Transcript IST-2006-026409 - EELA Documents

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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
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• 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
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• 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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Sonification on the GRID network
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• 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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Second Phase: Java
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• 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
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• 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
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(Picture: M. Monzier IRD/IG-EPN)
About seismograms audification
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• 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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Original data
• Example of ASCII files processed
(sample from Etna data):
Starting time: 15/06/2001 00:03:39.920
Frequency: 100.1603 Hz
Samples: 168960
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First Sonification:
From 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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Quasi-regular phenomena
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• 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 quasiregular phenomena is in the range 0-50 Hz, with a
spectral envelope centered around 25-30 Hz.
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20x resampled Etna seismogram
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Etna waveform and sonogram
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Tungurahua Waveform
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Tungurahua Spectrum
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Spectral lines = Regular patterns
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Tungurahua Sonogram
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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
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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
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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
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0.0
0.587785252292
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0.0
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-0.587785252292
0.0
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Original data
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Graphical representation
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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
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• 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
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… 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
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Players:
• Mt Etna: Piano
• Mt Tungurahua: Guitar
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Reference sites:
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• 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/miditoolbox/MIDI_Toolbox_Ma
nual.pdf
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Thanks! …Questions
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