We employ algorithms to form translation matrices between raw time series data and audio. This allows for the identification of patterns within data through the medium of sound.
Some examples of sonification representing what a merge between two neutron stars might sound like. By slicing and manipulating data in interesting ways, it is possible to “hear” events which are otherwise unhearable. Rather than merely mapping metrical values, our interest in sonification lies more within the translation between raw data and audio waveforms. This allows for a far more thorough experience of the data streams in question.