Courses

Learn principles and techniques of signal processing in linear, time-invariant (LTI) systems. Covers continuous-time and discrete-time signals and systems, sampling, filter design. Free, interactive course. Summary This course gives an introduction to the concepts, mathematics, principles and techniques of signal processing in linear, time-invariant (LTI) systems. The course covers analysis methods for both continuous-time and discrete-time signals and systems, presents sampling and gives an elementary introduction to filter design. Many everyday signal processing examples are included. The concepts and methods of signals and systems presented here play an important role in many areas of science and engineering, and therefore the course should be of interest to a broad range of students. Print and ebook versions of this material are available as part of the Wolfram eTextbook Series. Wolfram AI Course Assistant is available for this interactive course. Featured Products & Technologies: Wolfram Language (available in Mathematica and Wolfram|One) You'll Learn To Perform basic operations on elementary signals and sequences Work with LTI systems and understand their properties Work with differential and difference equation models of LTI systems Obtain system responses using convolution integrals and sums Gain insight into signals and systems through Fourier analysis Use Laplace and z-transforms to analyze systems and solve differential and difference equations Understand continuous-time sampling and the related concept of aliasing Design digital filters using the bilinear transformation and the window method

Course Overview: Familiarize yourself with the course.Spectral Analysis Workflow: Import signals into MATLAB and view power spectra.Preprocessing Signals: Clean up time base and align signals.Spectral Analysis: Perform spectral analysis to view signals in the frequency domain.Filtering: Filter signals using basic techniques.Signal Measurements: Extract information from signals.Conclusion: Learn next steps and give feedback on the course.

This Specialization provides a full course in Digital Signal Processing, with a focus on audio processing and data transmission. You will start from the basic concepts of discrete-time signals and proceed to learn how to analyze data via the Fourier transform, how to manipulate data via digital filters and how to convert analog signals into digital format. Finally, you will also discover how to implement real-time DSP algorithms on a general-purpose microcontroller. The solid theoretical bases provided by the four courses in this specialization are complemented by applied examples in Python, in the form of Jupyter Notebooks; exercises with solutions provide a wealth of examples in order to tackle the weekly homework.

Embark on an 8-hour comprehensive journey through the fundamentals of signal processing. Begin with a crash course on complex numbers before diving into time domain concepts, exploring signal representation, transformations, properties, and elementary signals. Progress to system properties, LTI systems, impulse response, and convolution. Transition to the frequency domain, mastering Fourier series, filtering, Gibbs phenomenon, and Fourier transform. Delve into frequency spectra, response, modulation, equalization, and sampling theory. Conclude with an in-depth exploration of the Laplace domain, covering Laplace transform, Region of Convergence, transfer functions, and system properties. Gain a solid foundation in signal processing principles through this comprehensive tutorial series.

Explore a comprehensive 12-hour course on digital signal processing, covering fundamental concepts and advanced techniques. Begin with an introduction to DSP and progress through frequency domain sampling, discrete Fourier transforms, and their properties. Master DFT computation methods, including matrix and FFT algorithms. Dive into circular and linear convolution, overlap methods, and FIR filter design using various windows. Examine digital filter representations, DSP architecture, and hardware units. Study fixed-point and floating-point formats in digital signal processors. Conclude with an in-depth look at IIR filter design, including bilinear transformation and Butterworth filter problems. Gain practical problem-solving skills through numerous examples and applications throughout the course.

Instructor: Prof. S.C. Dutta Roy, Department of Electrical Engineering, IIT Delhi. This course discusses topics on digital signal processing: Review of Signals and Systems; Characterization, Description, and Testing of Digital Systems; Discrete Fourier Transform; z-Transform; Discrete-Time Systems in Frequency Domain; Simple Digital Filters; Digital Processing of Continuous-Time Signals; Analog Filter Design; Digital Filter Structures; FIR and IIR Filter Design.

COURSE OUTLINE: This course will introduce you to the basics of discrete-time sequences, z-transform, frequency response of discrete-time systems, sampling, and the DFT. ABOUT INSTRUCTOR: C.S. Ramalingam obtained his BE (ECE) from the University of Madras, an M.Tech degree from IIT Kharagpur, and a Ph.D in Electrical Engineering from the Univ. of Rhode Island, Kingston, USA. He was a Member of Technical Staff at the DSPS R&D Center of Texas Instruments (Dallas, TX) from 1995—2001. Since 2001 he is with the Department of Electrical Engineering at IIT Madras, where he is currently Associate Professor. His areas of interest are Signal Processing with applications to Speech Analysis, Synthesis, and Coding

Instructor: Prof. T.K. Basu, Department of Electrical Engineering, IIT Kharagpur. The course covers lessons in digital signal processing. Topics include Discrete-Time Signal and System, Frequency Domain Representation of Discrete Signals, z-Transform, Solution of Difference Equation, Tutorial on Discrete-Time Signals and their Transforms, Relation between Discrete-Time and Continuous Signals, Discrete Fourier Transform, State Space Representation, Filters, FIR Filters, IIR Filters, Computer-Aided Design of Filters, Lattice Filter, Effects of Quantization, Relationship between Real and Imaginary Parts of DTFT, and Multirate Signal Processing.

This course was developed in 1987 by the MIT Center for Advanced Engineering Studies. It was designed as a distance-education course for engineers and scientists in the workplace. Advances in integrated circuit technology have had a major impact on the technical areas to which digital signal processing techniques and hardware are being applied. A thorough understanding of digital signal processing fundamentals and techniques is essential for anyone whose work is concerned with signal processing applications. Digital Signal Processing begins with a discussion of the analysis and representation of discrete-time signal systems, including discrete-time convolution, difference equations, the z-transform, and the discrete-time Fourier transform. Emphasis is placed on the similarities and distinctions between discrete-time. The course proceeds to cover digital network and nonrecursive (finite impulse response) digital filters. Digital Signal Processing concludes with digital filter design and a discussion of the fast Fourier transform algorithm for computation of the discrete Fourier transform.

6.341x is designed to provide both an in-depth and an intuitive understanding of the theory behind modern discrete-time signal processing systems and applications. The course begins with a review and extension of the basics of signal processing including a discussion of group delay and minimum-phase systems, and the use of discrete-time (DT) systems for processing of continuous-time (CT) signals. The course develops flow-graph and block diagram structures including lattice filters for implementing DT systems, and techniques for the design of DT filters. Parametric signal modeling and the efficient implementation of DT multirate and sampling rate conversion systems are discussed and developed. An in-depth development of the DFT and its computation as well as its use for spectral analysis and for filtering is presented. This component of the course includes a careful and insightful development of the relationship between the time-dependent Fourier transform and the use of filter banks for both spectral analysis and signal coding. 6.341x is organized around eleven units each typically consisting of a set of two to four topics. The source material for learning each topic includes suggested reading in the course text, clarifying notes, other related reading, and video excerpts and will include an interactive on-line discussion forum. The course text is the widely used text by Oppenheim and Schafer (third edition). The video segments are adapted from live video recordings of the MIT residential course. Each topic includes a set of automatically-graded exercises for self-assessment and to help in digesting and understanding the basics of the topic, and in some cases to preview topics. A typical unit in the course concludes with a set of more extensive problems to help in integrating the topics and developing a deeper understanding. Automatic grading of your answers to these problems as well as solutions will be provided. 6.341x and this freely-available version were developed through the support and encouragement of the MIT Department of Electrical Engineering and Computer Science, the MIT Office of Digital Learning, and the MIT Research Laboratory of Electronics. This course can be cited as: Alan V. Oppenheim and Thomas A. Baran, 6.341x Discrete-Time Signal Processing, on edX, Summer 2016. https://www.edx.org/course/discrete-time-signal-processing-mitx-6-341x-1

Introduction: Familiarize yourself with the course.Generating Signals and Common Signal Operations: Generate different types of sampled signals. Perform operations in the time-domain like changing the sample rate of a signal or shifting the frequency content without introducing unwanted artifacts.Estimating Power Spectral Density: Estimate the power spectrum of signals with different frequency components. Explore standard techniques to improve the accuracy of your estimation.Improving the Power Spectral Density Estimate: Explore different spectral analysis techniques to improve results for noisy, time-varying, or short signals.Characterizing Digital Filters: Visualize filter characteristics in different domains to understand how a filter will modify the time-domain and frequency-domain of your signals.Designing Digital Filters: Design digital FIR and IIR filters using common filter response types. Start with a set of specifications or a preferred design algorithm.Streaming Signal Processing: Process streaming signals by dividing input data into frames and processing each frame as it is acquired.Conclusion: Learn next steps and give feedback on the course.

Dive into a comprehensive 15-hour video lecture series on Signals and Systems, an introductory course in analog and digital signal processing. Explore key concepts applicable to various fields including seismic data processing, communications, speech processing, image processing, consumer electronics, and defense electronics. Learn from instructor Alan V. Oppenheim through 20 in-depth lectures, covering topics from sampling techniques to advanced signal processing methods. Gain practical insights through demonstrations and theoretical knowledge essential for understanding modern digital systems. Access this educational resource under the Creative Commons BY-NC-SA license, offering a solid foundation for students and professionals interested in digital signal processing and its wide-ranging applications.

Students can master the basic theory of digital speech signal processing and mathematical analysis and modeling method of sound object through this course. Also, they will understand the specific applications and cutting-edge technology in the field of speech signal processing. This course aims to improve the ability of using basic theory to solve practical problems. The knowledge includes digital representations of speech signals, overview of phonetics, speech production model, auditory systems and speech perception, short-time analysis of speech signal and key techniques of speech signal processing(linear predictive analysis, homomorphic analysis, vector quantization and Hidden Markov Model);specific application areas of digital speech signal processing include speech coding, speech recognition, speech synthesis and speech enhancement; applications of cutting-edge technologies such as deep learning in the field of speech signal processing.

Learn about the fundamentals of signal processing and receiver technologies in this one-hour lecture from NPTEL-NOC IITM. Explore key concepts in signal acquisition, processing techniques, and receiver architectures that form the backbone of modern communication systems. Gain insights into how signals are captured, filtered, amplified, and decoded in various applications.

Dive into the world of advanced digital signal processing with this comprehensive 1.5-hour course. Review fundamental concepts of signal processing, including frequency analysis and analog-to-digital conversion. Explore the quantization process and compare analog and digital signal processing techniques, weighing their respective advantages and disadvantages. Learn about discrete-time signals, their representation, and classification based on energy, power, periodicity, and symmetry. Examine various discrete-time systems, their characteristics, and practical examples to deepen your understanding of this crucial field in modern technology.

Dive into a comprehensive workshop on practical digital signal processing, exploring the lifecycle of audio signals from continuous acoustic to discrete digital forms. Learn essential concepts including sampling theory, filtering techniques, block vs sample-based processing, and transformations between time and frequency domains. Gain hands-on experience with MATLAB, covering topics such as mathematical notation, properties of sine waves, continuous time sound and signals, plotting techniques, sampling frequency, interpolation, anti-aliasing, and working with indexable vectors. Master the art of adding sinusoids, changing sampling frequencies, and troubleshooting common MATLAB issues in this in-depth tutorial designed for both newcomers and experienced audio developers.

ABOUT THE COURSE:This course explores the fundamental and advanced concepts of signal processing, including time-domain and frequency-domain techniques, time-frequency analysis, and architectural implementations. The course is designed to provide a comprehensive understanding of real-time signal processing systems, with practical insights into algorithm design and hardware architectures. It is aimed at undergraduate, postgraduate, and PhD students, and is highly relevant for those pursuing careers in telecommunications, electronics, and machine learning.INTENDED AUDIENCE: BTech 4th year in electrical sciences, MTech in signal processing, communication, instrumentation, PhD in signal processing and machine learningPREREQUISITES: Signals and SystemsEmbedded SystemsINDUSTRY SUPPORT: QualcommEricssonIntelTexas Instruments

Explore topological signal processing and learning in this IEEE Signal Processing Society webinar presented by Sergio Barbarossa from Sapienza University of Rome. Gain insights into advanced techniques for analyzing and processing signals using topological methods as part of the Data sciEnce on GrAphS (DEGAS) Webinar Series. Discover how topology can be applied to signal processing and machine learning, enhancing your understanding of complex data structures and their analysis.

Dive into a comprehensive online course on Digital Signal Processing using MATLAB. Master key concepts including impulse response of discrete time systems, linear and circular convolution, discrete and fast Fourier transforms, and various filter designs such as Butterworth and Chebyshev using bilinear transformation and impulse invariance. Explore window methods for FIR filter design, power spectral density analysis, and sampling rate reduction techniques. Develop practical skills in implementing DSP algorithms and gain a deep understanding of signal processing principles through hands-on exercises in MATLAB.

Explore digital signal processing and Swift's capabilities in a 19-minute conference talk from try! Swift Tokyo 2018. Dive deep into the field of digital signal processing and discover how Apple's Accelerate framework utilizes Fast Fourier Transforms for audio frequency detection. Learn how to leverage Swift's power to break down audio sampling rates through an interactive live demo. Speaker Daisy Ramos, an experienced iOS developer, shares insights on data-driven solutions and new features in consumer banking apps. Gain valuable knowledge about applying Swift to complex audio processing tasks and expand your understanding of this powerful programming language's applications in signal processing.