Dataset Open Access

MAESTRO Synthetic - Multi-Annotator Estimated Strong Labels

Irene Martin Morato; Manu Harju; Annamaria Mesaros


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    <subfield code="a">&lt;p&gt;The dataset was created for studying estimation of strong labels using crowdsourcing.&lt;/p&gt;

&lt;p&gt;It&amp;nbsp;contains 20 synthetic audio files created using Scaper, the reference annotation created with Scaper, and the annotation outcome.&amp;nbsp;Annotation was performed using Amazon Mechanical Turk.&lt;/p&gt;

&lt;p&gt;Audio files contain excerpts of recordings uploaded to freesound.org.(from Urban Sound 8k dataset).&amp;nbsp;Please see FREESOUNDCREDITS.txt for an attribution list.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;The dataset contains:&amp;nbsp;&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;audio: the 20 synthetic soundscapes, each 3 min long&lt;/li&gt;
	&lt;li&gt;ground truth:&amp;nbsp; the &amp;quot;true&amp;quot; reference annotation created using Scaper&lt;/li&gt;
	&lt;li&gt;estimated strong labels: the reference annotation created from the crowdsourced data&lt;/li&gt;
	&lt;li&gt;audio tags: the weak labels corresponding to each 10 s segment of the soundscapes, as annotated&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;For details on the annotation procedure and label processing methodology, see the following paper:&lt;/p&gt;

&lt;p&gt;Irene Martin&amp;nbsp;Morato, Manu Harju, and Annamaria Mesaros.&amp;nbsp;&lt;em&gt;&lt;a href="https://arxiv.org/abs/2107.12089"&gt;Crowdsourcing strong labels for sound event detection&lt;/a&gt;.&lt;/em&gt;&amp;nbsp;In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2021). New Paltz, NY, Oct 2021.&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

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