Digital Signal Processing Basics Questions and Answers for Viva

Answer-1: DSP is the manipulation of signals using digital techniques to modify or extract information from them.

Answer-2: Analog signals are continuous in nature, while digital signals are discrete, represented by a sequence of numbers.

Answer-3: The sampling theorem states that to accurately reconstruct a continuous signal from its samples, the sampling frequency must be at least twice the highest frequency component of the signal.

Answer-4: Aliasing occurs when the sampling frequency is insufficient, resulting in high-frequency components folding back into lower frequencies, distorting the signal.

Answer-5: The Nyquist frequency is half the sampling frequency and represents the maximum frequency that can be accurately represented in a digital signal.

Answer-6: Time-domain analysis deals with signals in the time dimension, while frequency-domain analysis examines signals in terms of their frequency content.

Answer-7: The Fourier transform decomposes a signal into its frequency components, revealing the signal's frequency content.

Answer-8: The DFT is a mathematical operation that computes the frequency components of a finite-length signal, while the FFT is an algorithm used to compute the DFT efficiently.

Answer-9: Convolution is an operation that combines two signals to produce a third signal, representing the mathematical integration of the two input signals.

Answer-10: Digital filters are algorithms used to remove or enhance certain frequency components of a signal by attenuating or amplifying specific frequency ranges.

Answer-11: FIR (Finite Impulse Response) filters have only finite-duration responses, while IIR (Infinite Impulse Response) filters have responses that can extend to infinity.

Answer-12: The Z-transform converts discrete-time signals from the time domain to the Z-domain, enabling analysis and manipulation in the frequency domain.

Answer-13: Decimation involves reducing the sampling rate of a signal, while interpolation involves increasing the sampling rate, both achieved by adding or removing samples from the signal.

Answer-14: Applications include audio processing, image processing, speech recognition, telecommunications, radar systems, and biomedical signal processing.

Answer-15: Windowing is a technique used to reduce spectral leakage in the Fourier transform by applying a window function to the signal before analysis.

Answer-16: DSP hardware is designed to efficiently perform mathematical operations required for signal processing tasks, often implemented in specialized processors or integrated circuits.

Answer-17: Quantization is the process of approximating continuous signal values with a finite set of discrete values, which introduces quantization error.

Answer-18: Digital signal processing offers greater precision, flexibility, and immunity to noise compared to analog processing. It also enables easy storage, transmission, and manipulation of signals.

Answer-19: Challenges include achieving high-speed processing, minimizing latency, optimizing memory usage, and ensuring numerical stability in algorithms.

Answer-20: DSP techniques are fundamental in extracting features from signals, such as speech or images, which are then used as inputs for machine learning algorithms, enabling tasks like pattern recognition, classification, and prediction.