2010:Symbolic Music Similarity and Retrieval
The text is copied over 2006:Symbolic Melodic Similarity page.
Task suggestion: Symbolic Melodic Similarity
1. Retrieve the most similar incipits from the UK subset of the RISM A/II collection (about 15,000 incipits), given one of the incipits as a query, and rank them by melodic similarity. Both the query and the collection are monophonic. Half the queries are hummed or whistled queries that have been converted to MIDI, thus with slight rhythmic and pitch imperfections, and half the queries are quantized in pitch and rhythm.
2. Like task 1, but with two collections of mostly polyphonic MIDI files to be searched for matches. The queries would still be monophonic. The first collection would be 10,000 randomly picked MIDI files from a collection of about 60,000 MIDI files that were harvested from the Web. They include different genres (Western and Asian popular music, classical music, ringtones, just to name a few). The second collection would be more focused: about 1000 .kar files (Karaoke MIDI files) with mostly Western popular music which stem from the same web harvest.
Task 1: Input: Parameters: - the name of a directory containing about 15,000 MIDI files containing mostly monophonic incipits and - the name of one MIDI file containing a monophonic query.
The program will be called 6 times. Three of the queries are going to be quantized (produced from symbolic notation) and three produced by humming or whistling, thus with slight rhythmic and pitch deviations.
Expected Output: - a list of the names of the 10 most similar matching MIDI files, ordered by melodic similarity. Write the file name in separate lines, without empty lines in between.
Task 2: Input: same interface as for task 1, thus the name of the directory with files to be searched and the name of the query. However, the directory will contain either about 10,000 mostly polyphonic MIDI files or 1000 Karaoke files.
Output: a list of the names of 10 different MIDI file names that contain melodically similar musical material, ordered by similarity, plus for each file the time (offset from the beginning in seconds) where the query matches and where the matching bit ends. If the query matches in more than one position, return the position of the most similar match (or any one of them if there is more than one most similar match). If the algorithm does not align the query with the MIDI file at any particular position, just return 0 as start time and the duration of the MIDI file as end time.
Sample output line:
(means that somefile.mid matches the query, and the matching bit starts at the very beginning of the file and ends 2.3 seconds later). The most similar match should be returned first.
Building the ground truth
Unlike last year, it is now nearly impossible to manually build a proper ground truth in advance.
Because of that, after the algorithms have been submitted, their results are going to be pooled for every query, and every participant is going to be asked to judge the relevance of the matches for some queries. To make that a manageable burden, it is important that the algorithms do not only return the names of the matching MIDI files for task 2, but also where the matching bit starts and ends in the matching MIDI file. We can then automatically extract those matching bits and put them into small new MIDI files whose relevance can then be quickly checked.
Use the same measures as [last year] to compare the search results of the various algorithms.