951 research outputs found
Weighted-Sampling Audio Adversarial Example Attack
Recent studies have highlighted audio adversarial examples as a ubiquitous
threat to state-of-the-art automatic speech recognition systems. Thorough
studies on how to effectively generate adversarial examples are essential to
prevent potential attacks. Despite many research on this, the efficiency and
the robustness of existing works are not yet satisfactory. In this paper, we
propose~\textit{weighted-sampling audio adversarial examples}, focusing on the
numbers and the weights of distortion to reinforce the attack. Further, we
apply a denoising method in the loss function to make the adversarial attack
more imperceptible. Experiments show that our method is the first in the field
to generate audio adversarial examples with low noise and high audio robustness
at the minute time-consuming level.Comment: https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuXL.9260.pd
Learning Audio Sequence Representations for Acoustic Event Classification
Acoustic Event Classification (AEC) has become a significant task for
machines to perceive the surrounding auditory scene. However, extracting
effective representations that capture the underlying characteristics of the
acoustic events is still challenging. Previous methods mainly focused on
designing the audio features in a 'hand-crafted' manner. Interestingly,
data-learnt features have been recently reported to show better performance. Up
to now, these were only considered on the frame-level. In this paper, we
propose an unsupervised learning framework to learn a vector representation of
an audio sequence for AEC. This framework consists of a Recurrent Neural
Network (RNN) encoder and a RNN decoder, which respectively transforms the
variable-length audio sequence into a fixed-length vector and reconstructs the
input sequence on the generated vector. After training the encoder-decoder, we
feed the audio sequences to the encoder and then take the learnt vectors as the
audio sequence representations. Compared with previous methods, the proposed
method can not only deal with the problem of arbitrary-lengths of audio
streams, but also learn the salient information of the sequence. Extensive
evaluation on a large-size acoustic event database is performed, and the
empirical results demonstrate that the learnt audio sequence representation
yields a significant performance improvement by a large margin compared with
other state-of-the-art hand-crafted sequence features for AEC
Full-range Gate-controlled Terahertz Phase Modulations with Graphene Metasurfaces
Local phase control of electromagnetic wave, the basis of a diverse set of
applications such as hologram imaging, polarization and wave-front
manipulation, is of fundamental importance in photonic research. However, the
bulky, passive phase modulators currently available remain a hurdle for
photonic integration. Here we demonstrate full-range active phase modulations
in the Tera-Hertz (THz) regime, realized by gate-tuned ultra-thin reflective
metasurfaces based on graphene. A one-port resonator model, backed by our
full-wave simulations, reveals the underlying mechanism of our extreme phase
modulations, and points to general strategies for the design of tunable
photonic devices. As a particular example, we demonstrate a gate-tunable THz
polarization modulator based on our graphene metasurface. Our findings pave the
road towards exciting photonic applications based on active phase
manipulations
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