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.
State-of-the-art under-determined audio source separation systems rely on supervised end-end trai... more State-of-the-art under-determined audio source separation systems rely on supervised end-end training of carefully tailored neural network architectures operating either in the time or the spectral domain. However, these methods are severely challenged in terms of requiring access to expensive source level labeled data and being specific to a given set of sources and the mixing process, which demands complete retraining when those assumptions change. This strongly emphasizes the need for unsupervised methods that can leverage the recent advances in data-driven modeling, and compensate for the lack of labeled data through meaningful priors. To this end, we propose a novel approach for audio source separation based on generative priors trained on individual sources. Through the use of projected gradient descent optimization, our approach simultaneously searches in the source-specific latent spaces to effectively recover the constituent sources. Though the generative priors can be defined in the time domain directly, e.g. WaveGAN, we find that using spectral domain loss functions for our optimization leads to good-quality source estimates. Our empirical studies on standard spoken digit and instrument datasets clearly demonstrate the effectiveness of our approach over classical as well as state-of-the-art unsupervised baselines.
As more utility scale photovoltaic (PV) power plants are installed, there is a need to improve mo... more As more utility scale photovoltaic (PV) power plants are installed, there is a need to improve monitoring and management of PV arrays. A procedure is presented here for optimizing the electrical configuration of a PV array under a variety of operating conditions. Computer simulations and analysis with synthetic and real data are presented in this paper. The performance of the optimization system is evaluated for a variety of partial shading conditions using a SPICE circuit simulator. In general, a 4 − 5% gain in power output is achievable by employing active switching in order to reconfigure the array's electrical configuration.
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.
We provide an overview of recent work on distributed and agile sensing algorithms and their imple... more We provide an overview of recent work on distributed and agile sensing algorithms and their implementation. Modern sensor systems with embedded processing can allow for distributed sensing to continuously infer intelligent information as well as for agile sensing to configure systems in order to maintain a desirable performance level. We examine distributed inference techniques for detection and estimation at the fusion center and wireless networks for the sensor systems for real time scenarios. We also study waveform-agile sensing, which includes methods for adapting the sensor transmit waveform to match the environment and to optimize the selected performance metric. We specifically concentrate on radar and underwater acoustic signal transmission environments. As we consider systems with potentially large number of sensors, we discuss the use of resource-agile implementation approaches based on multiple-core processors in order to efficiently implement the computationally intensive processing in configuring the sensors. These resource-agile approaches can be extended to also optimize sensing in distributed sensor networks.
A distributed estimation problem is considered with multiple-access channels between sensors and ... more A distributed estimation problem is considered with multiple-access channels between sensors and a fusion center. The sensors phase-modulate their noisy observations before transmitting them to the fusion center, where a signal parameter is estimated. The asymptotic efficiency of this estimator is then determined by using two inequalities that relate the Fisher information and the characteristic function. A necessary and sufficient condition for equality is found for the first time in the literature. The loss in efficiency of the distributed estimation scheme relative to the centralized approach is quantified for different sensing noise distributions. It is shown that the distributed estimator does not incur an efficiency loss if and only if the sensing noise distribution is Gaussian.
IEEE Transactions on Circuits and Systems I: Regular Papers, 2014
Distributed estimation uses many inexpensive sensors to compose an accurate estimate of a given p... more Distributed estimation uses many inexpensive sensors to compose an accurate estimate of a given parameter. It is frequently implemented using wireless sensor networks. There have been several studies on optimizing power allocation in wireless sensor networks used for distributed estimation, the vast majority of which assume linear radio-frequency amplifiers. Linear amplifiers are inherently inefficient, so in this dissertation nonlinear amplifiers are examined to gain efficiency while operating distributed sensor networks. This research presents a method to boost efficiency by operating the amplifiers in the nonlinear region of operation. Operating amplifiers nonlinearly presents new challenges. First, nonlinear amplifier characteristics change across manufacturing process variation, temperature, operating voltage, and aging. Secondly, the equations conventionally used for estimators and performance expectations in linear amplify-andforward systems fail. To compensate for the first challenge, predistortion is utilized not to linearize amplifiers but rather to force them to fit a common nonlinear limiting amplifier model close to the inherent amplifier performance. This minimizes the power impact and the training requirements for predistortion. Second, new estimators are required that account for transmitter nonlinearity. This research derives analytically and confirms via simulation new estimators and performance expectation equations for use in nonlinear distributed estimation.
The Java-DSP (J-DSP) on-line laboratory software has been developed from the ground up at Arizona... more The Java-DSP (J-DSP) on-line laboratory software has been developed from the ground up at Arizona State University (ASU) to support the computer lab portion of the senior-level DSP course. J-DSP provides capabilities for web-based DSP simulations that can be run using a PC with a Javaenabled browser. J-DSP is accompanied by exercises that actively engage students in several concepts including Z-transforms, filter design, spectral analysis, and random signal processing. Tools and on-line instruments for assessment of the J-DSP software and the associated lab exercises have been developed and described in this letter. Statistical analysis of the pre/post assessment data revealed that the effect of student involvement and the student learning has been enhanced by using J-DSP.
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 ...
Synthesis Lectures on Image, Video, and Multimedia Processing, 2014
Image understanding has been playing an increasingly crucial role in several inverse problems and... more Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blind source separation, super-resolution, and classification. e primary goal of this book is to present the theory and algorithmic considerations in using sparse models for image understanding and computer vision applications. To this end, algorithms for obtaining sparse representations and their performance guarantees are discussed in the initial chapters. Furthermore, approaches for designing overcomplete, data-adapted dictionaries to model natural images are described. e development of theory behind dictionary learning involves exploring its connection to unsupervised clustering and analyzing its generalization characteristics using principles from statistical learning theory. An exciting application area that has benefited extensively from the theory of sparse representations is compressed sensing of image and video data. eory and algorithms pertinent to measurement design, recovery, and modelbased compressed sensing are presented. e paradigm of sparse models, when suitably integrated with powerful machine learning frameworks, can lead to advances in computer vision applications such as object recognition, clustering, segmentation, and activity recognition. Frameworks that enhance the performance of sparse models in such applications by imposing constraints based on the prior discriminatory information and the underlying geometrical structure, and kernelizing the sparse coding and dictionary learning methods are presented. In addition to presenting theoretical fundamentals in sparse learning, this book provides a platform for interested readers to explore the vastly growing application domains of sparse representations.
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 ...
33rd Annual Frontiers in Education, 2003. FIE 2003., 2003
Time -frequency representations (TFRs) such as the spectrogram are important two-dimensional to... more Time -frequency representations (TFRs) such as the spectrogram are important two-dimensional tools for processing time-varying signals. In this paper, we present the Java software module we developed for the spectrogram implementation together with the associated programming environment. Our aim is to introduce to students the advanced concepts of TFRs at an early stage in their education without requiring a rigorous theoretical background. We developed two sets of exercises using the spectrogram based on signal analysis and speech processing together with on-line evaluation forms to assess student learning experiences. In the paper, we also provide the positive statistical and qualitative feedback we obtained when the Java software and corresponding exercises were used in a signal processing course.
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 ...
2012 IEEE International Conference on Emerging Signal Processing Applications, 2012
Sparse coding of image patches is a compact but computationally expensive method of representing ... more Sparse coding of image patches is a compact but computationally expensive method of representing images. As part of our SenSIP consortium industry projects, we implement the Orthogonal Matching Pursuit algorithm using a single CUDA kernel on a GPU and sparse codes for image patches are obtained in parallel. Image-based "exact search" and "visually similar search" using the image patch sparse codes are performed. Results demonstrate large speed-up over CPU implementations and achieve good retrieval performance.
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
Recent work in audio information retrieval has demonstrated the effectiveness of combining semant... more Recent work in audio information retrieval has demonstrated the effectiveness of combining semantic information, such as descriptive, tags with acoustic content. However, these methods largely ignore the possibility of tag queries that do not yet exist in the database and the possibility of similar terms. In this work, we propose a network structure integrating similarity between semantic tags, content-based similarity between environmental audio recordings, and the collective sound descriptions provided by a user community. We then demonstrate the effectiveness of our approach by comparing the use of existing similarity measures for incorporating new vocabulary into an audio annotation and retrieval system.
State-of-the-art under-determined audio source separation systems rely on supervised end-end trai... more State-of-the-art under-determined audio source separation systems rely on supervised end-end training of carefully tailored neural network architectures operating either in the time or the spectral domain. However, these methods are severely challenged in terms of requiring access to expensive source level labeled data and being specific to a given set of sources and the mixing process, which demands complete retraining when those assumptions change. This strongly emphasizes the need for unsupervised methods that can leverage the recent advances in data-driven modeling, and compensate for the lack of labeled data through meaningful priors. To this end, we propose a novel approach for audio source separation based on generative priors trained on individual sources. Through the use of projected gradient descent optimization, our approach simultaneously searches in the source-specific latent spaces to effectively recover the constituent sources. Though the generative priors can be defined in the time domain directly, e.g. WaveGAN, we find that using spectral domain loss functions for our optimization leads to good-quality source estimates. Our empirical studies on standard spoken digit and instrument datasets clearly demonstrate the effectiveness of our approach over classical as well as state-of-the-art unsupervised baselines.
As more utility scale photovoltaic (PV) power plants are installed, there is a need to improve mo... more As more utility scale photovoltaic (PV) power plants are installed, there is a need to improve monitoring and management of PV arrays. A procedure is presented here for optimizing the electrical configuration of a PV array under a variety of operating conditions. Computer simulations and analysis with synthetic and real data are presented in this paper. The performance of the optimization system is evaluated for a variety of partial shading conditions using a SPICE circuit simulator. In general, a 4 − 5% gain in power output is achievable by employing active switching in order to reconfigure the array's electrical configuration.
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.
We provide an overview of recent work on distributed and agile sensing algorithms and their imple... more We provide an overview of recent work on distributed and agile sensing algorithms and their implementation. Modern sensor systems with embedded processing can allow for distributed sensing to continuously infer intelligent information as well as for agile sensing to configure systems in order to maintain a desirable performance level. We examine distributed inference techniques for detection and estimation at the fusion center and wireless networks for the sensor systems for real time scenarios. We also study waveform-agile sensing, which includes methods for adapting the sensor transmit waveform to match the environment and to optimize the selected performance metric. We specifically concentrate on radar and underwater acoustic signal transmission environments. As we consider systems with potentially large number of sensors, we discuss the use of resource-agile implementation approaches based on multiple-core processors in order to efficiently implement the computationally intensive processing in configuring the sensors. These resource-agile approaches can be extended to also optimize sensing in distributed sensor networks.
A distributed estimation problem is considered with multiple-access channels between sensors and ... more A distributed estimation problem is considered with multiple-access channels between sensors and a fusion center. The sensors phase-modulate their noisy observations before transmitting them to the fusion center, where a signal parameter is estimated. The asymptotic efficiency of this estimator is then determined by using two inequalities that relate the Fisher information and the characteristic function. A necessary and sufficient condition for equality is found for the first time in the literature. The loss in efficiency of the distributed estimation scheme relative to the centralized approach is quantified for different sensing noise distributions. It is shown that the distributed estimator does not incur an efficiency loss if and only if the sensing noise distribution is Gaussian.
IEEE Transactions on Circuits and Systems I: Regular Papers, 2014
Distributed estimation uses many inexpensive sensors to compose an accurate estimate of a given p... more Distributed estimation uses many inexpensive sensors to compose an accurate estimate of a given parameter. It is frequently implemented using wireless sensor networks. There have been several studies on optimizing power allocation in wireless sensor networks used for distributed estimation, the vast majority of which assume linear radio-frequency amplifiers. Linear amplifiers are inherently inefficient, so in this dissertation nonlinear amplifiers are examined to gain efficiency while operating distributed sensor networks. This research presents a method to boost efficiency by operating the amplifiers in the nonlinear region of operation. Operating amplifiers nonlinearly presents new challenges. First, nonlinear amplifier characteristics change across manufacturing process variation, temperature, operating voltage, and aging. Secondly, the equations conventionally used for estimators and performance expectations in linear amplify-andforward systems fail. To compensate for the first challenge, predistortion is utilized not to linearize amplifiers but rather to force them to fit a common nonlinear limiting amplifier model close to the inherent amplifier performance. This minimizes the power impact and the training requirements for predistortion. Second, new estimators are required that account for transmitter nonlinearity. This research derives analytically and confirms via simulation new estimators and performance expectation equations for use in nonlinear distributed estimation.
The Java-DSP (J-DSP) on-line laboratory software has been developed from the ground up at Arizona... more The Java-DSP (J-DSP) on-line laboratory software has been developed from the ground up at Arizona State University (ASU) to support the computer lab portion of the senior-level DSP course. J-DSP provides capabilities for web-based DSP simulations that can be run using a PC with a Javaenabled browser. J-DSP is accompanied by exercises that actively engage students in several concepts including Z-transforms, filter design, spectral analysis, and random signal processing. Tools and on-line instruments for assessment of the J-DSP software and the associated lab exercises have been developed and described in this letter. Statistical analysis of the pre/post assessment data revealed that the effect of student involvement and the student learning has been enhanced by using J-DSP.
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 ...
Synthesis Lectures on Image, Video, and Multimedia Processing, 2014
Image understanding has been playing an increasingly crucial role in several inverse problems and... more Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blind source separation, super-resolution, and classification. e primary goal of this book is to present the theory and algorithmic considerations in using sparse models for image understanding and computer vision applications. To this end, algorithms for obtaining sparse representations and their performance guarantees are discussed in the initial chapters. Furthermore, approaches for designing overcomplete, data-adapted dictionaries to model natural images are described. e development of theory behind dictionary learning involves exploring its connection to unsupervised clustering and analyzing its generalization characteristics using principles from statistical learning theory. An exciting application area that has benefited extensively from the theory of sparse representations is compressed sensing of image and video data. eory and algorithms pertinent to measurement design, recovery, and modelbased compressed sensing are presented. e paradigm of sparse models, when suitably integrated with powerful machine learning frameworks, can lead to advances in computer vision applications such as object recognition, clustering, segmentation, and activity recognition. Frameworks that enhance the performance of sparse models in such applications by imposing constraints based on the prior discriminatory information and the underlying geometrical structure, and kernelizing the sparse coding and dictionary learning methods are presented. In addition to presenting theoretical fundamentals in sparse learning, this book provides a platform for interested readers to explore the vastly growing application domains of sparse representations.
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 ...
33rd Annual Frontiers in Education, 2003. FIE 2003., 2003
Time -frequency representations (TFRs) such as the spectrogram are important two-dimensional to... more Time -frequency representations (TFRs) such as the spectrogram are important two-dimensional tools for processing time-varying signals. In this paper, we present the Java software module we developed for the spectrogram implementation together with the associated programming environment. Our aim is to introduce to students the advanced concepts of TFRs at an early stage in their education without requiring a rigorous theoretical background. We developed two sets of exercises using the spectrogram based on signal analysis and speech processing together with on-line evaluation forms to assess student learning experiences. In the paper, we also provide the positive statistical and qualitative feedback we obtained when the Java software and corresponding exercises were used in a signal processing course.
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 ...
2012 IEEE International Conference on Emerging Signal Processing Applications, 2012
Sparse coding of image patches is a compact but computationally expensive method of representing ... more Sparse coding of image patches is a compact but computationally expensive method of representing images. As part of our SenSIP consortium industry projects, we implement the Orthogonal Matching Pursuit algorithm using a single CUDA kernel on a GPU and sparse codes for image patches are obtained in parallel. Image-based "exact search" and "visually similar search" using the image patch sparse codes are performed. Results demonstrate large speed-up over CPU implementations and achieve good retrieval performance.
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
Recent work in audio information retrieval has demonstrated the effectiveness of combining semant... more Recent work in audio information retrieval has demonstrated the effectiveness of combining semantic information, such as descriptive, tags with acoustic content. However, these methods largely ignore the possibility of tag queries that do not yet exist in the database and the possibility of similar terms. In this work, we propose a network structure integrating similarity between semantic tags, content-based similarity between environmental audio recordings, and the collective sound descriptions provided by a user community. We then demonstrate the effectiveness of our approach by comparing the use of existing similarity measures for incorporating new vocabulary into an audio annotation and retrieval system.
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.
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Papers by Andreas Spanias
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