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A Music Search EngineBuilt upon Audio-based andWeb-based Similarity Measures
P. Knees, T., Pohle, M. Schedl, G. Widmer
SIGIR 2007
INTRODUCTION
Basically all existing music search systems make use of manually assigned subjective meta-information like genre or style to index the underlying music collection. Explicit manual annotations A small set of meta-data
Recent approaches Content-based analysis of the audio files Collaborative recommendations Incorporate information from different sources
RELATED WORK
Query-by-example Query-by-Humming/Singing (QBHS) Operate on MIDI Music piece → Meta-data
Cross-media Semantic ontology
Semantic relations Crawler on “audio blogs” Word sense disambiguation Text surrounding the links to audio files Last.fm – listening habits & tags
PREPROCESSING THE COLLECTION
ID3 tags Artist Album Title
Ignored Only speech pieces ( skit in rap) Intro / Outro Duration below 1 minute
WEB-BASED FEATURES
Queries to Google1. “artist” music2. “artist” “album” music review3. “artist” “title” music review -lyrics
For each query, retrieve top-ranked 100 pages Clean HTML tags and stop words in 6 languages
WEB-BASED FEATURES (CONT.)
term list of each music piece Remove all terms with dftm <= 2
global term list Remove all terms that co-occur < 0.1%
Resulting 78,000 terms (dimensions) weight( t, m )
tf * idf N – # of music pieces mpft – music piece frequency
Cosine normalization Removes the influence of the length of pages
AUDIO-BASED SIMILARITY
MFCCs, Gaussian Mixture Model, KL divergence
Problem Hubs - frequently similar Outliers - never similar to others Triangle inequality - does not fulfill
Author’s previous work solve these problems
AUDIO-BASED SIMILARITY (CONT.)
Always similar – hubs ndist(A) = distance to the nth nearest neighbour
g(A, Pi) = Dbasic(A, Pi) / ndist(Pi), for all i
sort g(A, Pi) ascending, pick nth value as f(A)
Dn-NN norm(A, B) = Dbasic(A, B) / ( f(A) * f(B) )
Never similar – outliers like above
Triangle inequality sort Dbasic(A, Pi), for all i
interpolating Dbasic(A, B) into Dbasic(A, Pi)
DP(A, B) is the rank of Dbasic(A, B) in Dbasic(A, Pi)
Dpv(A, B) = DP(A, B) + DP(B, A)
DIMENSIONALITY REDUCTION
χ2 test s : 100 most similar tracks d : 100 most dissimilar tracks Calculate χ2( t, s )
N terms with highest value are then joined into a global list
s d
tA B
!tC D
n __ 50 100 150
dimensionality
78000 4679 6975 8866
VECTOR ADAPTATION
Particularly necessary for tracks where no related information could be retrieved from the web
Perform a simple smoothing
QUERYING THE MUSIC SEARCH ENGINE
Original query + “music” -site:last.fm
Google search 10 top-most web pages Map to vector space Calculate Euclidean distances
AUDIOSCROBBLER GROUND TRUTH
Common approach genre information several drawbacks
http://www.audioscrobbler.net Web services to access Last.fm data Tag information provided by Last.fm drawbacks
Using top tags for tracks (total 227 tags)
PERFORMANCE EVALUATION
Dimensionality reduction
pass significance test
χ2 /50 best
random permutation
FUTURE WORK
Dimensionality reduction
12601tracks
ID3 tag
Web-based feature
Google search
Audio similarity
Vector adaptatio
n
Query Google search
Vector space
results
FUTURE WORK
Dimensionality reduction
12601tracks
ID3 tag
Web-based feature
Google search
Audio similarity
Vector adaptatio
n
Query Google search
Vector space
合輯 , remix
results
FUTURE WORK
Dimensionality reduction
12601tracks
ID3 tag
Web-based feature
Google search
Audio similarity
Vector adaptatio
n
Query Google search
Vector space
Lyrics
results
FUTURE WORK
Dimensionality reduction
12601tracks
ID3 tag
Web-based feature
Google search
Audio similarity
Vector adaptatio
n
Query Google search
Vector space
Indexing documents
results
FUTURE WORK
Dimensionality reduction
12601tracks
ID3 tag
Web-based feature
Google search
Audio similarity
Vector adaptatio
n
Query Google search
Vector space
PLSA
results
FUTURE WORK
Dimensionality reduction
12601tracks
ID3 tag
Web-based feature
Google search
Audio similarity
Vector adaptatio
n
Query Google search
Vector space
Computation inefficient
results
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