Volcano seismograms sonification application

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Volcano seismograms sonification application

• Domenico Vicinanza, CERN
• EELA Conference, Santiago, September 04-05, 2006

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Volcano eruption forecasting

• Currently no definitive method to predict the
  eruption of a volcano has been discovered or
  implemented (yet).
• Scientists monitor
• seismic waves
• number of earthquakes and the intensity of a
  specific type of quake (harmonic tremors) in the
  run up to eruptions.
• changes in the shape of the volcano or
  concentrations of gases emitted from the cone.

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The hope music for forecasting

• By correlating spectra and melodies with precise
  stages of volcanic activity we hope to discover a
  sort of signature tune of an imminent eruption
  or earthquake.
• By identifying musical patterns that warn of an
  eruption it would be possible to implement civil
  protection measures, days or even hours before
  the event

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Data Audification

• Data audification can be considered as the
  acoustic counterpart of data graphic
  visualization, a mathematical mapping of
  information from data sets to sounds.
• Data audification is currently used in several
  fields, for different purposes
• science and engineering,
• education and training,
• mainly as data analysis and interpretation tool.
• Although most data analysis techniques are
  exclusively visual in nature, data presentation
  and exploration systems could benefit greatly
  from the addition of sonification capabilities.

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Motivations

• Sonic representations are particularly useful
  when dealing with complex, high-dimensional
  data, or in data monitoring tasks where it is
  practically impossible to use the visual
  inspection, or for pattern detection.
• Research has shown that people are quite more
  confident in recognizing patterns audibly rather
  then visually
• Music theorists and researchers have carried out
  in centuries of history lots of techniques and
  methods to detect, study and classify musical
  phrases
• Main idea Music as a language and music
  analysis as a tool to inspect scientific data

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Advantages of hearing

• Hearing is non-directional
• It is surely easier to recognize a change in a
  sound with respect to a modification in something
  which has to be looked at.
• It is quite impossible to distinguish a blinking
  light flashing 100 times a second from another
  one flashing at 200, 1000, or 10000 times a
  second, while anyone can recognize periodic
  signals from 20 Hz to (almost) 20000 Hz

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Sonification on the GRID network

• First experiments involving sound production with
  INFN-GRID facilities started during the last
  months of 2003.
• In September 2003, it was installed CSound, a
  free and cross-platform acoustic compiler, on a
  GRID test site, the Catania INFN-GRID computer
  farm
• The compiler was tested within the new
  environment and since its beginning, the test
  phase produced interesting results efficient use
  of the calculus resources, customizable quality
  of the audio files.

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First Phase CSound

• After a four-five months test phase, CSound has
  been distributed to other GRID farms in Europe,
• Data sonification, sound synthesis and
  algorithmic composition have been investigated
  using couple of Csound files and Python (or
  sometimes Perl) scripts.
• Python scripts have been used to prepare the
  right data files during the tests.

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Second Phase Java

• Second test phase development of a sound
  production suite based on Java (equipped with the
  standard audio and math libraries), more flexible
  and easy to manage.
• All the results presented in this website have
  been carried on using this last approach sample
  computation, audio rendering, DFT computing were
  obtained with the Java sonification program on
  the GRID
• Executable "/bin/sh" StdOutput
  "sonification.out" StdError
  "sonification.err" InputSandbox
  "sonification.sh", "Sonification.java",
  "etna.dat" OutputSandbox "Sonification.aiff"
  , "Sound.dat","Spectrum.dat", "sonification.out","
  sonification.err", "logfile" RetryCount 7
  Arguments "sonification.sh"

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Sound form volcanoes

• Sonified data were geophysical data collected by
  digital seismographs placed on the Etna volcano
  in Catania (Italy) and on Tungurahua volcano in
  Ecuador.
• We carried out two sonifications
• seismogram straight sonification (tranformation
  into an audible waveform)
• seismogram melodisation (tranformation into a
  melody)

Etna Volcano
Tungurahua  (Picture M. Monzier IRD/IG-EPN)
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About seismograms audification

• In both the cases, structural properties of the
  seismographic information would be straightly
  mapped into sound or melody properties
• In the first case, regularities in the
  seismograms will be reflected by the existence of
  spectral lines in the sonified signal
• In the second case, regularities in the
  seismograms will be transferred into regularities
  in the melody (such as a repeated set of data
  will become a repeated musical phrase)

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Seismograms sonification

• Seismographic data have been recorded onto the
  surface of volcanoes, by digital seismographs at
  a sampling frequency of about 100 Hz (100.1603
  Hz).

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Original data

• Example of ASCII files processed
• (sample from Etna data)
• Starting time 15/06/2001 000339.920
  Frequency 100.1603 Hz Samples 168960 44
  43 42 44 44 46 43 45

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First SonificationFrom data to waveform

• Scaling procedure to properly arrange the samples
  in the -1,1 interval, according to their
  sampling frequency.
• Users can specify in the sonification program a
  certain resample frequency (pitch shift).
• Setting resample factors in the Java code greater
  than 1 won't preserve the original pitch,
  allowing a frequency shift...
• ....making audible regular phenomena happening at
  very low frequencies.
• In this way it is possible to observe and study
  periodical patterns, regular behaviors,
  long-range correlations, which can happen at
  different time scales.

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Sample and hold and linear interpolation

• It have been implemented two interpolation
  methods to generate the waveform
• linear interpolation and sample-and-hold (SH)
  interpolation.
• Linear interpolation returns particularly
  "smoother" sounds.
• Nevertheless, for some analysis application, it
  could be useful to prefer the SH audio file,
  where no external values are added to the
  original (scaled) data set.

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Creating the waveform Linear interp.
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Creating the waveform Sample Hold
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Quasi-regular phenomena

• The waveform coded in the audio file will have
  exactly the same regularities, also recognizable
  thanks to the presence of some higher lines in
  the spectrum.
• The order of magnitude of the frequency of
  quasi-regular phenomena is in the range 0-50 Hz,
  with a spectral envelope centered around 25-30
  Hz.

20x resampled Etna seismogram
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Etna waveform and sonogram
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Tungurahua Waveform
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Tungurahua Spectrum
Spectral lines Regular patterns
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Tungurahua Sonogram
Time evolution of the spectrum. Each vertical
slice is the spectrum at a certain time
Oscillation pattern variations are clearly
visible in the pattern
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Etna sonification within GILDA
https//gilda.ct.infn.it/
https//glite-demo.ct.infn.it/
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http//glite-demo.ct.infn.it
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Listening to sonification.aiff file
Etna sonogram
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Data melodization

• The whole data set interval is mapped on the
  (equally tempered) piano keyboard
• The min value of the seismographic data will
  correspond to the lowest playable note on the
  piano keyboard
• The max value to the highest playable note

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Main advantages

• Already available tools to manage MIDI files and
  analyze them
• Tracking the evolution of the musical intervals,
  the dynamics of their patterns, it is possible to
  detect, with an high level and in a customizable
  way, any kind of modifications in the shape of
  seismogram.
• Two example of MIDI analysis (free) software
• Rubato (www.rubato.org)
• MIDI Toolbox (http//www.jyu.fi/musica/miditoolbox
  /)

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MIDI Toolbox
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Dynamic evolution of the tonalities (related to
couples of adjacent values)
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Example Sinusoidal behavior

• 0.00.5877852522920.9510565162950.9510565162950
  .5877852522920.0-0.587785252292-0.951056516295
  -0.951056516295-0.5877852522920.00.587785252292
  0.9510565162950.9510565162950.5877852522920.0

Graphical representation
Original data
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Sinus melodization
Data are periodic, so the melody is periodic,
with the same period
Music representation (of the same set of data)
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Melodization a pictorial view

• Pictorially we can say that the melodization
  works by overlaying seismograms with music notes
• To create the volcanic score, we take a
  seismogram and trace the shape on to blank music
  bars.
• Then we overlay the contours with musical notes.

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Seismograms Melodisation
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Seismograms Melodisation
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Seismograms Melodisation
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Seismograms Melodisation
have you ever heard a volcano playing a piano ?
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Melody follows the shape of the oscillation
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First Etna-Tungurahua duet

• Players
• Mt Etna Piano
• Mt Tungurahua Guitar

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Reference sites

• Etna Sonification website http//grid.ct.infn.it/
  etnasound
• Tungurahua Sonification web repository
  http//grid.ct.infn.it/tungurahuasound
• MIDI Toolbox manual http//www.jyu.fi/musica/midi
  toolbox/MIDI_Toolbox_Manual.pdf

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Thanks! Questions