Methods of improving the robustness of algorithms to modeling errors and limited data are derived. Deterministic beamforming, space-time random processes, optimal and adaptive algorithms, and the sensitivity of algorithm performance to modeling errors and limited data. Prereq: 6.7000 and ( 2.687 or ( 6.3010 and 18.06))Īdaptive and non-adaptive processing of signals received at arrays of sensors. Additional topics include image enhancement, image restoration and reconstruction, compression of images and videos, visual recognition, and the application of machine learning-based approaches to image processing. Core topics covered include models of image formation, image processing fundamentals, filtering in the spatial and frequency domains, image transforms, and feature extraction. Introduces models, theories, and algorithms key to digital image processing. Multirate signal processing, perfect reconstruction filter banks, and connection to wavelets. Spectral analysis, time-frequency analysis, relation to filter banks.

Discrete Fourier transform, DFT computation, and FFT algorithms. Parametric signal modeling, linear prediction, and lattice filters. Decimation, interpolation, and sampling rate conversion. Representation, analysis, and design of discrete time signals and systems. Explores a variety of applications, such as event classification, audio alignment, chord recognition, structural analysis, tempo and beat tracking, content-based audio retrieval, and audio decomposition. Addresses analysis methods including dynamic time warping, dynamic programming, self-similarity matrices, and matrix factorization.

Covers fundamental tools like windowing, feature extraction, discrete and short-time Fourier transforms, chromagrams, and onset detection. Hypothesis testing detection matched filters.Ħ.3020 Fundamentals of Music ProcessingĪnalyzes recorded music in digital audio form using advanced signal processing and optimization techniques to understand higher-level musical meaning. Least-mean square error estimation Wiener filtering. Probabilistic models stochastic processes, correlation functions, power spectra, spectral factorization. Topics include input-output and state-space models of linear systems driven by deterministic and random signals time- and transform-domain representations in discrete and continuous time and group delay. Prereq: 6.3000 and ( 6.3700, 6.3800, or 18.05)Ĭovers signals, systems and inference in communication, control and signal processing. Applications draw broadly from areas of contemporary interest with emphasis on both analysis and design. Topics include Fourier series, Fourier transforms, the Discrete Fourier Transform, sampling, convolution, deconvolution, filtering, noise reduction, and compression. Lecture: TR2 ( 3-270) Lab: TR4 ( 36-153) or TR4 ( 4-149) Recitation: TR3 ( 36-153, 4-149) +finalįundamentals of signal processing, focusing on the use of Fourier methods to analyze and process signals such as sounds and images.

Course 6 Home CI-M Subjects for Undergraduate Majors Evaluations (Certificates Required)

Essential video analytics 6.30