Calibration of sound source localisation for robots using multiple adaptive filter models of the cerebellum

The aim of this research was to investigate the calibration of Sound Source Localisation (SSL) for robots using the adaptive filter model of the cerebellum and how this could be automatically adapted for multiple acoustic environments. The role of the cerebellum has mainly been identified in the context of motor control, and only in recent years has it been recognised that it has a wider role to play in the senses and cognition. The adaptive filter model of the cerebellum has been successfully applied to a number of robotics applications but so far none involving auditory sense. Multiple models frameworks such as MOdular Selection And Identification for Control (MOSAIC) have also been developed in the context of motor control, and this has been the inspiration for adaptation of audio calibration in multiple acoustic environments; again, application of this approach in the area of auditory sense is completely new. The thesis showed that it was possible to calibrate the output of an SSL algorithm using the adaptive filter model of the cerebellum, improving the performance compared to the uncalibrated SSL. Using an adaptation of the MOSAIC framework, and specifically using responsibility estimation, a system was developed that was able to select an appropriate set of cerebellar calibration models and to combine their outputs in proportion to how well each was able to calibrate, to improve the SSL estimate in multiple acoustic contexts, including novel contexts. The thesis also developed a responsibility predictor, also part of the MOSAIC framework, and this improved the robustness of the system to abrupt changes in context which could otherwise have resulted in a large performance error. Responsibility prediction also improved robustness to missing ground truth, which could occur in challenging environments where sensory feedback of ground truth may become impaired, which has not been addressed in the MOSAIC literature, adding to the novelty of the thesis. The utility of the so-called cerebellar chip has been further demonstrated through the development of a responsibility predictor that is based on the adaptive filter model of the cerebellum, rather than the more conventional function fitting neural network used in the literature. Lastly, it was demonstrated that the multiple cerebellar calibration architecture is capable of limited self-organising from a de-novo state, with a predetermined number of models. It was also demonstrated that the responsibility predictor could learn against its model after self-organisation, and to a limited extent, during self-organisation. The thesis addresses an important question of how a robot could improve its ability to listen in multiple, challenging acoustic environments, and recommends future work to develop this ability

Computational Audiovisual Scene Analysis

Yan R. Computational Audiovisual Scene Analysis. Bielefeld: Universitätsbibliothek Bielefeld; 2014.In most real-world situations, a robot is interacting with multiple people. In this case, understanding of the dialogs is essential. However, dialog scene analysis is missing in most existing systems of human-robot interaction. In such systems, only one speaker can talk with the robot or each speaker wears an attached microphone or a headset. The target of Computational AudioVisual Scene Analysis (CAVSA) is therefore making dialogs between humans and robots more natural and flexible. The CAVSA system is able to learn how many speakers are in the scenario, where the speakers are and who is currently speaking. CAVSA is a challenging task due to the complexity of dialogue scenarios. First, speakers are unknown in advance, thus a database for training high-level features beforehand to recognize faces or voices is not available. Second, people can dynamically come into and leave the scene, may move all the time and even change their locations outside the camera field of view. Third, the robot can not see all the people at the same time due to limited camera field of view and head movements. Moreover, a sound could be related to a person who stands outside the camera field of view and has never been seen. I will show that the CAVSA system is able to assign words to corresponding speakers. A speaker is recognized again when he leaves and enters the scene, or changes his position even with a newly appearing person

Sensory Communication

Contains table of contents for Section 2 and reports on five research projects.National Institutes of Health Contract 2 R01 DC00117National Institutes of Health Contract 1 R01 DC02032National Institutes of Health Contract 2 P01 DC00361National Institutes of Health Contract N01 DC22402National Institutes of Health Grant R01-DC001001National Institutes of Health Grant R01-DC00270National Institutes of Health Grant 5 R01 DC00126National Institutes of Health Grant R29-DC00625U.S. Navy - Office of Naval Research Grant N00014-88-K-0604U.S. Navy - Office of Naval Research Grant N00014-91-J-1454U.S. Navy - Office of Naval Research Grant N00014-92-J-1814U.S. Navy - Naval Air Warfare Center Training Systems Division Contract N61339-94-C-0087U.S. Navy - Naval Air Warfare Center Training System Division Contract N61339-93-C-0055U.S. Navy - Office of Naval Research Grant N00014-93-1-1198National Aeronautics and Space Administration/Ames Research Center Grant NCC 2-77

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

International audienc

Binaural sound source localization using machine learning with spiking neural networks features extraction

Human and animal binaural hearing systems are able take advantage of a variety of cues to localise sound-sources in a 3D space using only two sensors. This work presents a bionic system that utilises aspects of binaural hearing in an automated source localisation task. A head and torso emulator (KEMAR) are used to acquire binaural signals and a spiking neural network is used to compare signals from the two sensors. The firing rates of coincidence-neurons in the spiking neural network model provide information as to the location of a sound source. Previous methods have used a winner-takesall approach, where the location of the coincidence-neuron with the maximum firing rate is used to indicate the likely azimuth and elevation. This was shown to be accurate for single sources, but when multiple sources are present the accuracy significantly reduces. To improve the robustness of the methodology, an alternative approach is developed where the spiking neural network is used as a feature pre-processor. The firing rates of all coincidence-neurons are then used as inputs to a Machine Learning model which is trained to predict source location for both single and multiple sources. A novel approach that applied spiking neural networks as a binaural feature extraction method was presented. These features were processed using deep neural networks to localise multisource sound signals that were emitted from different locations. Results show that the proposed bionic binaural emulator can accurately localise sources including multiple and complex sources to 99% correctly predicted angles from single-source localization model and 91% from multi-source localization model. The impact of background noise on localisation performance has also been investigated and shows significant degradation of performance. The multisource localization model was trained with multi-condition background noise at SNRs of 10dB, 0dB, and -10dB and tested at controlled SNRs. The findings demonstrate an enhancement in the model performance in compared with noise free training data

Seventh Annual Workshop on Space Operations Applications and Research (SOAR 1993), volume 2

This document contains papers presented at the Space Operations, Applications and Research Symposium (SOAR) Symposium hosted by NASA/Johnson Space Center (JSC) and cosponsored by NASA/JSC and U.S. Air Force Materiel Command. SOAR included NASA and USAF programmatic overviews, plenary session, panel discussions, panel sessions, and exhibits. It invited technical papers in support of U.S. Army, U.S. Navy, Department of Energy, NASA, and USAF programs in the following areas: robotics and telepresence, automation and intelligent systems, human factors, life support, and space maintenance and servicing. SOAR was concerned with Government-sponsored research and development relevant to aerospace operations

Sonic interactions in virtual environments

This book tackles the design of 3D spatial interactions in an audio-centered and audio-first perspective, providing the fundamental notions related to the creation and evaluation of immersive sonic experiences. The key elements that enhance the sensation of place in a virtual environment (VE) are: Immersive audio: the computational aspects of the acoustical-space properties of Virutal Reality (VR) technologies Sonic interaction: the human-computer interplay through auditory feedback in VE VR systems: naturally support multimodal integration, impacting different application domains Sonic Interactions in Virtual Environments will feature state-of-the-art research on real-time auralization, sonic interaction design in VR, quality of the experience in multimodal scenarios, and applications. Contributors and editors include interdisciplinary experts from the fields of computer science, engineering, acoustics, psychology, design, humanities, and beyond. Their mission is to shape an emerging new field of study at the intersection of sonic interaction design and immersive media, embracing an archipelago of existing research spread in different audio communities and to increase among the VR communities, researchers, and practitioners, the awareness of the importance of sonic elements when designing immersive environments