Semester.ly

Johns Hopkins University | EN.520.652

Filtering and Smoothing

3.0

credits

Average Course Rating

(4.27)

This course is intended to give students an opportunity to do directed research in algorithm development that culminates in a MATLAB program. Students will learn about extracting signals from noise using statistical and non-statistical models. Topics include Kalman filtering, smoothing, interpolation (upsampling), spline fitting, and the numerical linear algebra issues that impact these problems. Emphasis is on fast, compact, stable algorithms. The grade is based on the term project and occasional homework. There are no examinations. Class attendance is mandatory.

Spring 2013

(4.14)

Spring 2014

(4.0)

Spring 2015

(4.67)

Spring 2013

Professor: Howard Weinert

(4.14)

The best aspects of the course included the useful content and material learned, as well as the assignments and projects which helped students understand many of the topics. The worst aspects of the course included the outdated research materials and hefty workload. The students felt that the course was also too focused on speech. The course would improve if there was a better textbook and more updated research materials. Prospective students should know that the course is chal enging and that a good knowledge of probability and statistics is helpful.

Spring 2014

Professor: Howard Weinert

(4.0)

The best aspect of this course seemed to be learning the methods of speech recognition. They also had lots of hands-on experience. But all topics are not covered in depth and it was not well organized, according to some students. It was suggested that more emphasis be put on state-of- the-art work in speech processing and that there be more assignments. Prospective students should have a background in speech processing and be interested in it.

Spring 2015

Professor: Howard Weinert

(4.67)

The best aspects included the relatable and effective professor, the 1-on-1 feedback sessions, and the obvious student progress made throughout the semester. Many students agreed that the method of working on different iterations of the same speech was an effective approach, and that the professor’s guidance was very helpful. Some students felt the semester course was too short and would have benefited from a full semester of work. Suggestions for improvement included having opportunities for individual presentations. Prospective students should be prepared for a significant amount of speech preparation every week and the opportunity to improve public speaking and presentation skills.