It is fairly easy to automatically recognize single instruments. However, when more than one instrument plays at the same time, the task becomes much more difficult. In this track, the goal is to build a model based on data collected for both single instruments and example mixtures in order to recognize pairs of instruments.
Training data consists of two datasets: large one containing data for single instruments and much smaller one containing mixtures of pairs of different instruments. Both sets have the same attributes (the single instruments set contains some additional information, though).
The test set contains only data for mixtures. The pairs of instruments that are playing are to be predicted. Note that test and training sets contain different pairs of instruments (i.e. the pairs from the training set do not occur in the test set). Moreover, not all instruments from the training data must also occur in the test part. There also may be some instruments from the test set that only appear in the single instruments part of the training set.
The data consists of the following attributes:
The datasets can be found in the Repository:
Note: you must be registered to this challenge in order to access the files.
Solutions and evaluation
Solution should be a text file containing one pair of labels per line. The names of the instruments in each line can be in any order and should be separated by a comma. Different number of labels in a line will result in an error. Labels are not case sensitive.
The baseline solution can be found in the Repository. It was generated by a modified Nearest Neighbour algorithm (selects two closest neighbours), without any data preprocessing.
The evaluation metric is modified accuracy:
Task 2, Music Instruments, was prepared by prof. Zbigniew Raś and dr Wenxin (Mike) Jiang from University of North Carolina, Charlotte, USA and Warsaw University of Technology, Poland.