1 / 30

Music from Volcanoes on the Network

Domenico Vicinanza CERN – Geneva – Switzerland University of Salerno - INFN Catania - Italy Conservatory of Music of Salerno - Italy Hugo Yepez Istituto Geofisico – EPN – Quito - Ecuador. Music from Volcanoes on the Network. Volcano eruption forecasting.

israel
Download Presentation

Music from Volcanoes on the Network

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Domenico Vicinanza CERN – Geneva – Switzerland University of Salerno - INFN Catania - Italy Conservatory of Music of Salerno - Italy Hugo Yepez Istituto Geofisico – EPN – Quito - Ecuador Music from Volcanoes on the Network

  2. TNC2007 - Copenhagen, May 21st 2007 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. TNC2007 - Copenhagen, May 21st 2007 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 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. Data Audification

  5. 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 Motivations

  6. TNC2007 - Copenhagen, May 21st 2007 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.

  7. TNC2007 - Copenhagen, May 21st 2007 Computing power • The computing power needed to run data sonification (and analyze patterns) and the mass storage to keep results are really large • To create one minute of music coming from volcanoes, the algorithm has to process: • more than 2.6 Millions data • taking more than 100 Mb of disk space • and 10-12 CPU hours • This is why GRID is a natural platform for this kind of studies • Moreover the possibility granted by the Grid to have a unique distributed shared database accessible from the researcher of all the word will pave the way for an effective interaction among researchers

  8. 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"; Java Sound Production Package

  9. Sonified data were geophysical data collected by digital seismographs placed on the Etna volcano in Catania (Italy) and on Tungurahua volcano in Ecuador. Data from Tungurahua are transferred from the Ecuadorian NREN, CEIDA, to GARR in Italy via RedCLARA’s 622 Mbps connection to GEANT2 We carried out two sonifications: seismogram straight sonification (tranformation into an audible waveform) seismogram melodisation (tranformation into a melody) Sound form volcanoes Etna Volcano Tungurahua  (Picture: M. Monzier IRD/IG-EPN)

  10. TNC2007 - Copenhagen, May 21st 2007 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 into 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)

  11. 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. Quasi-regular phenomena 20x resampled Etna seismogram

  12. Etna waveform and sonogram

  13. TNC2007 - Copenhagen, May 21st 2007 Tungurahua Waveform

  14. TNC2007 - Copenhagen, May 21st 2007 Tungurahua Spectrum Spectral lines = Regular patterns

  15. TNC2007 - Copenhagen, May 21st 2007 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

  16. TNC2007 - Copenhagen, May 21st 2007 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

  17. TNC2007 - Copenhagen, May 21st 2007 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/)

  18. TNC2007 - Copenhagen, May 21st 2007 MIDI Toolbox

  19. TNC2007 - Copenhagen, May 21st 2007 Dynamic evolution of the tonalities (related to couples of adjacent values)

  20. 0.00.5877852522920.9510565162950.9510565162950.5877852522920.0-0.587785252292-0.951056516295-0.951056516295-0.5877852522920.00.5877852522920.9510565162950.9510565162950.5877852522920.00.00.5877852522920.9510565162950.9510565162950.5877852522920.0-0.587785252292-0.951056516295-0.951056516295-0.5877852522920.00.5877852522920.9510565162950.9510565162950.5877852522920.0 Example: Sinusoidal behavior Graphical representation Original data

  21. Sinus melodization Data are periodic, so the melody is periodic, with the same period Music representation (of the same set of data)

  22. TNC2007 - Copenhagen, May 21st 2007 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.

  23. Seismograms Melodisation

  24. Seismograms Melodisation

  25. Seismograms Melodisation

  26. Seismograms Melodisation … have you ever heard a volcano playing a piano ?

  27. TNC2007 - Copenhagen, May 21st 2007 Melody follows the shape of the oscillation

  28. TNC2007 - Copenhagen, May 21st 2007 First Etna-Tungurahua duet Players: Mt Etna: Piano Mt Tungurahua: Guitar

  29. TNC2007 - Copenhagen, May 21st 2007 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/miditoolbox/MIDI_Toolbox_Manual.pdf

  30. TNC2007 - Copenhagen, May 21st 2007 Thanks! …Questions

More Related