SEASR Audio

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Pathway to SEASR Workshop in March 2009 in North Carolina

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SEASR Audio

  1. 1. Pathways to SEASR Audio Analysis NEMA NESTER National Center for Supercomputing Applicationsquot; University of Illinois at Urbana-Champaign The SEASR project and its Meandre infrastructure! are sponsored by The Andrew W. Mellon Foundation
  2. 2. Defining Music Information Retrieval? •  Music Information Retrieval (MIR) is the process of searching for, and finding, music objects, or parts of music objects, via a query framed musically and/or in musical terms •  Music Objects: Scores, Parts, Recordings (WAV, MP3, etc.), etc. •  Musically framed query: Singing, Humming, Keyboard, Notation-based, MIDI file, Sound file, etc. •  Musical terms: Genre, Style, Tempo, etc.
  3. 3. NEMA Networked Environment for Music Analysis –  UIUC, McGill (CA), Goldsmiths (UK), Queen Mary (UK), Southampton (UK), Waikato (NZ) –  Multiple geographically distributed locations with access to different audio collections –  Distributed computation to extract a set of features and/or build and apply models
  4. 4. SEASR: @ Work – NEMA Executes a SEASR flow for each run –  Loads audio data –  Extracts features from every 10 second moving window of audio –  Loads models –  Applies the models –  Sends results back to the WebUI
  5. 5. NEMA Flow – Blinkie
  6. 6. NEMA Vision •  researchers at Lab A to easily build a virtual collection from Library B and Lab C, •  acquire the necessary ground-truth from Lab D, •  incorporate a feature extractor from Lab E, combine with the extracted features with those provided by Lab F, •  build a set of models based on pair of classifiers from Labs G and H •  validate the results against another virtual collection taken from Lab I and Library J. •  Once completed, the results and newly created features sets would be, in turn, made available for others to build upon
  7. 7. Do It Yourself (DIY) 1
  8. 8. DIY Options
  9. 9. DIY Job List
  10. 10. DIY Job View
  11. 11. Nester: Cardinal Annotation •  Audio tagging environment •  Green boxes indicate a tag by a researcher •  Given tags, automated approaches to learn the pattern are applied to find untagged patterns
  12. 12. Nester: Cardinal Catalog View
  13. 13. Examining Audio Collection •  Tagged a set of examples Male and Female
  14. 14. Pathways to SEASRquot; Audio National Center for Supercomputing Applicationsquot; University of Illinois at Urbana-Champaign The SEASR project and its Meandre infrastructurequot; are sponsored by The Andrew W. Mellon Foundation

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