Signal Processing

Digital Signal Processing (DSP) is a core area of signal processing that utilizes computational techniques from computer science to analyze and manipulate signals represented as sequences of numbers. It involves converting real-world analog signals, such as sound, images, or sensor readings, into a digital format through a process called sampling. Once in this discrete form, algorithms are applied to perform a vast range of operations, including filtering to remove unwanted noise, compressing data for efficient storage and transmission, and analyzing frequencies using tools like the Fast Fourier Transform (FFT), making it fundamental to modern technology from mobile phones to medical imaging.

Speech Synthesis and Processing is a field at the intersection of Computer Science and Signal Processing that focuses on the computational analysis and generation of human speech. It encompasses two main areas: speech processing, which uses algorithms to analyze audio signals for tasks like automatic speech recognition (converting speech to text) and speaker identification; and speech synthesis, or text-to-speech (TTS), which involves artificially creating human-like speech from written text. By applying signal processing techniques to manipulate audio waveforms and machine learning models to understand linguistic patterns, this discipline enables more natural and intuitive human-computer interaction.