1,275 research outputs found
Acoustic Impulse Responses for Wearable Audio Devices
We present an open-access dataset of over 8000 acoustic impulse from 160
microphones spread across the body and affixed to wearable accessories. The
data can be used to evaluate audio capture and array processing systems using
wearable devices such as hearing aids, headphones, eyeglasses, jewelry, and
clothing. We analyze the acoustic transfer functions of different parts of the
body, measure the effects of clothing worn over microphones, compare
measurements from a live human subject to those from a mannequin, and simulate
the noise-reduction performance of several beamformers. The results suggest
that arrays of microphones spread across the body are more effective than those
confined to a single device.Comment: To appear at ICASSP 201
DoubleEcho: Mitigating Context-Manipulation Attacks in Copresence Verification
Copresence verification based on context can improve usability and strengthen
security of many authentication and access control systems. By sensing and
comparing their surroundings, two or more devices can tell whether they are
copresent and use this information to make access control decisions. To the
best of our knowledge, all context-based copresence verification mechanisms to
date are susceptible to context-manipulation attacks. In such attacks, a
distributed adversary replicates the same context at the (different) locations
of the victim devices, and induces them to believe that they are copresent. In
this paper we propose DoubleEcho, a context-based copresence verification
technique that leverages acoustic Room Impulse Response (RIR) to mitigate
context-manipulation attacks. In DoubleEcho, one device emits a wide-band
audible chirp and all participating devices record reflections of the chirp
from the surrounding environment. Since RIR is, by its very nature, dependent
on the physical surroundings, it constitutes a unique location signature that
is hard for an adversary to replicate. We evaluate DoubleEcho by collecting RIR
data with various mobile devices and in a range of different locations. We show
that DoubleEcho mitigates context-manipulation attacks whereas all other
approaches to date are entirely vulnerable to such attacks. DoubleEcho detects
copresence (or lack thereof) in roughly 2 seconds and works on commodity
devices
Spatial audio in small display screen devices
Our work addresses the problem of (visual) clutter in mobile device interfaces. The solution we propose involves the translation of technique-from the graphical to the audio domain-for expliting space in information representation. This article presents an illustrative example in the form of a spatialisedaudio progress bar. In usability tests, participants performed background monitoring tasks significantly more accurately using this spatialised audio (a compared with a conventional visual) progress bar. Moreover, their performance in a simultaneously running, visually demanding foreground task was significantly improved in the eye-free monitoring condition. These results have important implications for the design of multi-tasking interfaces for mobile devices
Studies on binaural and monaural signal analysis methods and applications
Sound signals can contain a lot of information about the environment and the sound sources present in it. This thesis presents novel contributions to the analysis of binaural and monaural sound signals. Some new applications are introduced in this work, but the emphasis is on analysis methods. The three main topics of the thesis are computational estimation of sound source distance, analysis of binaural room impulse responses, and applications intended for augmented reality audio.
A novel method for binaural sound source distance estimation is proposed. The method is based on learning the coherence between the sounds entering the left and right ears. Comparisons to an earlier approach are also made. It is shown that these kinds of learning methods can correctly recognize the distance of a speech sound source in most cases.
Methods for analyzing binaural room impulse responses are investigated. These methods are able to locate the early reflections in time and also to estimate their directions of arrival. This challenging problem could not be tackled completely, but this part of the work is an important step towards accurate estimation of the individual early reflections from a binaural room impulse response.
As the third part of the thesis, applications of sound signal analysis are studied. The most notable contributions are a novel eyes-free user interface controlled by finger snaps, and an investigation on the importance of features in audio surveillance.
The results of this thesis are steps towards building machines that can obtain information on the surrounding environment based on sound. In particular, the research into sound source distance estimation functions as important basic research in this area. The applications presented could be valuable in future telecommunications scenarios, such as augmented reality audio
Multi-View Networks For Multi-Channel Audio Classification
In this paper we introduce the idea of multi-view networks for sound
classification with multiple sensors. We show how one can build a multi-channel
sound recognition model trained on a fixed number of channels, and deploy it to
scenarios with arbitrary (and potentially dynamically changing) number of input
channels and not observe degradation in performance. We demonstrate that at
inference time you can safely provide this model all available channels as it
can ignore noisy information and leverage new information better than standard
baseline approaches. The model is evaluated in both an anechoic environment and
in rooms generated by a room acoustics simulator. We demonstrate that this
model can generalize to unseen numbers of channels as well as unseen room
geometries.Comment: 5 pages, 7 figures, Accepted to ICASSP 201
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