Difference between revisions of "2007:Query by Singing/Humming"
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== Query Data == | == Query Data == | ||
| − | + | 1. Roger Jang's corpus ([http://neural.cs.nthu.edu.tw/jang2/dataSet/childSong4public/QBSH-corpus/ MIREX2006 QBSH corpus]) which is comprised of 2797 queries along with 48 ground-truth MIDI files. All queries are from the beginning of references. | |
| − | + | 2. ThinkIT corpus comprised of 355 queries and 106 monophonic ground-truth midi files (with MIDI 0 or 1 format). There are no "singing from beginning" gurantee. This corpus will be published after the task running. | |
| − | To build a large test set which can reflect real-world queries, it is suggested that every participant makes a contribution to the evaluation corpus | + | 3. Noise MIDI will be the 5000+ Essen collection(can be accessed from http://www.esac-data.org/). |
| + | |||
| + | To build a large test set which can reflect real-world queries, it is suggested that every participant makes a contribution to the evaluation corpus. | ||
== Task description == | == Task description == | ||
Revision as of 01:57, 9 May 2007
Status
This is only a very basic draft version of a task proposal. Once more people show interest we can fill in the details.
The goal of the Query-by-Singing/Humming (QBSH) task is the evaluation of MIR systems that take as query input queries sung or hummed by real-world users. More information can be found in:
Please feel free to edit this page.
Query Data
1. Roger Jang's corpus (MIREX2006 QBSH corpus) which is comprised of 2797 queries along with 48 ground-truth MIDI files. All queries are from the beginning of references.
2. ThinkIT corpus comprised of 355 queries and 106 monophonic ground-truth midi files (with MIDI 0 or 1 format). There are no "singing from beginning" gurantee. This corpus will be published after the task running.
3. Noise MIDI will be the 5000+ Essen collection(can be accessed from http://www.esac-data.org/).
To build a large test set which can reflect real-world queries, it is suggested that every participant makes a contribution to the evaluation corpus.
Task description
Classic QBSH evaluation:
- Input: human singing/humming snippets (.wav)
- Database: ground-truth and noise midi files(which are monophonic)
- Output: candidate list.
- Evaluation: Mean Reciprocal Rank (MMR) and Top-X hit rate.
Rainer Typke also suggests a hybrid symbolic/audio query by humming task which combines a few different algorithm modules (like mono/poly phonic transcriber and rhythm/melody matcher) and evaluates them in a more complex database composed of polyphonic audio files. It could be further discussed.
Participants
If you think there is a slight chance that you might want to participate, please add your name and e-mail address to this list
- Xiao Wu (xwu at hccl dot ioa dot ac dot cn)
- Maarten Grachten (maarten dot grachten at jku dot at)
- Jiang Danning (jiangdn at cn dot ibm dot com)
- Niko Mikkila (mikkila at cs dot helsinki dot fi)
