The slides from my ( Simon’s) keynote are now online under Courses > One-off events. I’ll try to add a recording and perhaps a bibliography later.
November 15, 2014
Token passing
Token passing is a really nice way to understand (and even to implement) Viterbi search for Hidden Markov Models. Here we see token passing in action, and you can look at the spreadsheet to see the calculations. To keep things simple, we are ignoring transition probabilities in this example. It would be simple to add them […]
Continue reading... October 11, 2014
Classification and regression trees (CART)
A quick introduction to a very simple but widely-applicable model that can perform classification (predicting a discrete label) or regression (predicting a continuous value). The tree is learned from labelled data, using supervised learning. Before watching this video, you might want to check that you understand what Entropy is.
Continue reading... January 2, 2016
Interactive unit selection
Just a toy demo, but should give you some idea of how unit selection waveform generation works. Click with your mouse to choose a candidate diphone from each column, then the corresponding synthesised waveform will appear. You can click on the synthesised waveform to hear it again. Try to obtain the most natural-sounding synthesis by […]
Continue reading... November 23, 2014
The Gaussian probability density function: understanding the equation
The equation for the Gaussian probability density function looks a little scary at first, but this video should help you understand what each of the terms is doing, and how they fit together. After watching the video download the spreadsheet which shows the calculations and plots from this video (tip: the Apple Numbers.app version includes images […]
Continue reading... November 1, 2022
Bitrate
The bitrate (or bit rate) of a signal is the number of bits required to store, or transmit, 1 s of that signal. A bit is a binary number: either 0 or 1. Let’s calculate the bitrate of a digital waveform. First you should revise the concepts of sampling and quantisation from this module of the […]
Continue reading... October 11, 2014
Sampling and quantisation
Is digital better than analogue? Here we discover that there are limitations when storing waveforms digitally. We learn that the consequence of sampling at a fixed rate is an upper limit on the frequencies that can be represented, called the Nyquist frequency. In addition to the limitations of sampling, storing each sample of the waveform as a […]
Continue reading... October 11, 2014
Windowing
When we say that a signal is non-stationary we mean that its properties, such as the spectrum, change over time. To analyse signals like this, we need to first assume that these properties do not change over some short period of time, called the frame. We can then analyse individual frames of the signal, one at a […]
Continue reading... October 12, 2014
Entropy: understanding the equation
The equation for entropy is very often presented in textbooks without much explanation, other than to say it has the desired properties. Here, I attempt an informal derivation of the equation starting from uniform probability distributions. A good way to think about information is in terms of sending messages. In the video, we send messages […]
Continue reading... October 11, 2014
Pipeline architecture for TTS
Most text-to-speech systems split the problem into two main stages. The first stage is called the front end and contains many separate processes which gradually build up a linguistic specification from the input text. The second stage typically uses language-independent techniques (although they still require a language-specific speech corpus) to generate a waveform. Here we see those two […]
Continue reading... October 11, 2014
Aliasing
In sampling and quantisation we saw that sampling a signal at a fixed rate means that there is an upper limit on the frequencies that can be represented. This limit is called the Nyquist frequency. Before sampling a signal, we must remove all energy above the Nyquist frequency, and here we will see what would […]
Continue reading... October 18, 2014
TD-PSOLA …the hard way
Time-Domain Pitch Synchronous Overlap and Add (TD-PSOLA) can modify the fundamental frequency and duration of speech signals, without affecting the segment identity – that is, without changing the formants. Normally, it’s an automatic algorithm, but here we do it the hard way – by hand! If you want to follow-along, you will need Audacity and these materials (a […]
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This is the new version. Still under construction.