Andreas Spanias is Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University (ASU). He is also the director of the Sensor Signal and Information Processing (SenSIP) center and the founder of the SenSIP industry consortium (now an NSF I/UCRC site). His research interests are in the areas of adaptive signal processing, speech processing, and sensor systems. He and his student team developed the computer simulation software Java-DSP and its award winning iPhone/iPad and Android versions. He is author of two text books: Audio Processing and Coding by Wiley and DSP; An Interactive Approach (2nd Ed.). He served as Associate Editor of the IEEE Transactions on Signal Processing and as General Co-chair of IEEE ICASSP-99. He also served as the IEEE Signal Processing Vice-President for Conferences. Andreas Spanias is co-recipient of the 2002 IEEE Donald G. Fink paper prize award and was elected Fellow of the IEEE in 2003. He served as Distinguished lecturer for the IEEE Signal processing society in 2004. He is a series editor for the Morgan and Claypool lecture series on algorithms and software.
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015
Audio signal modeling and simulation is important in several coding, noise removal, and recogniti... more Audio signal modeling and simulation is important in several coding, noise removal, and recognition applications. This paper focuses on implementing models for loudness estimation and their use in estimating parameters on iOS mobile devices (iPhones and iPads). We briefly address estimating excitation patterns and loudness through auditory models. These loudness estimation and other algorithms were implemented in the award winning educational iOS app iJDSP for performing DSP simulations on mobile devices. The modules were introduced to graduate students in the general signal processing area, to evaluate their effectiveness as teaching tools. The evaluation process involved giving the students a pre-quiz, guiding them through hands-on activities on the iOS app, and finally, a post-quiz. Assessments results were positive with noticeable improvement of student understanding of topics such as spectrograms and linear predictive coding.
2014 IEEE International Conference on Image Processing (ICIP), 2014
Human annotation in large scale image databases is time-consuming and error-prone. Since it is ve... more Human annotation in large scale image databases is time-consuming and error-prone. Since it is very hard to mine image databases using just visual features or textual descriptors, it is common to transform the image features into a semantically meaningful space. In this paper, we propose to perform image annotation in a semantic space inferred based on sparse representations. By constructing a semantic embedding for the visual features, that is constrained to be close to the tag embedding, we show that a robust inverse map can be used to predict the tags. Experiments using standard datasets show the effectiveness of the proposed approach in automatic image annotation when compared to existing methods.
Research and development in intelligent systems XXV: proceedings of AI-2008, the Twenty-Eighth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Oct 22, 2008
Sparse representations have been often used for inverse problems in signal and image processing. ... more Sparse representations have been often used for inverse problems in signal and image processing. Furthermore, frameworks for signal classification using sparse and overcomplete representations have been developed. Data-dependent representations using learned dictionaries have been significant in applications such as feature extraction and denoising. In this paper, our goal is to perform pattern classification in a domain referred to as the data representation domain, where data from different classes are sparsely ...
Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control, 2005., 2005
In this paper, we propose a unified Bayesian decision theory model to integrate various component... more In this paper, we propose a unified Bayesian decision theory model to integrate various components of a sensor network. We identify the key aspects of the Bayesian decision theory model, the functionalities of each network component, and the nature of interaction of the various network components in the proposed Bayesian framework. We also highlight some of the research avenues that
1991., IEEE International Sympoisum on Circuits and Systems, 1991
... Re(S(ip,)),i= l,.., H ) (2a) and S,, = {Zm(s(ip0)), z = 1 , .. , H ) (2b) where H denotes the... more ... Re(S(ip,)),i= l,.., H ) (2a) and S,, = {Zm(s(ip0)), z = 1 , .. , H ) (2b) where H denotes the number of harmonic components selected, and po the discrete frequency index corresponding to the fundamental frequency w The real and imaginary component vectors, Shr and Shl , are ...
The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, 2003
... 1~ ~ ~. . IO] Y. KO, T. Duman, and A. Spanias, I-DSP for communications, in 1111 T. ~ oulon... more ... 1~ ~ ~. . IO] Y. KO, T. Duman, and A. Spanias, I-DSP for communications, in 1111 T. ~ oulon , R Tsakalis, and A. Spanias, I-DSP-C, A co~mol 33rdASEUIEEE FIE-03, Boulder, No. 2003 . . systems -simulation environment for dismce learning: labs and assessment, in ...
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015
Audio signal modeling and simulation is important in several coding, noise removal, and recogniti... more Audio signal modeling and simulation is important in several coding, noise removal, and recognition applications. This paper focuses on implementing models for loudness estimation and their use in estimating parameters on iOS mobile devices (iPhones and iPads). We briefly address estimating excitation patterns and loudness through auditory models. These loudness estimation and other algorithms were implemented in the award winning educational iOS app iJDSP for performing DSP simulations on mobile devices. The modules were introduced to graduate students in the general signal processing area, to evaluate their effectiveness as teaching tools. The evaluation process involved giving the students a pre-quiz, guiding them through hands-on activities on the iOS app, and finally, a post-quiz. Assessments results were positive with noticeable improvement of student understanding of topics such as spectrograms and linear predictive coding.
2014 IEEE International Conference on Image Processing (ICIP), 2014
Human annotation in large scale image databases is time-consuming and error-prone. Since it is ve... more Human annotation in large scale image databases is time-consuming and error-prone. Since it is very hard to mine image databases using just visual features or textual descriptors, it is common to transform the image features into a semantically meaningful space. In this paper, we propose to perform image annotation in a semantic space inferred based on sparse representations. By constructing a semantic embedding for the visual features, that is constrained to be close to the tag embedding, we show that a robust inverse map can be used to predict the tags. Experiments using standard datasets show the effectiveness of the proposed approach in automatic image annotation when compared to existing methods.
Research and development in intelligent systems XXV: proceedings of AI-2008, the Twenty-Eighth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Oct 22, 2008
Sparse representations have been often used for inverse problems in signal and image processing. ... more Sparse representations have been often used for inverse problems in signal and image processing. Furthermore, frameworks for signal classification using sparse and overcomplete representations have been developed. Data-dependent representations using learned dictionaries have been significant in applications such as feature extraction and denoising. In this paper, our goal is to perform pattern classification in a domain referred to as the data representation domain, where data from different classes are sparsely ...
Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control, 2005., 2005
In this paper, we propose a unified Bayesian decision theory model to integrate various component... more In this paper, we propose a unified Bayesian decision theory model to integrate various components of a sensor network. We identify the key aspects of the Bayesian decision theory model, the functionalities of each network component, and the nature of interaction of the various network components in the proposed Bayesian framework. We also highlight some of the research avenues that
1991., IEEE International Sympoisum on Circuits and Systems, 1991
... Re(S(ip,)),i= l,.., H ) (2a) and S,, = {Zm(s(ip0)), z = 1 , .. , H ) (2b) where H denotes the... more ... Re(S(ip,)),i= l,.., H ) (2a) and S,, = {Zm(s(ip0)), z = 1 , .. , H ) (2b) where H denotes the number of harmonic components selected, and po the discrete frequency index corresponding to the fundamental frequency w The real and imaginary component vectors, Shr and Shl , are ...
The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, 2003
... 1~ ~ ~. . IO] Y. KO, T. Duman, and A. Spanias, I-DSP for communications, in 1111 T. ~ oulon... more ... 1~ ~ ~. . IO] Y. KO, T. Duman, and A. Spanias, I-DSP for communications, in 1111 T. ~ oulon , R Tsakalis, and A. Spanias, I-DSP-C, A co~mol 33rdASEUIEEE FIE-03, Boulder, No. 2003 . . systems -simulation environment for dismce learning: labs and assessment, in ...
This book provides a description of digital signal processing (DSP) algorithms along with a cover... more This book provides a description of digital signal processing (DSP) algorithms along with a coverage of compelling modern applications including cellular telephones and MP3 players. Topics covered include filter design, FFT, random signals, adaptive filters, and speech/audio processing. The mathematical underpinnings of DSP are accompanied by qualitative descriptions, solved problems, interactive computer simulations, and Java graphics. The presentation of signal processing concepts is enhanced with object-oriented programming examples supported by the universally accessible Java-DSP (J-DSP) and its mobile versions. The second edition has several new topics including 2-d image filtering, recent speech and audio processing algorithms, and new applications. This new edition has a support web site with code and video clips for J-DSP, iOS, Android and MATLAB. More than 100 new exercises added. The book can be used in an undergraduate or graduate DSP course. ISBN 978-1-4675-9892-7
A. Spanias, Digital Signal Processing; An Interactive Approach – 2nd Edition, 403 pages, Textbook with JAVA exercises, ISBN 978-1-4675-9892-7,Lulu Press On-demand Publishers Morrisville, NC, May 2014.
An in-depth treatment of algorithms and standards for perceptual coding of high-fidelity audio, t... more An in-depth treatment of algorithms and standards for perceptual coding of high-fidelity audio, this self-contained reference surveys and addresses all aspects of the field. Coverage includes signal processing and perceptual (psychoacoustic) fundamentals, details on relevant research and signal models, details on standardization and applications, and details on performance measures and perceptual measurement systems. It includes a comprehensive bibliography with over 600 references, computer exercises, and MATLAB-based projects for use in EE multimedia, computer science, and DSP courses. An ftp site containing supplementary material such as wave files, MATLAB programs and workspaces for the students to solve some of the numerical problems and computer exercises in the book can be found at
Andreas Spanias, Ted Painter, Venkatraman Atti, Audio Signal Processing and Coding, Hardcover 544 pages, ISBN: 0-471-79147-4, Wiley, Textbook with theory, problems, and MATLAB computer exercises. March 2007.
This software is intended to be an educational tool for students and instructors in DSP, and sig... more This software is intended to be an educational tool for students and instructors in DSP, and signals and systems courses. The development of Android JDSP (A-JDSP) is carried out using the Android SDK, which is a Java-based open source development platform. The proposed application contains basic DSP functions for convolution, sampling, FFT, filtering and frequency domain analysis, with a convenient graphical user interface. A description of the architecture, functions and planned assessments are presented in this paper.
The use of mobile devices and tablets in engineering education has been gaining lot of interest, ... more The use of mobile devices and tablets in engineering education has been gaining lot of interest, due to its interactive capabilities and its ability to stimulate student interest. On the other hand, this technology can also enable instructors to broaden the scope of their curriculum and increase student participation. In this paper, we describe an interactive application to perform signal processing simulations on iOS devices such as the iPhone and the iPad. Furthermore, we describe two laboratory exercises to introduce continuous/discrete convolution and filter design. The exercises and the proposed application will be evaluated by students of an undergraduate DSP course at Arizona State University during Fall 2011. Finally, we describe the planned assessment methodology which will enable us to provide prescriptive recommendations for using i-JDSP in DSP courses.
J. Liu, S. Hu, J. J. Thiagarajan, X. Zhang, S. Ranganath, M. K. Banavar, and K. N. Ramamurthy, “Interactive DSP laboratories on mobile phones and tablets,” Proceedings of IEEE ICASSP, 2012.
Java Digital Signal Processing (J-DSP) is a Java-based object-oriented programming environment th... more Java Digital Signal Processing (J-DSP) is a Java-based object-oriented programming environment that was developed at Arizona State University (ASU) for use in undergraduate- and graduate-level engineering classes. J-DSP is written as a platform-independent Java applet that resides on the Web and is thereby accessible by all students using a Web browser. We describe an innovative software extension to J-DSP, called 2D J-DSP, to accommodate on-line laboratories for two-dimensional digital signal processing. Two-dimensional DSP capabilities in J-DSP include: 2D signal generation; 2D FIR filter design and implementation; 2D transforms. Image processing capabilities include image restoration and enhancement. In order to illustrate 2D concepts graphically, contour (2D) and perspective (3D) plots have been incorporated in 2D J-DSP. Online laboratory exercises have been developed in the aforementioned areas for use in the graduate-level multidimensional signal processing and image processing courses at ASU, and are posted on the Website (http://jdsp.asu.edu). Statistical and qualitative evaluations that assess the learning experiences of the students that use 2D J-DSP are also presented.
Uploads
A. Spanias, Digital Signal Processing; An Interactive Approach – 2nd Edition, 403 pages, Textbook with JAVA exercises, ISBN 978-1-4675-9892-7,Lulu Press On-demand Publishers Morrisville, NC, May 2014.
http://www.lulu.com/shop/andreas-spanias/dsp-an-interactive-approach-second-edition/paperback/product-21640001.html
ftp://ftp.wiley.com/public/sci_tech_med/audio_signal
ISBN: 978-0-471-79147-8
Andreas Spanias, Ted Painter, Venkatraman Atti, Audio Signal Processing and Coding, Hardcover 544 pages, ISBN: 0-471-79147-4, Wiley, Textbook with theory, problems, and MATLAB computer exercises. March 2007.
Book Containing this software:
• A. Spanias, Digital Signal Processing; An Interactive Approach – 2nd Edition, 403 pages, Textbook with JAVA exercises, ISBN 978-1-4675-9892-7,Lulu Press On-demand Publishers Morrisville, NC, May 2014.
http://www.lulu.com/shop/andreas-spanias/dsp-an-interactive-approach-second-edition/paperback/product-21640001.html
Relevant Book with iPhone Simulations:
• A. Spanias, Digital Signal Processing; An Interactive Approach – 2nd Edition, 403 pages, Textbook with JAVA exercises, ISBN 978-1-4675-9892-7,Lulu Press On-demand Publishers Morrisville, NC, May 2014.
http://www.lulu.com/shop/andreas-spanias/dsp-an-interactive-approach-second-edition/paperback/product-21640001.html
J. Liu, S. Hu, J. J. Thiagarajan, X. Zhang, S. Ranganath, M. K. Banavar, and K. N. Ramamurthy, “Interactive DSP laboratories on mobile phones and tablets,” Proceedings of IEEE ICASSP, 2012.
Relevant Book at:
http://www.lulu.com/shop/andreas-spanias/dsp-an-interactive-approach-second-edition/paperback/product-21640001.html