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Andreas Spanias
Arizona State University
449Publications
28H-index
5,037Citations
Publications 440
Newest
#1Mohit Shah (ASU: Arizona State University)H-Index: 7
#2Ming Tu (ASU: Arizona State University)H-Index: 5
Last.Andreas Spanias (ASU: Arizona State University)H-Index: 28
view all 5 authors...
Speech emotion recognition methods combining articulatory information with acoustic features have been previously shown to improve recognition performance. Collection of articulatory data on a large scale may not be feasible in many scenarios, thus restricting the scope and applicability of such methods. In this paper, a discriminative learning method for emotion recognition using both articulatory and acoustic information is proposed. A traditional l1-regularized logistic regression cost functi...
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#1Gowtham Muniraju (ASU: Arizona State University)H-Index: 3
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
Last.Andreas Spanias (ASU: Arizona State University)H-Index: 28
view all 3 authors...
A novel distributed algorithm for estimating the maximum of the node initial state values in a network, in the presence of additive communication noise is proposed. Conventionally, the maximum is estimated locally at each node by updating the node state value with the largest received measurements in every iteration. However, due to the additive channel noise, the estimate of the maximum at each node drifts at each iteration and this results in nodes diverging from the true max value. Max-plus a...
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#1Victor Solo (UNSW: University of New South Wales)H-Index: 18
#2Maria Greco (UniPi: University of Pisa)H-Index: 28
Last.Monica F. Bugallo (SBU: Stony Brook University)H-Index: 20
view all 6 authors...
The anniversary of a number of significant signal processing algorithms from the 1960s, including the least mean square algorithm and the Kalman filter, provided an opportunity at ICASSP 2019 to reflect on the links between education and innovation. This led ultimately to the proposal of some special sessions as well a panel session that would provide some insight, via a historical perspective, consideration of the current status, and an assessment of the emerging educational future.
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Sep 1, 2019 in ICIP (International Conference on Image Processing)
#1Juan Andrade (ASU: Arizona State University)H-Index: 1
#2Pavan Turaga (ASU: Arizona State University)H-Index: 2
Last.Andreas Spanias (ASU: Arizona State University)H-Index: 28
view all 3 authors...
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#1Suhas Ranganath (ASU: Arizona State University)H-Index: 7
#2Jayaraman J. Thiagarajan (ASU: Arizona State University)H-Index: 14
Last.Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
view all 8 authors...
In this paper, we present a unique Android-DSP (AJDSP) application which was built from the ground up to provide mobile laboratory and computational experiences for educational use. AJDSP provides a mobile intuitive environment for developing and running signal processing simulations in a user-friendly. It is based on a block diagram system approach to support signal generation, analysis, and processing. AJDSP is designed for use by undergraduate and graduate students and DSP instructors. Its ex...
Last.Farib KhondokerH-Index: 1
view all 13 authors...
#1Henry Braun (ASU: Arizona State University)H-Index: 6
#2Pavan Turaga (ASU: Arizona State University)H-Index: 24
Last.Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
view all 6 authors...
Abstract Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors includi...
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May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Vivek Sivaraman Narayanaswamy (ASU: Arizona State University)H-Index: 1
#2Jayaraman J. Thiagarajan (LLNL: Lawrence Livermore National Laboratory)H-Index: 14
Last.Andreas Spanias (ASU: Arizona State University)H-Index: 28
view all 4 authors...
State-of-the-art speaker diarization systems utilize knowledge from external data, in the form of a pre-trained distance metric, to effectively determine relative speaker identities to unseen data. However, much of recent focus has been on choosing the appropriate feature extractor, ranging from pre-trained i–vectors to representations learned via different sequence modeling architectures (e.g. 1D-CNNs, LSTMs, attention models), while adopting off-the-shelf metric learning solutions. In this pap...
2 CitationsSource
May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Uday Shankar Shanthamallu (ASU: Arizona State University)H-Index: 2
#2Sunil Rao (ASU: Arizona State University)H-Index: 4
Last.Andreas Spanias (ASU: Arizona State University)H-Index: 28
view all 6 authors...
Machine Learning (ML) and Artificial Intelligence (AI) algorithms are enabling several modern smart products and devices. Furthermore, several initiatives such as smart cities and autonomous vehicles utilize AI and ML computational engines. The current and emerging applications and the growing industrial interest in AI necessitate introducing ML algorithms at the undergraduate level. In this paper, we describe a series of activities to introduce ML in undergraduate digital signal processing (DSP...
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#1Vivek Sivaraman Narayanaswamy (ASU: Arizona State University)H-Index: 1
#2Raja Ayyanar (ASU: Arizona State University)H-Index: 25
Last.Devarajan SrinivasanH-Index: 5
view all 5 authors...
A cyber-physical system (CPS) approach for optimizing the output power of photovoltaic (PV) energy systems is proposed. In particular, a novel connection topology reconfiguration strategy for PV arrays to maximize power output under partial shading conditions using neural networks is put forth. Depending upon an irradiance/shading profile of the panels, topologies, namely series parallel (SP), total cross tied (TCT) or bridge link (BL) produce different maximum power points (MPP). The connection...
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