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The Latest News, Articles, and Events in Signal Processing
We address voice activity detection in acoustic environments of transients and stationary noises, which often occur in real-life scenarios. We exploit unique spatial patterns of speech and non-speech audio frames by independently learning their underlying geometric structure. This process is done through a deep encoder-decoder-based neural network architecture.
Given the recent surge in developments of deep learning, this paper provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross fertilization between areas.
The IEEE Signal Processing Society congratulates the following recipients who will receive the 2018 IEEE Signal Processing Society Paper Award for their papers published in the IEEE Journal of Selected Topics in Signal Processing. Presentation of the paper awards will take place at ICASSP 2019 in Brighton, UK.
The Departments of Biomedical Engineering and Electrical and Computer Engineering at the University of Virginia seek Research Associates to work in the laboratory of Dr. Gustavo Rohde (imagedatascience.com) to perform research on pattern recognition, machine learning, and image and signal analysis with applications to biomedical imaging, cancer detection, mobile and remote sensing, and others.
The 29th MLSP workshop in the series of workshops organized by the IEEE Signal Processing Society MLSP Technical Committee will present the most recent and exciting advances in machine learning for signal processing through keynote talks, tutorials, as well as special and regular single-track sessions.
We propose a novel technique for steganography on 3-D meshes so as to resist steganalysis. The majority of existing methods modulate vertex coordinates to embed messages in a nonadaptive way. We take account of complexity of local regions as joint distortion of a triple unit (vertice) and coding method such as syndrome trellis codes to adaptively embed messages, which owns stronger security with respect to existing steganalysis.
In general, low-rank representation (LRR) aims to find the lowest rank representation with respect to a dictionary. In fact, the dictionary is a key aspect of low-rank representation. However, a lot of low-rank representation methods usually use the data itself as a dictionary (i.e., a fixed dictionary), which may degrade their performances due to the lack of clustering ability of a fixed dictionary.
The partition algorithm as a digital image processing technique is significant to many applications, such as data encryption, image denoising, and 3-D reconstruction. In order to achieve well partition that can availably reduce the distortion phenomenon, a novel approach named image adaptive triangular partition (IATP) is proposed, which considers the grayscale distribution of the image and removes...
The problem of authenticating a re-sampled image has been investigated over many years. Currently, however, little research proposes a statistical model-based test, resulting in that statistical performance of the resampling detector could not be completely analyzed. To fill the gap, we utilize a parametric model to expose the traces of resampling forgery, which is described with the distribution of residual noise.
We present a compression scheme for multiview imagery that facilitates high scalability and accessibility of the compressed content. Our scheme relies upon constructing at a single base view, a disparity model for a group of views, and then utilizing this base-anchored model to infer disparity at all views belonging to the group.
Signal decomposition is a classical problem in signal processing, which aims to separate an observed signal into two or more components, each with its own property. Usually, each component is described by its own subspace or dictionary. Extensive research has been done for the case where the components are additive, but in real-world applications, the components are often non-additive.
The surface normal estimation from photometric stereo becomes less reliable when the surface reflectance deviates from the Lambertian assumption. The non-Lambertian effect can be explicitly addressed by physics modeling to the reflectance function, at the cost of introducing highly nonlinear optimization.
Being able to cover a wide range of views, pan-tilt-zoom (PTZ) cameras have been widely deployed in visual surveillance systems. To achieve a global-view perception of a surveillance scene, it is necessary to generate its panoramic background image, which can be used for the subsequent applications such as road segmentation, active tracking, and so on.
IEEE SPS has built a streamlined mechanism for employers to add a job announcement by simply filling in a simple job opportunity submission Web form related to a particular TC field. To submit job announcements for a particular Technical Committee, the submission form can be found by visiting the page below and selecting a particular TC. Visit the SPS Job Submission Form.
We introduce the multiple enrollment scheme for SRAM-physical unclonable functions (PUFs). During each enrollment, the binary power-on values of the SRAM are observed, and a corresponding key and helper data are generated. Each key can later be reconstructed from an additional observation and the helper data.
In September 2017, the McAfee Labs quarterly report estimated that brute-force attacks represent 20% of total network attacks, making them the most prevalent type of attack ex-aequo with browser-based vulnerabilities. These attacks have sometimes catastrophic consequences, and understanding their fundamental limits may play an important role in the risk assessment of password-secured systems and in the design of better security protocols.
Electric network frequency (ENF) is a time-varying signal of the frequency of mains electricity in a power grid. It continuously fluctuates around a nominal value (50/60 Hz) due to changes in the supply and demand of power over time. Depending on these ENF variations, the luminous intensity of a mains-powered light source also fluctuates.
Face presentation attacks are the main threats to face recognition systems, and many presentation attack detection (PAD) methods have been proposed in recent years. Although these methods have achieved significant performance in some specific intrusion modes, difficulties still exist in addressing replayed video attacks.
Low-level criminals, who do the legwork in a criminal organization, are the most likely to be arrested, whereas the high-level ones tend to avoid attention. But crippling the work of criminal organizations is not possible unless investigators can identify the most influential, high-level members and monitor their communication channels.
Current anomaly detection systems (ADSs) apply statistical and machine learning algorithms to discover zero-day attacks, but such algorithms are vulnerable to advanced persistent threat actors. In this paper, we propose an adversarial statistical learning mechanism for anomaly detection, outlier Dirichlet mixture-based ADS (ODM-ADS), which has three new capabilities.
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