Other tasks also included comparing or categorizing objects. Few studies utilized walking in real or virtual environments. Most trials were conducted in natural acoustic conditions, as subjects are marginally less likely to correctly echolocate in anechoic or acoustically dampened rooms. Aside from live echolocation tests, other methodologies included the use of binaural recordings, artificial echoes or rendered virtual audio. The sounds most frequently used in the tests were natural sounds such as the palatal mouth click and finger snapping. Several studies have focused on the use of artificially generated sounds, such as noise or synthetic clicks. A promising conclusion from all the reviewed studies is that both blind and sighted persons can efficiently learn echolocation.