Statistical signal processing algorithms work to extract the good despite the “efforts” of the bad. This course covers the two basic approaches to statistical signal processing: estimation and detection. In estimation, we want to determine a signal’s waveform or some signal aspect(s). Typically the parameter or signal we want is buried in. Of Statistical Signal Processing: Detection Theory', S. The function subprograms Q.m and Qinv.m are required. Fig77new - computes Figure 7.7 in 'Fundamentals of Statistical Signal Processing: Detection Theory', S. Gendata - generates a complex or real AR, MA, or ARMA time series given the filter parameters. Statistical Signal Processing involves processing these signals and forms the backbone of modern communication and signal processing systems.This course will the three broad components of statistical signal processing: random signal modelling, estimation theory and detection theory.
- Statistical Signal Processing Applications
- Signal Processing Course
- Fundamentals Of Statistical Signal Processing
- Statistical Signal Processing Online Course
- Statistical Signal Processing By Louis L Scharf
Welcome to the home page of
Statistical Signal Processing Research Laboratory (SSPRL) and UT Acoustic Laboratory (UTAL)
This site provides general information about our research labs and the project contributors. For more information, contact Dr. Issa Panahi or our team members or just visit us. Click on these links to visit the Research, Resources, and Hearing Aid Project page to view our exciting demos and read more about our research on Audio DSP.
Mission
“To extend the frontiers of signal processing research and system design to improve the quality of life by developing novel methods and viable solutions to real-life problems.”
“We continue to strive for perfection in the audio, acoustics, and speech signal processing research and development for biomedical and commercial applications. “
Research
The SSPRL and UT-AL were founded by Dr. Issa Panahi in 2001. The research focus of the labs have been to explore and conquer new trends in Audio DSP Research and Technology, some of which are:
- Active Noise Control, Noise & Interference cancellation
- Speech Enhancement (Single channel/Multi-channel)
- Microphone Array, Source Localization, DOA Estimation and Tracking, Signal detection and estimation
- Blind source separation, De-Reverberation, Blind Deconvolution and System identification
- Sound Masking
- Ultrasonic/ Parametric Array loudspeakers
- Remote Sensing and Signal Control
- Adaptive signal processing, Microphone, and loudspeaker arrays
- MIMO signal analysis, spectrum estimation, and modeling
- MIMO Digital filtering.
- Welcome to IEEE Statistical Signal Processing Workshop 2021 (SSP 2021)! SSP 2021 is the 21st of a series of unique meetings that bring members of the IEEE Signal Processing Society together with researchers from allied fields such as bioinformatics, communications, machine learning, and statistics.
- Digital signal processing techniques are rapidly replacing the older analog techniques for synchronous detection in lock-in amplifiers. In these instruments the input signal is digitized by a fast, high-resolution A/D converter, and the signal amplitude and phase are determined by high-speed computations in a DSP.
Research Assistantship/ Student Worker Openings
Financial support available for highly qualified M.S. and Ph.D. students at SSPRL and UT-AL. Must be interested in:
- Android/iOS (Java,C/C++) programming for Audio DSP, and/or
- Speech Enhancement/Speech Processing, and/or
- Sound Masking/ Speech Masking, and/or
- Microphone Arrays.
Please visit Dr. Panahi or send us an email at [email protected] with your recent resume. Please visit the People page for more information.
News & Updates
“NIH Funds to Spur Development of Technology for Hearing Impaired“, featured in UT Dallas News Center, Sept. 20, 2016.
- SSPRL research: “Signal Processing Research Resonates with Hearing Loss Sufferers [Special Reports],” featured in IEEE Signal Processing Magazine, vol. 33, no. 1, pp. 11-162, Jan. 2016. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7366698&isnumber=7366379
Statistical signal and data processing through applications
COM-500
Statistical Signal Processing Applications
Lecturer(s) :
Ridolfi AndreaLanguage:
English
Summary
Building up on the basic concepts of sampling, filtering and Fourier transforms, we address stochastic modeling, spectral analysis, estimation and prediction, classification, and adaptive filtering, with an application oriented approach and hands-on numerical exercises.Content
1. Fundamentals of Statistical Signal and Data Processing: Signals and systems from the deterministic and the stochastic point of view; Processing and analysing signals and systems with a mathematical computing language.
2. Models, Methods, and Algorithms: Parametric and non-parametric signal models (wide sense stationary, Gaussian, Markovian, auto-regressive and white noise signals); Linear prediction and estimation (orthogonality principle and Wiener filter); Maximum likehood estimation and Bayesian a priori; Maximum a posteriori estimation. Shiva trilogyall about myths religion.
3. Statistical Signal and Data Processing Tools for Spread Spectrum Wireless Transmission: Coding and decoding of information using position of pulses (annihilating filter approach); Spectrum estimation (periodogram, line spectrum methods, smooth spectrum methods, harmonic signals).
4. Statistical Signal and Data Processing Tools for the Analysis of Neurobiological Recordings: Poisson process for neurobiological spikes; Characterization of multiple state neurons (Markovian models and maximum likelihood estimation); Classifying firing rates of neuron (Mixture models and the EM algorithm); Hidden Markov models; Spike sorting and Principal Component Analysis.
5. Statistical Signal and Data Processing Tools for Echo Cancellation: Adaptive filtering (least mean squares and recursive least squares); Adaptive echo cancellation and denoising.
Keywords
Statistical tools, spectral analysis, prediction, estimation, annihilating filter, mixture models, principal component analysis, stochastic processes, hidden Markov models, adaptive filtering, mathematical computing language (Matlab, Python, or similar).
Learning Prerequisites
Required courses
Stochastic Models in Communications (COM-300), Signal Processing for Communications (COM-303).
Recommended courses
Mathematical Foundations of Signal Processing (COM-514).
Important concepts to start the course
Calculus, Algebra, Fourier Transform, Z Transform, Probability, Linear Systems, Filters.
Learning Outcomes
Signal Processing Course
By the end of the course, the student must be able to:- Choose appropriate statistical tools to solve signal processing problems;
- Analyze real data using a mathematical computing language;
- Interpret spectral content of signals;
- Develop appropriate models for observed signals;
- Assess / Evaluate advantages and limitations of different statistical tools for a given signal processing problem;
- Implement numerical methods for processing signals.
Teaching methods
Ex cathedra with exercises and numerical examples.
Expected student activities
Attendance at lectures, completing exercises, testing presented methods with a mathematical computing language (Matlab, Python, or similar).
Assessment methods
- 20% midterm
- 10% mini project
- 70% Final exam
Supervision
| Office hours | Yes |
| Assistants | Yes |
| Forum | Yes |
Resources
Fundamentals Of Statistical Signal Processing
Bibliography
Background texts
- P. Prandoni,Signal Processing for Communications, EPFL Press;
- P. Bremaud, An Introduction to Probabilistic Modeling, Springer-Verlag, 1988;
- A.V. Oppenheim, R.W. Schafer, Discrete Time Signal Processing, Prentice Hall, 1989;
- B. Porat, A Course in Digital Signal Processing, John Wiley & Sons,1997;
- C.T. Chen, Digital Signal Processing, Oxford University Press;
- D. P. Bertsekas, J. N. Tsitsiklis, Introduction to Probability, Athena Scientific, 2002 (excellent book on probability).
Statistical Signal Processing Online Course
More advanced texts
Statistical Signal Processing By Louis L Scharf
- L. Debnath and P. Mikusinski, Introduction to Hilbert Spaces with Applications, Springer-Verlag, 1988;
- A.N. Shiryaev, Probability, Springer-Verlag, New York, 2nd edition, 1996;
- S.M. Ross, Introduction to Probability Models, Third edition, 1985;
- P. Bremaud, Markov Chains, Springer-Verlag, 1999;
- P. Bremaud, Mathematical Principles of Signal Processing, Springer-Verlag, 2002;
- S.M. Ross, Stochastic Processes, John Wiley, 1983;
- B. Porat, Digital Processing of Random Signals, Prentice Hall,1994;
- P.M. Clarkson, Optimal and Adaptive Signal Processing, CRC Press, 1993;
- P. Stoïca and R. Moses, Introduction to Spectral Analysis, Prentice-Hall, 1997.
Ressources en bibliothèque
Notes/Handbook
- Slides handouts;
- Collection of exercises.