Investigating the build-up of precedence effect using reflection masking

The auditory processing level involved in the build‐up of precedence [Freyman et al., J. Acoust. Soc. Am. 90, 874–884 (1991)] has been investigated here by employing reflection masked threshold (RMT) techniques. Given that RMT techniques are generally assumed to address lower levels of the auditory signal processing, such an approach represents a bottom‐up approach to the buildup of precedence. Three conditioner configurations measuring a possible buildup of reflection suppression were compared to the baseline RMT for four reflection delays ranging from 2.5–15 ms. No buildup of reflection suppression was observed for any of the conditioner configurations. Buildup of template (decrease in RMT for two of the conditioners), on the other hand, was found to be delay dependent. For five of six listeners, with reflection delay=2.5 and 15 ms, RMT decreased relative to the baseline. For 5‐ and 10‐ms delay, no change in threshold was observed. It is concluded that the low‐level auditory processing involved in RMT is not sufficient to realize a buildup of reflection suppression. This confirms suggestions that higher level processing is involved in PE buildup. The observed enhancement of reflection detection (RMT) may contribute to active suppression at higher processing levels

Machine Learning for Human Activity Detection in Smart Homes

Recognizing human activities in domestic environments from audio and active power consumption sensors is a challenging task since on the one hand, environmental sound signals are multi-source, heterogeneous, and varying in time and on the other hand, the active power consumption varies significantly for similar type electrical appliances. Many systems have been proposed to process environmental sound signals for event detection in ambient assisted living applications. Typically, these systems use feature extraction, selection, and classification. However, despite major advances, several important questions remain unanswered, especially in real-world settings. A part of this thesis contributes to the body of knowledge in the field by addressing the following problems for ambient sounds recorded in various real-world kitchen environments: 1) which features, and which classifiers are most suitable in the presence of background noise? 2) what is the effect of signal duration on recognition accuracy? 3) how do the SNR and the distance between the microphone and the audio source affect the recognition accuracy in an environment in which the system was not trained? We show that for systems that use traditional classifiers, it is beneficial to combine gammatone frequency cepstral coefficients and discrete wavelet transform coefficients and to use a gradient boosting classifier. For systems based on deep learning, we consider 1D and 2D CNN using mel-spectrogram energies and mel-spectrograms images, as inputs, respectively and show that the 2D CNN outperforms the 1D CNN. We obtained competitive classification results for two such systems and validated the performance of our algorithms on public datasets (Google Brain/TensorFlow Speech Recognition Challenge and the 2017 Detection and Classification of Acoustic Scenes and Events Challenge). Regarding the problem of the energy-based human activity recognition in a household environment, machine learning techniques to infer the state of household appliances from their energy consumption data are applied and rule-based scenarios that exploit these states to detect human activity are used. Since most activities within a house are related with the operation of an electrical appliance, this unimodal approach has a significant advantage using inexpensive smart plugs and smart meters for each appliance. This part of the thesis proposes the use of unobtrusive and easy-install tools (smart plugs) for data collection and a decision engine that combines energy signal classification using dominant classifiers (compared in advanced with grid search) and a probabilistic measure for appliance usage. It helps preserving the privacy of the resident, since all the activities are stored in a local database. DNNs received great research interest in the field of computer vision. In this thesis we adapted different architectures for the problem of human activity recognition. We analyze the quality of the extracted features, and more specifically how model architectures and parameters affect the ability of the automatically extracted features from DNNs to separate activity classes in the final feature space. Additionally, the architectures that we applied for our main problem were also applied to text classification in which we consider the input text as an image and apply 2D CNNs to learn the local and global semantics of the sentences from the variations of the visual patterns of words. This work helps as a first step of creating a dialogue agent that would not require any natural language preprocessing. Finally, since in many domestic environments human speech is present with other environmental sounds, we developed a Convolutional Recurrent Neural Network, to separate the sound sources and applied novel post-processing filters, in order to have an end-to-end noise robust system. Our algorithm ranked first in the Apollo-11 Fearless Steps Challenge.Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 676157, project ACROSSIN

Technical Workshop: Advanced Helicopter Cockpit Design

Information processing demands on both civilian and military aircrews have increased enormously as rotorcraft have come to be used for adverse weather, day/night, and remote area missions. Applied psychology, engineering, or operational research for future helicopter cockpit design criteria were identified. Three areas were addressed: (1) operational requirements, (2) advanced avionics, and (3) man-system integration

A speaker classification framework for non-intrusive user modeling : speech-based personalization of in-car services

Speaker Classification, i.e. the automatic detection of certain characteristics of a person based on his or her voice, has a variety of applications in modern computer technology and artificial intelligence: As a non-intrusive source for user modeling, it can be employed for personalization of human-machine interfaces in numerous domains. This dissertation presents a principled approach to the design of a novel Speaker Classification system for automatic age and gender recognition which meets these demands. Based on literature studies, methods and concepts dealing with the underlying pattern recognition task are developed. The final system consists of an incremental GMM-SVM supervector architecture with several optimizations. An extensive data-driven experiment series explores the parameter space and serves as evaluation of the component. Further experiments investigate the language-independence of the approach. As an essential part of this thesis, a framework is developed that implements all tasks associated with the design and evaluation of Speaker Classification in an integrated development environment that is able to generate efficient runtime modules for multiple platforms. Applications from the automotive field and other domains demonstrate the practical benefit of the technology for personalization, e.g. by increasing local danger warning lead time for elderly drivers.Die Sprecherklassifikation, also die automatische Erkennung bestimmter Merkmale einer Person anhand ihrer Stimme, besitzt eine Vielzahl von Anwendungsmöglichkeiten in der modernen Computertechnik und Künstlichen Intelligenz: Als nicht-intrusive Wissensquelle für die Benutzermodellierung kann sie zur Personalisierung in vielen Bereichen eingesetzt werden. In dieser Dissertation wird ein fundierter Ansatz zum Entwurf eines neuartigen Sprecherklassifikationssystems zur automatischen Bestimmung von Alter und Geschlecht vorgestellt, welches diese Anforderungen erfüllt. Ausgehend von Literaturstudien werden Konzepte und Methoden zur Behandlung des zugrunde liegenden Mustererkennungsproblems entwickelt, welche zu einer inkrementell arbeitenden GMM-SVM-Supervector-Architektur mit diversen Optimierungen führen. Eine umfassende datengetriebene Experimentalreihe dient der Erforschung des Parameterraumes und zur Evaluierung der Komponente. Weitere Studien untersuchen die Sprachunabhängigkeit des Ansatzes. Als wesentlicher Bestandteil der Arbeit wird ein Framework entwickelt, das alle im Zusammenhang mit Entwurf und Evaluierung von Sprecherklassifikation anfallenden Aufgaben in einer integrierten Entwicklungsumgebung implementiert, welche effiziente Laufzeitmodule für verschiedene Plattformen erzeugen kann. Anwendungen aus dem Automobilbereich und weiteren Domänen demonstrieren den praktischen Nutzen der Technologie zur Personalisierung, z.B. indem die Vorlaufzeit von lokalen Gefahrenwarnungen für ältere Fahrer erhöht wird