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Lecture
Discrete Fourier Transform: Sampling and Interpretation
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Psychoacoustics and Signal Processing
Explores psychoacoustics, signal processing, and the brain's interpretation of sound frequencies, covering topics like the Missing Fundamental phenomenon and the inner workings of the cochlea.
Fourier Transform: Basics and Applications
Covers the basics of the Fourier transform and its applications in signal processing.
Signal Processing: Sampling and Reconstruction
Covers the concepts of quantization, coding, and sampling in signal processing.
Signals and Systems: Sampling Theorem and Applications
Discusses the sampling theorem and its applications in signal processing.
Discrete Fourier Transform: Frequency Periodicity and Reconstruction
Explores frequency periodicity in the discrete Fourier transform for signal reconstruction.
Sampling and Reconstruction Theory
Covers the concepts of analog, discrete, and digital signals, sampling times, frequencies, and pulses.
Signal Processing: Basics and Applications
Covers the basics of signal processing, including Fourier transform, linear systems, and signal manipulation.
Discrete Fourier Transform: Introduction
Introduces the discrete Fourier transform, a key tool for digital signal analysis.
The Sampling Theorem
Covers the sampling theorem, impulse train sampling, bandlimited signals, and the Nyquist rate.
Signal processing and vector spaces
Emphasizes the significance of vector spaces in signal processing, offering a unified framework for various signal types and system design.
