| 1. |
- Arnab, Anurag, et al.
(author)
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Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation: Combining Probabilistic Graphical Models with Deep Learning for Structured Prediction
- 2018
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In: IEEE Signal Processing Magazine. - 1558-0792 .- 1053-5888. ; 35:1, s. 37-52
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Journal article (peer-reviewed)abstract
- Semantic segmentation is the task of labeling every pixel in an image with a predefined object category. It has numerous applications in scenarios where the detailed understanding of an image is required, such as in autonomous vehicles and medical diagnosis. This problem has traditionally been solved with probabilistic models known as conditional random fields (CRFs) due to their ability to model the relationships between the pixels being predicted. However, deep neural networks (DNNs) recently have been shown to excel at a wide range of computer vision problems due to their ability to automatically learn rich feature representations from data, as opposed to traditional handcrafted features. The idea of combining CRFs and DNNs have achieved state-of-the-art results in a number of domains. We review the literature on combining the modeling power of CRFs with the representation-learning ability of DNNs, ranging from early work that combines these two techniques as independent stages of a common pipeline to recent approaches that embed inference of probabilistic models directly in the neural network itself. Finally, we summarize future research directions.
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| 2. |
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| 3. |
- Gerkmann, Timo, et al.
(author)
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Phase Processing for Single-Channel Speech Enhancement
- 2015
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In: IEEE signal processing magazine (Print). - 1053-5888 .- 1558-0792. ; 32:2, s. 55-66
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Journal article (peer-reviewed)abstract
- With the advancement of technology, both assisted listening devices and speech communication devices are becoming more portable and also more frequently used. As a consequence, users of devices such as hearing aids, cochlear implants, and mobile telephones, expect their devices to work robustly anywhere and at any time. This holds in particular for challenging noisy environments like a cafeteria, a restaurant, a subway, a factory, or in traffic. One way to making assisted listening devices robust to noise is to apply speech enhancement algorithms. To improve the corrupted speech, spatial diversity can be exploited by a constructive combination of microphone signals (so-called beamforming), and by exploiting the different spectro-temporal properties of speech and noise. Here, we focus on single-channel speech enhancement algorithms which rely on spectrotemporal properties. On the one hand, these algorithms can be employed when the miniaturization of devices only allows for using a single microphone. On the other hand, when multiple microphones are available, single-channel algorithms can be employed as a postprocessor at the output of a beamformer. To exploit the short-term stationary properties of natural sounds, many of these approaches process the signal in a time-frequency representation, most frequently the short-time discrete Fourier transform (STFT) domain. In this domain, the coefficients of the signal are complex-valued, and can therefore be represented by their absolute value (referred to in the literature both as STFT magnitude and STFT amplitude) and their phase. While the modeling and processing of the STFT magnitude has been the center of interest in the past three decades, phase has been largely ignored. In this article, we review the role of phase processing for speech enhancement in the context of assisted listening and speech communication devices. We explain why most of the research conducted in this field used to focus on estimating spectral magnitudes in the STFT domain, and why recently phase processing is attracting increasing interest in the speech enhancement community. Furthermore, we review both early and recent methods for phase processing in speech enhancement. We aim to show that phase processing is an exciting field of research with the potential to make assisted listening and speech communication devices more robust in acoustically challenging environments.
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| 4. |
- Hult, Robert, 1984, et al.
(author)
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Coordination of Cooperative Autonomous Vehicles: Toward safer and more efficient road transportation
- 2016
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In: IEEE Signal Processing Magazine. - : Institute of Electrical and Electronics Engineers (IEEE). - 1558-0792 .- 1053-5888. ; 33:6, s. 74-84
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Journal article (peer-reviewed)abstract
- While intelligent transportation systems come in many shapes and sizes, arguably the most transformational realization will be the autonomous vehicle. As such vehicles become commercially available in the coming years, first on dedicated roads and under specific conditions, and later on all public roads at all times, a phase transition will occur. Once a sufficient number of autonomous vehicles is deployed, the opportunity for explicit coordination appears. This article treats this challenging network control problem, which lies at the intersection of control theory, signal processing, and wireless communication. We provide an overview of the state of the art, while at the same time highlighting key research directions for the coming decades.
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| 5. |
- Karlsson, Rickard, 1970-, et al.
(author)
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The Future of Automotive Localization Algorithms : Available, reliable, and scalable localization: Anywhere and anytime
- 2017
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In: IEEE signal processing magazine (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-5888 .- 1558-0792. ; 34:2, s. 60-69
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Journal article (peer-reviewed)abstract
- Most navigation systems today rely on global navigation satellite systems (gnss), including in cars. With support from odometry and inertial sensors, this is a sufficiently accurate and robust solution, but there are future demands. Autonomous cars require higher accuracy and integrity. Using the car as a sensor probe for road conditions in cloud-based services also sets other kind of requirements. The concept of the Internet of Things requires stand-alone solutions without access to vehicle data. Our vision is a future with both invehicle localization algorithms and after-market products, where the position is computed with high accuracy in gnss-denied environments. We present a localization approach based on a prior that vehicles spend the most time on the road, with the odometer as the primary input. When wheel speeds are not available, we present an approach solely based on inertial sensors, which also can be used as a speedometer. The map information is included in a Bayesian setting using the particle filter (PF) rather than standard map matching. In extensive experiments, the performance without gnss is shown to have basically the same quality as utilizing a gnss sensor. Several topics are treated: virtual measurements, dead reckoning, inertial sensor information, indoor positioning, off-road driving, and multilevel positioning.
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| 6. |
- Kleijn, W. Bastiaan, et al.
(author)
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Optimizing Speech Intelligibility in a Noisy Environment
- 2015
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In: IEEE signal processing magazine (Print). - 1053-5888 .- 1558-0792. ; 32:2, s. 43-54
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Journal article (peer-reviewed)abstract
- Modern communication technology facilitates communication from anywhere to anywhere. As a result, low speech intelligibility has become a common problem, which is exacerbated by the lack of feedback to the talker about the rendering environment. In recent years, a range of algorithms has been developed to enhance the intelligibility of speech rendered in a noisy environment. We describe methods for intelligibility enhancement from a unified vantage point. Before one defines a measure of intelligibility, the level of abstraction of the representation must be selected. For example, intelligibility can be measured on the message, the sequence of words spoken, the sequence of sounds, or a sequence of states of the auditory system. Natural measures of intelligibility defined at the message level are mutual information and the hit-or-miss criterion. The direct evaluation of high-level measures requires quantitative knowledge of human cognitive processing. Lower-level measures can be derived from higher-level measures by making restrictive assumptions. We discuss the implementation and performance of some specific enhancement systems in detail, including speech intelligibility index (SII)-based systems and systems aimed at enhancing the sound-field where it is perceived by the listener. We conclude with a discussion of the current state of the field and open problems.
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| 7. |
- Larsson, Erik G., 1974-, et al.
(author)
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Teaching the Principles of Massive MIMO : Exploring reciprocity-based multiuser MIMO beamforming using acoustic waves
- 2017
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In: IEEE signal processing magazine (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-5888 .- 1558-0792. ; 34:1, s. 40-47
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Journal article (peer-reviewed)abstract
- Massive multiple-input, multiple-output (MIMO) is currently the most compelling wireless physical layer technology and a key component of fifth-generation (5G) systems. The understanding of its core principles has emerged during the last five years, and material is becoming available that is rigorously refined to focus on timeless fundamentals [1], facilitating the instruction of the topic to both master- and doctoral-level students [2]. Meaningful laboratory work that exposes the operational principles of massive MIMO is more difficult to accomplish. At Linköping University, Sweden, this was achieved through a project course, based on the conceive-design-implement-operate (CDIO) concept [3], and through the creation of a specially designed experimental setup using acoustic signals.
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| 8. |
- Liu, Liang, et al.
(author)
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Sparse Signal Processing for Grant-Free Massive Connectivity A future paradigm for random access protocols in the Internet of Things
- 2018
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In: IEEE signal processing magazine (Print). - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1053-5888 .- 1558-0792. ; 35:5, s. 88-99
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Journal article (peer-reviewed)abstract
- The next wave of wireless technologies will proliferate in connecting sensors, machines, and robots for myriad new applications, thereby creating the fabric for the Internet of Things (IoT). A generic scenario for IoT connectivity involves a massive number of machine-type connections, but in a typical application, only a small (unknown) subset of devices are active at any given instant; therefore, one of the key challenges of providing massive IoT connectivity is to detect the active devices first and then decode their data with low latency. This article advocates the usage of grant-free, rather than grant-based random access schemes to overcome the challenge of massive IoT access. Several key signal processing techniques that promote the performance of the grant-free strategies are outlined, with a primary focus on advanced compressed sensing techniques and their applications for the efficient detection of active devices. We argue that massive multiple-input, multiple-output (MIMO) is especially well suited for massive IoT connectivity because the device detection error can be driven to zero asymptotically in the limit as the number of antennas at the base station (BS) goes to infinity by using the multiple-measurement vector (MMV) compressed sensing techniques. This article also provides a perspective on several related important techniques for massive access, such as embedding short messages onto the device-activity detection process and the coded random access.
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| 9. |
- Mishra, Kumar Vijay, et al.
(author)
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Toward Millimeter-Wave Joint Radar Communications A signal processing perspective
- 2019
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In: IEEE signal processing magazine (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-5888 .- 1558-0792. ; 36:5, s. 100-114
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Journal article (peer-reviewed)abstract
- Synergistic design of communications and radar systems with common spectral and hardware resources is heralding a new era of efficiently utilizing a limited radio-frequency (RF) spectrum. Such a joint radar communications (JRC) model has advantages of low cost, compact size, less power consumption, spectrum sharing, improml performance, and safety due to enhanced information sharing. Today, millimeter-wave (mm-wave) communications have emerged as the preferred technology for short distance wireless links because they provide transmission bandwidth that is several gigahertz wide. This band is also promising for short-range radar applications, which benefit from the high-range resolution arising from large transmit signal bandwidths. Signal processing techniques are critical to the implementation of mm-wave JRC systems. Major challenges are joint waveform design and performance criteria that would optimally trade off between communications and radar functionalities. Novel multiple-input, multiple-output (MIMO) signal processing techniques are required because mm-wave JRC systems employ large antenna arrays. There are opportunities to exploit recent advances in cognition, compressed sensing, and machine learning to reduce required resources and dynamically allocate them with low overheads. This article provides a signal processing perspective of mm-wave JRC systems with an emphasis on waveform design.
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| 10. |
- Savazzi, Stefano, et al.
(author)
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Device-Free Radio Vision for Assisted Living Leveraging wireless channel quality information for human sensing
- 2016
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In: IEEE signal processing magazine (Print). - 1053-5888 .- 1558-0792. ; 33:2, s. 45-58
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Journal article (peer-reviewed)abstract
- Wireless propagation is conventionally considered as the enabling tool for transporting information in digital communications. However, recent research has shown that the perturbations of the same electromagnetic (EM) fields that are adopted for data transmission can be used as a powerful sensing tool for device-free radio vision. Applications range from human body motion detection and localization to passive gesture recognition. In line with the current evolution of mobile phone sensing [1], radio terminals are not only ubiquitous communication interfaces, but they also incorporate novel or augmented sensing potential, capable of acquiring an accurate human-scale understanding of space and motion. This article shows how radio-frequency (RF) signals can be employed to provide a device-free environmental vision and investigates the detection and tracking capabilities for potential benefits in daily life.
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