SEASR Audio
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Transcript of SEASR Audio
![Page 1: SEASR Audio](https://reader035.fdocuments.us/reader035/viewer/2022070302/5479d5275806b5a3048b475b/html5/thumbnails/1.jpg)
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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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.
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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
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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
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NEMA Flow – Blinkie
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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
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Do It Yourself (DIY) 1
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DIY Options
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DIY Job List
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DIY Job View
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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
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Nester: Cardinal Catalog View
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Examining Audio Collection
• Tagged a set of examples Male and Female
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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