40 research outputs found
The anterior pathway for intelligible speech: insights from univariate and multivariate methods
Whilst there is broad agreement concerning the existence of an anterior processing stream in the human brain concerned with extracting meaning from speech, there is an ongoing controversy as to whether intelligible speech is first resolved in left anterior or bilateral posterior temporal fields (Hickok and Poeppel, 2007;Rauschecker and Scott, 2009). Proponents of the bilateral processing model argue that bilateral responses are driven by the acoustic properties of the speech signal, whilst proponents of the left lateralised model suggest that left lateralisation is driven by access to linguistic representations. This thesis directly addresses these controversies using Functional Magnetic Resonance Imaging (fMRI) and univariate and multivariate analysis methods. Two main questions are addressed: (1) where are responses to intelligible, and intelligible but degraded speech, separated from responses to acoustic complexity and (2) does the resulting pattern of lateralisation, or otherwise, derive from the acoustic properties or the linguistic status of speech. The results of this thesis reconcile, to some degree, the two theoretical positions. I show that the most consistent and largest amplitude responses to intelligible, and degraded but intelligible speech, are found in the left anterior Superior Temporal Sulcus (STS). Additional responses were also found in right anterior and left posterior STS, however, these were less consistently identified. Regions of the left posterior STS showed sensitivity to resolved intelligible speech and also showed a response likely to reflect acoustic-phonetic processing supporting the resolving of intelligibility. Right posterior STS responses to intelligible speech were noticeably absent across all studies. No evidence was found for a relative acoustic basis for hemispheric lateralisation in the case of speech derived manipulations of spectrum and amplitude, but evidence was found in support of a left hemisphere specialism for resolving intelligible speech, supporting a relative left lateralisation to speech driven by linguistic rather than acoustic factors
Cognitive Radio Systems
Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems
Inaudible acoustics: Techniques and applications
This dissertation is focused on developing a sub-area of acoustics that we call inaudible acoustics. We have developed two core capabilities, (1) BackDoor and (2) Ripple, and demonstrated their use in various mobile and IoT applications. In BackDoor, we synthesize ultrasound signals that are inaudible to humans yet naturally recordable by all microphones. Importantly, the microphone does not require any modification, enabling billions of microphone-enabled devices, including phones, laptops, voice assistants, and IoT devices, to leverage the capability. Example applications include acoustic data beacons, acoustic watermarking, and spy-microphone jamming. In Ripple, we develop modulation and sensing techniques for vibratory signals that traverse through solid surfaces, enabling a new form of secure proximal communication. Applications of the vibratory communication system include on-body communication through imperceptible physical vibrations and device-device secure data transfer through physical contacts. Our prototypes include an inaudible jammer that secures private conversations from electronic eavesdropping, acoustic beacons for location-based information sharing, and vibratory communication in a smart-ring sending password through a finger touch. Our research also uncovers new security threats to acoustic devices. While simple abuse of inaudible jammer can disable hearing aids and cell phones, our work shows that voice interfaces, such as Amazon Echo, Google Home, Siri, etc., can be compromised through carefully designed inaudible voice commands. The contributions of this dissertation can be summarized in three primitives: (1) exploiting inherent hardware nonlinearity for sensing out-of-band signals, (2) developing the vibratory communication system for secure touch-based data exchange, and (3) structured information reconstruction from noisy acoustic signals. In developing these primitives, we draw from principles in wireless networking, digital communications, signal processing, and embedded design and translate them to completely functional systems
Single-Microphone Speech Enhancement and Separation Using Deep Learning
The cocktail party problem comprises the challenging task of understanding a
speech signal in a complex acoustic environment, where multiple speakers and
background noise signals simultaneously interfere with the speech signal of
interest. A signal processing algorithm that can effectively increase the
speech intelligibility and quality of speech signals in such complicated
acoustic situations is highly desirable. Especially for applications involving
mobile communication devices and hearing assistive devices. Due to the
re-emergence of machine learning techniques, today, known as deep learning, the
challenges involved with such algorithms might be overcome. In this PhD thesis,
we study and develop deep learning-based techniques for two sub-disciplines of
the cocktail party problem: single-microphone speech enhancement and
single-microphone multi-talker speech separation. Specifically, we conduct
in-depth empirical analysis of the generalizability capability of modern deep
learning-based single-microphone speech enhancement algorithms. We show that
performance of such algorithms is closely linked to the training data, and good
generalizability can be achieved with carefully designed training data.
Furthermore, we propose uPIT, a deep learning-based algorithm for
single-microphone speech separation and we report state-of-the-art results on a
speaker-independent multi-talker speech separation task. Additionally, we show
that uPIT works well for joint speech separation and enhancement without
explicit prior knowledge about the noise type or number of speakers. Finally,
we show that deep learning-based speech enhancement algorithms designed to
minimize the classical short-time spectral amplitude mean squared error leads
to enhanced speech signals which are essentially optimal in terms of STOI, a
state-of-the-art speech intelligibility estimator.Comment: PhD Thesis. 233 page
Models and Analysis of Vocal Emissions for Biomedical Applications
The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies
Effiziente und erklärbare Erkennung von mobiler Schadsoftware mittels maschineller Lernmethoden
In recent years, mobile devices shipped with Google’s Android operating system
have become ubiquitous. Due to their popularity and the high concentration of
sensitive user data on these devices, however, they have also become a
profitable target of malware authors. As a result, thousands of new malware
instances targeting Android are found almost every day. Unfortunately, common
signature-based methods often fail to detect these applications, as these
methods can- not keep pace with the rapid development of new malware.
Consequently, there is an urgent need for new malware detection methods to
tackle this growing threat.
In this thesis, we address the problem by combining concepts of static analysis
and machine learning, such that mobile malware can be detected directly on the
mobile device with low run-time overhead. To this end, we first discuss our
analysis results of a sophisticated malware that uses an ultrasonic side
channel to spy on unwitting smartphone users. Based on the insights we gain
throughout this thesis, we gradually develop a method that allows detecting
Android malware in general. The resulting method performs a broad static
analysis, gathering a large number of features associated with an application.
These features are embedded in a joint vector space, where typical patterns
indicative of malware can be automatically identified and used for explaining
the decisions of our method. In addition to an evaluation of its overall
detection and run-time performance, we also examine the interpretability of the
underlying detection model and strengthen the classifier against realistic
evasion attacks.
In a large set of experiments, we show that the method clearly outperforms
several related approaches, including popular anti-virus scanners. In most
experiments, our approach detects more than 90% of all malicious samples in the
dataset at a low false positive rate of only 1%. Furthermore, even on older
devices, it offers a good run-time performance, and can output a decision along
with a proper explanation within a few seconds, despite the use of machine
learning techniques directly on the mobile device.
Overall, we find that the application of machine learning techniques is a
promising research direction to improve the security of mobile devices. While
these techniques alone cannot defeat the threat of mobile malware, they at
least raise the bar for malicious actors significantly, especially if combined
with existing techniques.Die Verbreitung von Smartphones, insbesondere mit dem Android-Betriebssystem,
hat in den vergangenen Jahren stark zugenommen. Aufgrund ihrer hohen
Popularität haben sich diese Geräte jedoch zugleich auch zu einem lukrativen
Ziel für Entwickler von Schadsoftware entwickelt, weshalb mittlerweile täglich
neue Schadprogramme fĂĽr Android gefunden werden.
Obwohl verschiedene Lösungen existieren, die Schadprogramme auch auf mobilen
Endgeräten identifizieren sollen, bieten diese in der Praxis häufig keinen
ausreichenden Schutz. Dies liegt vor allem daran, dass diese Verfahren zumeist
signaturbasiert arbeiten und somit schädliche Programme erst zuverlässig
identifizieren können, sobald entsprechende Erkennungssignaturen vorhanden
sind. Jedoch wird es fĂĽr Antiviren-Hersteller immer schwieriger, die zur
Erkennung notwendigen Signaturen rechtzeitig bereitzustellen. Daher ist die
Entwicklung von neuen Verfahren nötig, um der wachsenden Bedrohung durch mobile
Schadsoftware besser begegnen zu können.
In dieser Dissertation wird ein Verfahren vorgestellt und eingehend untersucht,
das Techniken der statischen Code-Analyse mit Methoden des maschinellen Lernens
kombiniert, um so eine zuverlässige Erkennung von mobiler Schadsoftware direkt
auf dem Mobilgerät zu ermöglichen. Die Methode analysiert hierfür mobile
Anwendungen zunächst statisch und extrahiert dabei spezielle Merkmale, die eine
Abbildung einer Applikation in einen hochdimensionalen Vektorraum ermöglichen.
In diesem Vektorraum sind schlieĂźlich maschinelle Lernmethoden in der Lage,
automatisch Muster zur Erkennung von Schadprogrammen zu finden. Die gefundenen
Muster können dabei nicht nur zur Erkennung, sondern darüber hinaus auch zur
Erklärung einer getroffenenen Entscheidung dienen.
Im Rahmen einer ausfĂĽhrlichen Evaluation wird nicht nur die Erkennungsleistung
und die Laufzeit der vorgestellten Methode untersucht, sondern darĂĽber hinaus
das gelernte Erkennungsmodell im Detail analysiert. Hierbei wird auch die
Robustheit des Modells gegenĂĽber gezielten Angriffe untersucht und verbessert.
In einer Reihe von Experimenten kann gezeigt werden, dass mit dem
vorgeschlagenen Verfahren bessere Ergebnisse erzielt werden können als mit
vergleichbaren Methoden, sogar einschließlich einiger populärer
Antivirenprogramme. In den meisten Experimenten kann die Methode Schadprogramme
zuverlässig erkennen und erreicht Erkennungsraten von über 90% bei einer
geringen Falsch-Positiv-Rate von 1%
Satellite Communications
This study is motivated by the need to give the reader a broad view of the developments, key concepts, and technologies related to information society evolution, with a focus on the wireless communications and geoinformation technologies and their role in the environment. Giving perspective, it aims at assisting people active in the industry, the public sector, and Earth science fields as well, by providing a base for their continued work and thinking