Sivuaine suomeksi: Signaalin-, puheen- ja kielenkäsittely
Biämne på svenska: Signal-, tal- och språkbehandling
Extent: 20-25 credits
Teacher in charge: Mikko Kurimo
Target group: All students with sufficient prerequisite knowledge.
Application procedure: No separate application procedure.
Prerequisites: There are no compulsory prerequisite courses. However, in order to succeed in the minor the student needs good skills in mathematics, especially in linear algebra, calculus, probability theory and statistics, programming skills, basic understanding of linear systems and basic knowledge and skills in analog and digital signal processing. In Aalto University helpful courses include for example MS-A0003 Matrix algebra (ELEC1) and similar courses, MS-A0501 First course in probability and statistics and similar courses, ELEC-A7100 Basic Course in C programming, ELEC-A7200 Signals and Systems, ELEC-C5230 Digital Signal Processing Basics, ELEC-C5340 Applied Digital Signal Processing and ELEC-C5210 Stochastic Processes in Communications.
Content and structure of the minor
The purpose of the minor is to provide the students with a background in either Modern Signal Processing or Speech and Language Processing. Courses in Signal Processing provide a toolbox of knowledge on signals and systems modeling, representation through transforms, and systems optimization and implementation. Some emphasis will be given on the most recent research priorities in the field of signal processing, such as the domains of data analysis, compression and storage, communications and representation of signals. Courses in Speech Processing provide the students with the basics of speech and language processing, as well as the ability to apply those in various fields of science and technology. Speech and Language Processing utilizes signal processing, mathematical modeling and machine learning for statistical language modeling, information retrieval, and speech analysis, synthesis, recognition and coding.
The minor consist of two common compulsory courses (10 cr) and two or three elective courses (10-15 cr).
|Machine Learning: Supervised Methods||5||I-II|
Signal Processing for Communications
Optional courses: Choose 10 credits.
Methods of Data Mining
|ELEC-E5422||Convex Optimization I P||5||I|
|ELEC-E5423||Convex Optimization II P||5||II|
|ELEC-E5431||Large Scale Data Analysis P||5||III-IV|
Speech and Language Processing Methods P
Special Course in Machine Learning, Data Science and Artificial Intelligence
Speech Recognition P
Statistical Natural Language Processing P
|Statistical Signal Processing P||5||I-II|