Written by Bastiaan van der Weij and Dieuwke Hupkes in 2016.
Updated by Peter Dekker (2017), Bas Cornelissen (2017, 2018, 2019) Marianne de Heer Kloots (2018) and Francijn Keur (2025, 2026).
If you type your answers in Colab: don’t forget to make a copy of the notebook in your own Drive to save your changes!
The lab contains Do questions, where you need to work with the Praat software, and Think questions, which check your understanding of what is happening.
You can open this notebook in Google Colab, by clicking on the “Open in Colab” button, and write down your answers in the designated boxes (but make sure to copy the file to your own drive first!), or you could write down your answers using your preferred text editor.
Sounds are the raw materials in the study of language and music. In this lab we’ll learn how to use Praat for analysing and editing sounds. We’ll explore sound signals and look at how they relate to the things we perceive, such as words, melodies or rhythms.
Getting started¶
Praat is a free and open-source computer program widely used in phonetics (the study of human speech) and bioacoustics. It is a swiss-army knife containing many tools for visualising, analysing and synthesizing sounds.
First, download the materials you will need for this lab assignment, extract the contents of the zip-file and make sure you know where they are located:
bioacoustics.zip
Click here to download the zip-file.
Go to www.praat.org, find the download page for your favourite operating system and follow the installation instructions until you have started the Praat program (usually this involves double-clicking a beautiful pink icon).
You will see two windows: Praat objects and Praat picture. Praat objects is where the sounds you are editing or analyzing will appear. Praat picture is where you can visualize the output of various analyses.
The anatomy of a sound¶
From the Praat objects window, navigate to Open > Read from file,
or type Ctrl-O. In the materials folder we provided with this lab,
you’ll find a file called sine.wav. Open and load it into Praat. Now
that we have a Praat object, let’s have a look at what we can learn.
First, let’s play the sound.
Soundwaves and spectrums¶
The waveform is the most straightforward visual representation of a sound. The waveform is a plot of how the air pressure, recorded by the microphone, changes over time.
You’ll notice that this sound wave consists of a constantly repeating pattern. Each repetition of this pattern constitutes one vibration. The number of vibrations per second is called the frequency of the sound. Let’s try to find out the frequency of the sound we’ve opened. To do this, we’ll use a different representation of the sound, called a Spectrum. The Spectrum can be stored in a new Praat object (apart from sounds, Praat objects can also represent other information, such as the results of various sound-analyses).
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As you have heard, and seen, this sound is not particularly exciting. Let’s look at a more interesting sound.
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You should notice that the individual vibrations form a self-repeating pattern.
The frequency you just found---the frequency of the shortest non-repeating pattern---is called the fundamental frequency. The fundamental frequency usually (but not always) corresponds to perceived pitch. As we will see now, sounds often contain many more frequencies, which can be discovered by looking at the spectrum.
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The peaks you found in the spectrum are called harmonics. The same note on various instruments may have the same pitch, but the energy distribution over the harmonics varies, resulting in different timbres. The same principle allows us to distinguish between different vowels.
The waveform and spectrogram¶
Now we’ll look at human vocalizations.
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As you can hear and see, these sounds are more complex than the sounds we’ve dealt with so far. The previous two sounds didn’t change in pitch and maintained a (relatively) constant timbre throughout their duration. In the new sounds, the pattern of vibrations in is continuously changing. Counting vibrations or looking at the spectrum will not be able to tell us much. With these sort of sounds, a spectrogram is a much more informative visualisation.
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Plotting spectrograms¶
Now we’re going to explore some Praat functionality to draw two spectrograms above eachother in a picture. We’ve seen how to view and edit Praat objects. Praat has different viewers for different objects. In these viewers, you can interact with the objects and zoom in to regions of interest. However, when you’re, for example, writing a paper, you want to draw nice pictures containing these visualisations. For this reason, most Praat objects can be drawn into the Praat picture window. That picture, in turn, can be exported to various image formats.
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Plotting pitch contours¶
A common analysis used for sounds is the analysis, or fundamental frequency analysis. As we’ve learned, the fundamental frequency generally corresponds to perceived pitch. We can use Praat to draw a Pitch contour (this is actually the same type of analysis as the blue line we saw before on top of the spectrogram).
Now we’ll run the analysis and draw the results.
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Speech¶
Although we’re all very good at producing and interpreting speech sounds, recognizing sounds in waveforms or spectrograms is much harder. In the lecture and tutorial you have learned how different vowels are distinguished by their first two formants ( and ), and different consonants are distinguished on the three dimensions of manner, place and voicing.
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Phonemes¶
Phonemes are the basic components of speech. The word "slit", for example, consists of a fricative /s/, a lateral /l/, a vowel /i/, and a plosive (or stop) /t/. Fricatives are generated by making air ‘whirl’ through a constriction created by two articulators (e.g. your two lips, or your tongue and palate). Laterals are generated by letting air flow around the sides of the tongue. Plosives are generated by completely stopping the airflow for a very small fraction of time, resulting in complete silence.
Previously, we looked at harmonic frequencies in the bassoon sound. Amplified harmonics in speech sounds show up as peaks in the spectrum, or dark spots in the spectrogram. These peaks are called formants. Vowels can be differentiated by looking at how their formants are distributed.
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The sound of silence¶
Very small changes to the signal can sometimes have dramatic effects on perception. For example, inserting a small period of silence (silent interval) at specific places in words can create the effect of hearing an extra phoneme. In this final part of the lab we’ll explore the effect of inserting a small silence in our recording of "slit" at just the right place.
First, we’ll create a small silence to be inserted into the sound. To find out an appropriate duration for this silence, we’ll look at a paper that investigated the effect of a silent interval in the word "slit". Have a look at the methods section, as well as the graph with results, in the paper by Marcus (1978). Use the graph summarizing their results to find a good duration for the silent interval.
Now we’re going to insert the silence into our recording of the word "slit".
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[1] During the lecture, you heard cries from a French and a German baby. These were used in a study done by Mampe et al., 2009. The recordings that you are analyzing in this lab were recorded for a recent follow-up study done by Wermke et al., 2017 comparing German and Chinese babies. Have a look at the studies and the accompanying sounds if you’re interested! Both are included in this lab’s materials.
- Marcus, S. M. (1978). Distinguishing “slit” and “split”—an invariant timing cue in speech perception. Perception & Psychophysics, 23(1), 58–60. 10.3758/bf03214295
- Mampe, B., Friederici, A. D., Christophe, A., & Wermke, K. (2009). Newborns’ Cry Melody Is Shaped by Their Native Language. Current Biology, 19(23), 1994–1997. 10.1016/j.cub.2009.09.064
- Wermke, K., Ruan, Y., Feng, Y., Dobnig, D., Stephan, S., Wermke, P., Ma, L., Chang, H., Liu, Y., Hesse, V., & Shu, H. (2017). Fundamental Frequency Variation in Crying of Mandarin and German Neonates. Journal of Voice, 31(2), 255.e25-255.e30. 10.1016/j.jvoice.2016.06.009