Advanced Techniques in Digital Signal Processing
Coordinated by: Dragoș Burileanu, Șerban Mihalache
Rooms: B029
Description:
This subject is studied within the field of Electronic Engineering, Telecommunications and Information Technologies / specialization Microelectronics, optoelectronics and nanotechnologies, and aims to familiarize students with the several advanced topics in the field of digital signal processing (statistical processing of random signals, spectral analysis, adaptive filtering, multirate signal processing, neural networks, and machine learning methods), with applications in communication, speech technology, and audio processing. The objective is to understand the phenomena underlying the studied techniques and their implementation in real systems, as well as to introduce modern signal processor architectures and their use in real-time processing systems. The numerous examples and detailed explanations given in the lecture notes and chapters help both to clarify more difficult theoretical aspects and to solve practical applications and problems, relevant for engaging the students in the learning process. Additionally, the laboratory applications have as objective acquiring practical skills related to the key theoretical concepts taught in class. The applications include various software simulations using a high-level programming environment (MATLAB).
The subject addresses the following basic ideas and specific concepts: discrete random signals and the response of digital filters to random signals, non-parametric and parametric methods of spectral analysis, random signal modeling, linear estimation and the Wiener filter, adaptive filtering, multirate signal processing, speech signal analysis and processing, digital processing techniques for audio applications, the use of artificial neural networks in signal processing, digital signal processors for real DSP applications. All these contribute to providing students with an overview of the methodological and procedural benchmarks related to the DSP field.
Laboratory contents:
- Discrete-time deterministic signals: FFT, digital filters (MATLAB review). Discrete-time random signals: representation, statistical parameters
- Spectral analysis for random signals. Linear estimation; the Wiener filter
- Adaptive filters. The LMS and NLMS algorithms; applications
- Multirate signal processing: decimation, interpolation, resampling by rational factors; applications
- DSP techniques for audio and speech processing applications
- Use of adaptive filtering for speech enhancement
- Laboratory assessment