Cadenza Challenge (CAD2): databases for lyric intelligibility task
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1.
University of Salford
Contributors
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1.
University of Nottingham
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2.
University of Salford
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3.
University of Leeds
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4.
University of Sheffield
Description
Cadenza
This is the training and validation data for the lyric intelligibility task from the Second Cadenza Machine Learning Challenge (CAD2).
The Cadenza Challenges are improving music production and processing for people with a hearing loss. According to The World Health Organization, 430 million people worldwide have a disabling hearing loss. Studies show that not being able to understand lyrics is an important problem to tackle for those with hearing loss. Consequently, this task is about improving the intelligibility of lyrics when listening to pop/rock over headphones. But this needs to be done without losing too much audio quality - you can't improve intelligibility just by turning off the rest of the band! We will be using one metric for intelligibility and another metric for audio quality, and giving you different targets to explore the balance between these metrics.
Please see the Cadenza website for a full description of the data
Technical info (English)
To run the challenge baseline, unpacked the data using
tar -xvzf <PACKAGE_NAME>
This dataset is used by the CAD2 Task 1 baseline.
Notes
Release Notes
Version V 1.1.1
This updates replace the file evaluation file cadenza_cad2_task1_eval.v1_0.tar.gz by cadenza_cad2_task1_eval.v1_1.tar.gz. The new evaluation file corrects the path to the evaluation audio signals.
Please not this is just a path modification and that the audio files and metadata files remain unchanged.
Version V 1.1.0
This update includes all files from V 1.0.0 plus the cadenza_cad2_task1_eval.v1_0.tar.gz package, which corresponds to the evaluation data to be processed by the challenge participants.
Files
Files
(16.5 GB)
| Name | Size | Download all |
|---|---|---|
|
md5:f2930a30b89a186801f9e5bf73372683
|
1.5 GB | Download |
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md5:efbb28f7eb0a84232b13e23e240ec275
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15.0 GB | Download |
Additional details
Funding
- UK Research and Innovation
- EnhanceMusic: Machine Learning Challenges to Revolutionise Music Listening for People with Hearing Loss EP/W019434/1
Dates
- Available
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2024-08-07
Software
- Repository URL
- https://github.com/claritychallenge/clarity
- Programming language
- Python
- Development Status
- Active