Starting up

While the program does not have any pre-requisites, some mathematical, signal analysis, and linguistic knowledge will definitely come in handy and help you hit the ground running. The point is not to ace all this content, but to determine if you enjoy thinking in this technical, even mathematical way, as you perform assignments in the program.  A good place to start is with SCOOT "Speech Communication Online Training" speech tech content provided for free by ISCA. 

If you follow any of these online courses or materials and hit a snag, just drop a line to cf-vt@rug.nl with a description of the challenge you're facing and someone will connect you to an instructor who will help you! 

• Python for everybody will give you an idea of the basics. If you're a beginner, don't worry if you can't solve everything, The main point is to make sure you enjoy trying and learning.

• This machine learning content comes from a deep learning course at the University of Geneva. It is very extensive, with 1000+ slides, ~20h of screen-casts, and is full of examples in PyTorch. Do not feel as if you need to systematically do everything in this course --  this is exceptionally comprehensive and detailed. It is enough to check it out and make sure this content interests you.

While there is no mathematical requirement for this content, having a firm background in some of the basics can be an asset for the Machine Learning course and some of the upper-level voice tech courses. It is possible to pass all course content without understanding much math, but have a working knowledge of some mathematical content will help you get the most out of course content! 

Refresher in linear Algebra 

Matrix methods in data analysis, signal processing and machine learning (MIT). 

Of particular relevance for many upper-level VT courses:

• Lectures 1, 2, 3, 5 and 9 --> Vectors, matrices and basic operations

• Lecture 7, 8 --> Solving equations

Also of interest:

• Lecture 25 --> Used in machine learning algorithms (e.g. PCA)

• Lecture 33 --> Used in packages to solve equations.

Mathematics for machine learning and deep learning

This course, given by Prof. Strang, is nicely connected to machine learning and deep learning. Much of this content is covered in the machine learning course given in the MSc Voice Tech in term 1b (but with less mathematical rigor, so don't be put off by the mathematical difficulty). However, this content may help mathematically-inclined students learn how e.g. back propagation or gradient descent works. That said, this course may be more suitable for students who are more visual learners and prefer less-traditional modes of content delivery.

Calculus (don't panic!)

For those who have not studied calculus prior to joining this program, don't fret. We recognize that it is hard to learn calculus remotely. You can pass all course content without knowing calculus. For those who are interested, this is a nice resource. 

For Signals and Systems basics from the book Signals and Systems by Oppenheim, Willsky and Nawab (2nd Edition), Chapters 4,5 and 7 on Fourier Transforms (continuous and discrete) and sampling are recommended. If time permits, Chapters 9, 10 and 11 provide an added advantage to students. Here is a free download link (needs to be verified if it is authorized) -

You may also follow the NPTEL course on Digital Signal Processing by Prof. S.C. Dutta Roy. Video lectures 8-13 cover some fundamentals of Fourier and Z transforms.

• The Coursera course "Miracles of Human Language: An Introduction to Linguistics" is a crash-course in linguistics. In the MSc. Voice Technology program, the only relevant content pertains to material around phonetics and phonology (specifically week 2 of the MOOC). No other linguistic knowledge is required. 

• This YouTube series is also dedicated to introductory level linguistics. As above, the critical content pertains to phonetics and phonology (lessons 8 - 11, lessons 12 and 15 could also be useful if you're so inclined).

The MOOCs below are not related to course content in any way. We share them in case some students are interested in brushing up on language skills.  To be clear, all classes and assignments are in English.

• Frisian: Learn to speak, write and understand the second official language of the Netherlands with this free introductory language course.\

• Learn to speak, write and understand basic Dutch, with this free, three-week, introductory foreign language course.

Summer projects have been moved to their own page for ease of navigation/sharing. See here.

In principle, any laptop will do -- but if you have the financial resources and the inclination to make sure you have best machine possible, check out this link.

The MSc VT program materials are all open-source and/or freely available. In the event that an instructor assigns a reading from a textbook, that book can always be found in the Voice Tech team office on the 1st floor of the faculty building.

Here are some popular popular books you may want to check out. None of these are assigned in any course, but you may still wish to read them out of general interest. 

• How smart machines think (Sean Garrish)

• The age of surveillance capitalism (Shoshana Zuboff)

• Try to make a study schedule. You can do this with an excel sheet that covers all days in the first semester, with deadlines, exams, and guest lectures. A good study schedule helps you estimate how much time you'll need to complete assignments. You can also consider adapting templates like this one. Others make use of specialized software like Notion, which also has many student-friendly templates for planning and note-taking. If you sign up with your university email address, you'll get a student upgrade. 

• Check out the pink trombone.