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Volcano seismograms sonification application

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20x resampled Etna seismogram. IST-2006-026409 ... Mt Etna: Piano. Mt Tungurahua: Guitar ... Etna Sonification website: http://grid.ct.infn.it/etnasound ... – PowerPoint PPT presentation

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Title: Volcano seismograms sonification application


1
Volcano seismograms sonification application
  • Domenico Vicinanza, CERN
  • EELA Conference, Santiago, September 04-05, 2006

2
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.

3
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

4
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.

5
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

6
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

7
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.

8
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.

9
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"

10
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)
11
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)

12
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).

13
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

14
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.

15
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.

16
Creating the waveform Linear interp.
17
Creating the waveform Sample Hold
18
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
19
Etna waveform and sonogram
20
Tungurahua Waveform
21
Tungurahua Spectrum
Spectral lines Regular patterns
22
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
23
Etna sonification within GILDA
https//gilda.ct.infn.it/
https//glite-demo.ct.infn.it/
24
http//glite-demo.ct.infn.it
25
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29
Listening to sonification.aiff file
Etna sonogram
30
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

31
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
    /)

32
MIDI Toolbox
33
Dynamic evolution of the tonalities (related to
couples of adjacent values)
34
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
35
Sinus melodization
Data are periodic, so the melody is periodic,
with the same period
Music representation (of the same set of data)
36
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.

37
Seismograms Melodisation
38
Seismograms Melodisation
39
Seismograms Melodisation
40
Seismograms Melodisation
have you ever heard a volcano playing a piano ?
41
Melody follows the shape of the oscillation
42
First Etna-Tungurahua duet
  • Players
  • Mt Etna Piano
  • Mt Tungurahua Guitar

43
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

44
Thanks! Questions
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