10,871 research outputs found
A spiking neural network for real-time Spanish vowel phonemes recognition
This paper explores neuromorphic approach capabilities applied to real-time speech processing. A spiking
recognition neural network composed of three types of neurons is proposed. These neurons are based on an
integrative and fire model and are capable of recognizing auditory frequency patterns, such as vowel phonemes;
words are recognized as sequences of vowel phonemes. For demonstrating real-time operation, a complete
spiking recognition neural network has been described in VHDL for detecting certain Spanish words, and it has
been tested in a FPGA platform. This is a stand-alone and fully hardware system that allows to embed it in a
mobile system. To stimulate the network, a spiking digital-filter-based cochlea has been implemented in VHDL.
In the implementation, an Address Event Representation (AER) is used for transmitting information between
neurons.Ministerio de Economía y Competitividad TEC2012-37868-C04-02/0
Virtual Audio - Three-Dimensional Audio in Virtual Environments
Three-dimensional interactive audio has a variety ofpotential uses in human-machine interfaces. After lagging seriously
behind the visual components, the importance of sound is now becoming
increas-ingly accepted.
This paper mainly discusses background and techniques to implement
three-dimensional audio in computer interfaces. A case study of a
system for three-dimensional audio, implemented by the author, is
described in great detail. The audio system was moreover integrated
with a virtual reality system and conclusions on user tests and use
of the audio system is presented along with proposals for future work
at the end of the paper.
The thesis begins with a definition of three-dimensional audio and a
survey on the human auditory system to give the reader the needed
knowledge of what three-dimensional audio is and how human auditory
perception works
Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems
Voice Processing Systems (VPSes), now widely deployed, have been made
significantly more accurate through the application of recent advances in
machine learning. However, adversarial machine learning has similarly advanced
and has been used to demonstrate that VPSes are vulnerable to the injection of
hidden commands - audio obscured by noise that is correctly recognized by a VPS
but not by human beings. Such attacks, though, are often highly dependent on
white-box knowledge of a specific machine learning model and limited to
specific microphones and speakers, making their use across different acoustic
hardware platforms (and thus their practicality) limited. In this paper, we
break these dependencies and make hidden command attacks more practical through
model-agnostic (blackbox) attacks, which exploit knowledge of the signal
processing algorithms commonly used by VPSes to generate the data fed into
machine learning systems. Specifically, we exploit the fact that multiple
source audio samples have similar feature vectors when transformed by acoustic
feature extraction algorithms (e.g., FFTs). We develop four classes of
perturbations that create unintelligible audio and test them against 12 machine
learning models, including 7 proprietary models (e.g., Google Speech API, Bing
Speech API, IBM Speech API, Azure Speaker API, etc), and demonstrate successful
attacks against all targets. Moreover, we successfully use our maliciously
generated audio samples in multiple hardware configurations, demonstrating
effectiveness across both models and real systems. In so doing, we demonstrate
that domain-specific knowledge of audio signal processing represents a
practical means of generating successful hidden voice command attacks
Acoustic, psychophysical, and neuroimaging measurements of the effectiveness of active cancellation during auditory functional magnetic resonance imaging
Functional magnetic resonance imaging (fMRI) is one of the principal neuroimaging techniques for studying human audition, but it generates an intense background sound which hinders listening performance and confounds measures of the auditory response. This paper reports the perceptual effects of an active noise control (ANC) system that operates in the electromagnetically hostile and physically compact neuroimaging environment to provide significant noise reduction, without interfering with image quality. Cancellation was first evaluated at 600 Hz, corresponding to the dominant peak in the power spectrum of the background sound and at which cancellation is maximally effective. Microphone measurements at the ear demonstrated 35 dB of acoustic attenuation [from 93 to 58 dB sound pressure level (SPL)], while masked detection thresholds improved by 20 dB (from 74 to 54 dB SPL). Considerable perceptual benefits were also obtained across other frequencies, including those corresponding to dips in the spectrum of the background sound. Cancellation also improved the statistical detection of sound-related cortical activation, especially for sounds presented at low intensities. These results confirm that ANC offers substantial benefits for fMRI research
Efficient transfer entropy analysis of non-stationary neural time series
Information theory allows us to investigate information processing in neural
systems in terms of information transfer, storage and modification. Especially
the measure of information transfer, transfer entropy, has seen a dramatic
surge of interest in neuroscience. Estimating transfer entropy from two
processes requires the observation of multiple realizations of these processes
to estimate associated probability density functions. To obtain these
observations, available estimators assume stationarity of processes to allow
pooling of observations over time. This assumption however, is a major obstacle
to the application of these estimators in neuroscience as observed processes
are often non-stationary. As a solution, Gomez-Herrero and colleagues
theoretically showed that the stationarity assumption may be avoided by
estimating transfer entropy from an ensemble of realizations. Such an ensemble
is often readily available in neuroscience experiments in the form of
experimental trials. Thus, in this work we combine the ensemble method with a
recently proposed transfer entropy estimator to make transfer entropy
estimation applicable to non-stationary time series. We present an efficient
implementation of the approach that deals with the increased computational
demand of the ensemble method's practical application. In particular, we use a
massively parallel implementation for a graphics processing unit to handle the
computationally most heavy aspects of the ensemble method. We test the
performance and robustness of our implementation on data from simulated
stochastic processes and demonstrate the method's applicability to
magnetoencephalographic data. While we mainly evaluate the proposed method for
neuroscientific data, we expect it to be applicable in a variety of fields that
are concerned with the analysis of information transfer in complex biological,
social, and artificial systems.Comment: 27 pages, 7 figures, submitted to PLOS ON
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