Factors Affecting Music Retrieval in Query-by-Melody Christian Godi FACTORS Accuracy of the Query Provider Query transcription Accuracy of the acoustic Front End Query Length What is Query-by-Melody? Query Transcription? Length of a Query? Architecture of QBM System Query Query
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Factors Affecting Music Retrieval in Query-by-MelodyChristian Godi
It performs an approximate match between a relatively short query transcription
and a much longer monophonic melody. This match is based on a Dynamic Programming procedure which computes Melody Distance.
1. Solo Explorer
2. Ear Analyzer
1. Query Database
2. Melody Database
between the following errors
1. Interval Change
2. Interval Transposition
3. Interval Insertion or Deletion
4. Note Insertion
5. Note Deletion
6. Duration Error
Indicator of QTA is based on comparison of automatically generated and manually verified transcriptions of all the queries in a query database.
Total Transcription Error (TTE)
TTE = no. of deletions + insertions + substitutions
No. of notes in manual transcriptions
Indicator of Music Retrieval Accuracy
The QBM system is supposed to produce an output list of melodies and a target is said to be retrieved correctly if at least one of its
characterizing melodies appear in that list.
The indicator of music retrieval accuracy that is independent of any output list is the MEAN RECIPROCAL RANK (MRR).
MRR = 1/NqΣ1/ranki
Nq= the number of tested queries
Ranki = the position of the melody of the target of query I in an
output list of size Sl = Sd
Under the assumption that each target is characterized by one melody, the mean uncertainty about finding target Ti(i=1,….,Sd) in the output list L[q(Ti)] generated for some query q(Ti) of that target, can be computed as
H(Ti εL[q(Ti)]) = -E[logP(Ti εL[q(Ti)]) ]
RIF = log P = log P
log Po log Sl/Sd
RIF = Remaining information Factor
The QBM system that is capable of always putting the target melody
on top of the output list will yields a RIF=0 whereas a QBM system that behaves like that random system will yield a RIF =1.
Backend when supplied with the perfect query transcriptions it
behaves like a perfect system with RIF=0. But when supplied with
real life queries the performance degrades significantly.
Front ends with the highest transcription accuracies yield the
highest music retrieval accuracies.
RIF as a function of minimal query length (no. of notes)
By plotting the retrieval performances using RIF for the different
front-end/backend combinations as a function of minimal query length one Can see that the performance differences caused by changes of the Front end remain equally important irrespective of this length., RIF starts to raise as soon as query counts <20 notes
RIF is a robust and attractive indicator of
the music retrieval accuracy of a QBM