seasr audio

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Pathways to SEASR Audio Analysis NEMA NESTER National Center for Supercomputing Applications University of Illinois at Urbana-Champaign The SEASR project and its Meandre infrastructure are sponsored by The Andrew W. Mellon Foundation

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Pathway to SEASR Workshop in March 2009 in North Carolina

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Page 1: SEASR Audio

Pathways to SEASR

Audio Analysis

NEMA

NESTER

National Center for Supercomputing Applications"University of Illinois at Urbana-Champaign

The SEASR project and its Meandre infrastructure!are sponsored by The Andrew W. Mellon Foundation

Page 2: SEASR Audio

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.

Page 3: SEASR Audio

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

Page 4: SEASR Audio

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

Page 5: SEASR Audio

NEMA Flow – Blinkie

Page 6: SEASR Audio

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

Page 7: SEASR Audio

Do It Yourself (DIY) 1

Page 8: SEASR Audio

DIY Options

Page 9: SEASR Audio

DIY Job List

Page 10: SEASR Audio

DIY Job View

Page 11: SEASR Audio

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

Page 12: SEASR Audio

Nester: Cardinal Catalog View

Page 13: SEASR Audio

Examining Audio Collection

•  Tagged a set of examples Male and Female

Page 14: SEASR Audio

Pathways to SEASR"Audio

National Center for Supercomputing Applications"University of Illinois at Urbana-Champaign

The SEASR project and its Meandre infrastructure"are sponsored by The Andrew W. Mellon Foundation