Auto-Calibrated MIMO-OFDM Channel Sounder for 3D Spatial Channel

This paper presents an improved test-bed designed for analyzing the spatial behaviour of wideband indoor channels using MIMO-OFDM systems. A 3D antenna positioning system (3-DAPS) is specifically designed for obtaining 3D spatial data. Also, it allows carrying out some measurements in the range of correlation distance where fading of the radio channel link is significant for indoor scenarios. Special emphasize is made on the RF calibration module, which is designed to track the frequency response of all RF chain of transmitter and receiver and apply those responses to the channel measurements. The average of the pseudo-spectrum MUSIC over all frequencies improves the resolution of the spatial spectrum giving clear peaks where a signal source is and smoothing fake peaks coming from scatters

Advances in Architectural Acoustics

Satisfactory acoustics is crucial for the ability of spaces such as auditoriums and lecture rooms to perform their primary function. The acoustics of dwellings and offices greatly affects the quality of our life, since we are all consciously or subconsciously aware of the sounds to which we are daily subjected. Architectural acoustics, which encompasses room and building acoustics, is the scientific field that deals with these topics and can be defined as the study of generation, propagation, and effects of sound in enclosures. Modeling techniques, as well as related acoustic theories for accurately calculating the sound field, have been the center of many major new developments. In addition, the image conveyed by a purely physical description of sound would be incomplete without regarding human perception; hence, the interrelation between objective stimuli and subjective sensations is a field of important investigations. A holistic approach in terms of research and practice is the optimum way for solving the perplexing problems which arise in the design or refurbishment of spaces, since current trends in contemporary architecture, such as transparency, openness, and preference for bare sound-reflecting surfaces are continuing pushing the very limits of functional acoustics. All the advances in architectural acoustics gathered in this Special Issue, we hope that inspire researchers and acousticians to explore new directions in this age of scientific convergence

Super-Resolution TOA Estimation with Diversity Techniques for Indoor Geolocation Applications

Recently, there are great interests in the location-based applications and the location-awareness of mobile wireless systems in indoor areas, which require accurate location estimation in indoor environments. The traditional geolocation systems such as the GPS are not designed for indoor applications, and cannot provide accurate location estimation in indoor environments. Therefore, there is a need for new location finding techniques and systems for indoor geolocation applications. In this thesis, a wide variety of technical aspects and challenging issues involved in the design and performance evaluation of indoor geolocation systems are presented first. Then the TOA estimation techniques are studied in details for use in indoor multipath channels, including the maximum-likelihood technique, the MUSIC super-resolution technique, and diversity techniques as well as various issues involved in the practical implementation. It is shown that due to the complexity of indoor radio propagation channels, dramatically large estimation errors may occur with the traditional techniques, and the super-resolution techniques can significantly improve the performance of the TOA estimation in indoor environments. Also, diversity techniques, especially the frequency-diversity with the CMDCS, can further improve the performance of the super-resolution techniques

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

International audienc

Sound Event Localization, Detection, and Tracking by Deep Neural Networks

In this thesis, we present novel sound representations and classification methods for the task of sound event localization, detection, and tracking (SELDT). The human auditory system has evolved to localize multiple sound events, recognize and further track their motion individually in an acoustic environment. This ability of humans makes them context-aware and enables them to interact with their surroundings naturally. Developing similar methods for machines will provide an automatic description of social and human activities around them and enable machines to be context-aware similar to humans. Such methods can be employed to assist the hearing impaired to visualize sounds, for robot navigation, and to monitor biodiversity, the home, and cities. A real-life acoustic scene is complex in nature, with multiple sound events that are temporally and spatially overlapping, including stationary and moving events with varying angular velocities. Additionally, each individual sound event class, for example, a car horn can have a lot of variabilities, i.e., different cars have different horns, and within the same model of the car, the duration and the temporal structure of the horn sound is driver dependent. Performing SELDT in such overlapping and dynamic sound scenes while being robust is challenging for machines. Hence we propose to investigate the SELDT task in this thesis and use a data-driven approach using deep neural networks (DNNs). The sound event detection (SED) task requires the detection of onset and offset time for individual sound events and their corresponding labels. In this regard, we propose to use spatial and perceptual features extracted from multichannel audio for SED using two different DNNs, recurrent neural networks (RNNs) and convolutional recurrent neural networks (CRNNs). We show that using multichannel audio features improves the SED performance for overlapping sound events in comparison to traditional single-channel audio features. The proposed novel features and methods produced state-of-the-art performance for the real-life SED task and won the IEEE AASP DCASE challenge consecutively in 2016 and 2017. Sound event localization is the task of spatially locating the position of individual sound events. Traditionally, this has been approached using parametric methods. In this thesis, we propose a CRNN for detecting the azimuth and elevation angles of multiple temporally overlapping sound events. This is the first DNN-based method performing localization in complete azimuth and elevation space. In comparison to parametric methods which require the information of the number of active sources, the proposed method learns this information directly from the input data and estimates their respective spatial locations. Further, the proposed CRNN is shown to be more robust than parametric methods in reverberant scenarios. Finally, the detection and localization tasks are performed jointly using a CRNN. This method additionally tracks the spatial location with time, thus producing the SELDT results. This is the first DNN-based SELDT method and is shown to perform equally with stand-alone baselines for SED, localization, and tracking. The proposed SELDT method is evaluated on nine datasets that represent anechoic and reverberant sound scenes, stationary and moving sources with varying velocities, a different number of overlapping sound events and different microphone array formats. The results show that the SELDT method can track multiple overlapping sound events that are both spatially stationary and moving

The Analysis of Sophisticated Direction of Arrival Estimation Methods in Passive Coherent Locators

In passive coherent locators (PCL) systems, noise and the precision of direction of arrival (DOA) estimation are key issues. This thesis addresses the implementation of sophisticated DOA estimation methods, in particular the multiple signal classification (MUSIC) algorithm, the conventional beam forming (CBF) algorithm, and the algebraic constant modulus algorithm (ACMA). The goal is to compare the ACMA to the MUSIC, and CBF algorithms for application to PCL. The results and analysis presented here support the use of constant modulus information, where available, as an important addition to DOA estimation. The ACMA offers many simple solutions to noise and separation related problems; at low SNR levels, it provides much more accurate estimates and yields reasonable separation performance even in the presence of challenging signals. Differential ACMA, which allows the simple digital removal of the direct signal component from the output of a sensor array, is also introduced