Our code and method development offers the possibility to explore a variety of different data with sound.

Listening to our data

Datasets are becoming increasingly large and complex. This means that standard visualisation approaches are often insufficient to represent the full complexities of the data. Sonification offers an alternative to explore the data and make discoveries. In this example we can here our sonification approach to explore a three dimensional dataset. You can see an image of a galaxy, but you can hear the third spectral dimension (explanation also in YouTube description).

We currently have a in-develop tool for the interactive exploration of datacubes using sonification on github


We have been developing approaches to turn spectra into sound. Light spectra, can be directly, and intuitively be turned into a sound spectrum. The result is that the features in the light spectrum at different wavelengths correspond to the different frequencies that make up the corresponding sound. We introduced this approach in Trayford et al. 2023. This example is a galaxy spectra containing a supermassive black hole. The bright features in the spectra (emission lines) correspond to different transitions associated with different chemical species. Each features results in a specific tone that can be heard in the sound. You can try this out for yourself below in the browser-based notebooks.

Time series data

This mock data, shows how we can turn time-series day into sound. In this example the "brightness" values in the data are mapped to a cut-off frequency applied to a musical chord. The result of this is the sound appears to be "brighter" for higher (brighter) data values and duller for lower data values. More detail is presented in Tucker-Brown et al. 2022

This is just one of the many approaches to sonification that can be achieved with STRAUSS. You can try out different approaches using the browser-based notebook below.

Multi-variate data

With our code you can listen to multiple variables at the same time! This can be particularly useful if you want to understand how different variables relate to one another. In this example, from Trayford et al. 2023, we can listed to (1) the star formation rate (SFR) and (2) the ‘metal’ mass fraction (known as ’stellar metallicity’), of a simulated galaxy from the EAGLE simulations over 13 billion years of cosmic time. The metallicity is mapped to a low-pass filter cut off frequency (see right axes), and we provide two different example mappings for simultaneously mapping the star formation rate to volume low frequency oscillator frequency, as represented in the axes.  You can try this for yourself with the browser-based notebook below!

Try for yourself

The only requirement is that you need your own Google Account so that you can save a copy of the notebook into your own Google Drive. Once you have done this, everything will run for you in the browser (no need to run Python/STRAUSS on your own machine) and any edits will be saved to your Google Drive. 


1. When the project opens in Colab, immediately do File>Save a Copy in Drive. This will save a copy to your own Google Drive, so that you can make your own edits and save them as you explore the notebook.

2. Select Edit > Clear All Outputs.

3. Start reading the instructions inside the notebook.

4. To execute a code cell, click in the cell and then press the “Play” button

5. Feel free to edit the code and explore! For example, you can change the example data that is read in to experiment with sonifying the different examples.

Try the examples bundled with strauss:

More extended examples, produced for strauss workshops:

dotAstronomy 2024 workbook:


An extended example bringing together a number of case studies fomr other notebooks as well as new material. Example data is downloaded in-notebook.

Notebook example with star light curves: 


The data is also stored in the Google Drive in the form of csv files. There are three examples provided: 

GALEX_NUV_LC.csv: Flare stare

tic_lc.csv: Exclipsing binary star

kid11616200_lc.csv: Heartbeat star.

Notebook example with AGN galaxy spectra: 


The data is also stored in the Google Drive in the form of csv files.  These are grouped into three directories "Type1/" (four examples), "Type1.5/" (two examples) and "Type2/" (four examples). 

Section 2 of the notebook explains how to access the data with the different file names. 

Notebook example with multi-variate galaxy simulation data 


This example accesses the star-formation and metal-enrichment histories of 5 galaxies from the EAGLE simulations. It shows an example of how to sonify multiple properties at once (in a time-series like sonification).