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27.06.2022 08:31:15
Lecture: 5.04.4643 Adaptive systems for speech signal processing - Details
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General information

Course name Lecture: 5.04.4643 Adaptive systems for speech signal processing
Subtitle
Course number 5.04.4643
Semester Sommersemester 2022
Current number of participants 11
Home institute Institute of Physics
Courses type Lecture in category Teaching
Next date Mon., 27.06.2022 12:15 - 13:45, Room: W04 1-172
Type/Form VL,Ü
Participants 1. Fundamentals of speech signals and systems
- Linear algebra review
- Fundamental tasks of speech adaptive filters: identification, filtering, prediction, equalization
- MMSE filter (Wiener solution)
- Method of least squares

2. Recursive Algorithms for Speech Adaptive Filtering
- Control loop approach
- Normalized least-mean squares (NLMS)
- Recursive least-squares (RLS)
- Frequency-domain adaptive filter (FDAF)
- Kalman filter (state estimation)

3. Time-variant systems in speech applications
- State-space modeling
- Acoustic state space
- Application 1: hands-free communication systems
- Application 2: HRTF-measurement for virtual reality

4. Blind system identification
- Minimum-eigenvalue approach
- Maximum-eigenvalue approach (PCA)
- System identifiability conditions
- Measures for system distance
- Application 1: binaural speech enhancement
- Applications 2: acoustic sensor networks for speech

5. Nonlinear systems
- Definitions and measures of nonlinearity
- Applications of robust linearizations
- Nonlinear modeling and identification
- Application: Nonlinear loudspeaker in hands-free speech communication systems
Pre-requisites Signals and Systems, Signal Processing
Lehrsprache englisch

Course location / Course dates

W04 1-172 Mon.. 12:15 - 13:45 (12x)
Monday. 04.07.22, Monday. 18.07.22 14:15 - 15:45
W01 0-008 (Rechnerraum) Mon.. 14:15 - 15:45 (12x)
W03 1-154 Monday. 30.05.22 14:15 - 15:45

Comment/Description

The students gain a broad operational perspective for the design of speech adaptive systems and respective algorithms with a particular focus on adaptive digital filters. The important NLMS, RLS, FDAF and Kalman-Filter algorithms can be derived from fundamental principles. Diverse applications from speech and acoustic signal processing deliver practical insight into the utilization of the fundamentals, for instance, in acoustic noise reduction, echo cancellation, dereverberation, acoustic channel estimation and equalization. However, the acquired knowledge allows for a broader interpretation in the context of engineering and physics. The computer exercises of larger scale will teach the students to argue, select and evaluate algorithms for the problem at hand. By discussion in the panel, students learn to demonstrate, defend and trade their solution against others. Theoretical exercises finally deliver the ability to argue and prove a speech processing design with the appropriate vocabulary.
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