How can I make my visualisation with sound? As known sound has lots of raw data. such as…
And add amplitude for visualisation a sound
There are two main properties of a regular vibration – the amplitude and the frequency – which affect the way it sounds.
Amplitude is the size of the vibration, and this determines how loud the sound is. We have already seen that larger vibrations make a louder sound. Frequency is the speed of the vibration, and this determines the pitch of the sound. It is only useful or meaningful for musical sounds, where there is a strongly regular waveform. Frequency is measured as the number of wave cycles that occur in one second.
Frequency data from the song “Tained Love”- Frequency spectrum. And here is raw data coming from mp3 file:
and more experiments with style of visualisation
Playing with sound visualisation and frequency, we can create a sound, by setting parameters such as amplitude and frequency. So if amplitude and frequency come from some data set, we can create unique sound of data. This thoughts remind me a project by Leanne Wijnsma and Froukje Tan “Smell of Data” created to instinctively alert internet users of data leaks on personal devices
During my research I found an interesting project of Simon Roger call “TwoTone” a tool to represent a data as sound.
Another example
The Mueller Report Redactions Remix: PRI
Ryan Cavis, a technical lead at PRX, and the team at PRI’s The World mapped every word and page of Mueller report and turned it into a dataset. The volume represent the amount text on page
NASA turn the measurements of space into sound.
data from the Wind satellite, which measures the sun’s electromagnetic fluctuations, and turned it into audible sound through a computer algorithm.
If to talk about frequency not from a music view, but talk about Frequency analysis . Frequency analysis is based on the fact that, in any given stretch of written language, certain letters and combinations of letters occur with varying frequencies. Moreover, there is a characteristic distribution of letters that is roughly the same for almost all samples of that language. For instance, given a section of English language, E, T, A and O are the most common, while Z, Q, X and J are rare. Likewise, TH, ER, ON, and AN are the most common pairs of letters (termed bigrams or digraphs), and SS, EE, TT, and FF are the most common repeats.[1] The nonsense phrase “ETAOIN SHRDLU” represents the 12 most frequent letters in typical English language text. (Wikipedia)
Series of video with music visualization by using amplitudes data from songs
And final visualization of my favorite tracks collected from my playlist