Dataset Open Access

MAESTRO Synthetic - Multi-Annotator Estimated Strong Labels

Irene Martin Morato; Manu Harju; Annamaria Mesaros

The dataset was created for studying estimation of strong labels using crowdsourcing.

It contains 20 synthetic audio files created using Scaper, the reference annotation created with Scaper, and the annotation outcome. Annotation was performed using Amazon Mechanical Turk.

Audio files contain excerpts of recordings uploaded to freesound.org.(from Urban Sound 8k dataset). Please see FREESOUNDCREDITS.txt for an attribution list. 

The dataset contains: 

  • audio: the 20 synthetic soundscapes, each 3 min long
  • ground truth:  the "true" reference annotation created using Scaper
  • estimated strong labels: the reference annotation created from the crowdsourced data
  • audio tags: the weak labels corresponding to each 10 s segment of the soundscapes, as annotated

For details on the annotation procedure and label processing methodology, see the following paper:

Irene Martin Morato, Manu Harju, and Annamaria Mesaros. Crowdsourcing strong labels for sound event detection. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2021). New Paltz, NY, Oct 2021.

 

 

Files (590.8 MB)
Name Size
audio.zip
md5:0853d279e8eacc537decf60d54b338bd
590.4 MB Download
files_mapping.csv
md5:45895659263e79b8276f48f26c47bf1c
130.3 kB Download
FREESOUNDCREDITS.txt
md5:8824688e9626da1f7c6ebd46e7c2807e
26.2 kB Download
LICENSE.txt
md5:9b6ef28c503a5f5d0462d24a2cff740f
1.5 kB Download
meta.zip
md5:9e13b87fe7de06f30737dff9e511acea
232.9 kB Download
README.md
md5:deedb99e1ccaa2d18005cb93891621fc
6.6 kB Download
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