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Music Information Retrieval Information Universe. Seongmin Lim [email protected] Dept. of Industrial Engineering Seoul National University. contents. Brief history of MIR and state of research. Cross media retrieval supporting Natural language queries like mood, melody information.

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music information retrieval information universe

Music Information RetrievalInformation Universe

Seongmin Lim

[email protected]

Dept. of Industrial Engineering

Seoul National University

brief history of mir and state of research
Brief history of MIR and state of research
  • Cross media retrieval supporting Natural language queries like mood, melody information.
    • Contain semantic information taken from community data bases
    • “A Music Search Engine Built upon Audio-based and Web-based Similarity Measures”
  • Query by Example
    • You have an example query having the same representation in the database.
    • For music search: humming, recorded by cell phones, microphones
    • “Music Structure Based Vector Space Retrieval”
stages of first paper
Stages of First Paper
  • “A Music Search Engine Built upon Audio-based and Web-based Similarity Measures”
stage 1 preprocessing the collection
Stage 1: Preprocessing the Collection
  • Using information in the ID3 tag
    • Artist
    • Album
    • Title
  • all duplicates of tracks are excluded to avoid redundancies
  • Live or instrumentals of the same song removed
stage 2 web based features addition
Stage 2: Web based features addition
  • Search on the web for
    • “artist”music
    • “artist”“album”music review
    • “artist”“title”music review –lyrics
stage 2 web based features addition 2
Stage 2: Web based features addition (2)
  • Every term is weighted according to the term frequency ×inverse document frequency (tf×idf) function. w(t,m) of a term t for music piece m. N is the total number of documents.
stage 3 audio based similarity measures
Stage 3: Audio Based Similarity measures
  • For each audio track, Mel Frequency Cepstral Coefficients (MFCCs) are computed on short-time audio segments (called frames)
  • each song is represented as a Gaussian Mixture Model (GMM) of the distribution of MFCCs
  • Kullback-Leibler divergence can be calculated on the means and covariance matrices
  • A rank list of similar tracks is found based on this measure corresponding to each track
gmm gaussian mixture model
GMM(Gaussian Mixture Model)
  • a probabilistic model for representing the presence of sub-populations within an overall population
  • the mixture distribution that represents the probability distribution of observations in the overall population
stage 4 dimensionality reduction
Stage 4: Dimensionality Reduction
  • chi square test to distinguish the most similar terms using audio similarities
  • A is the number of documents in s which contain t
  • B is the number of documents in d which contain t
  • C is the number of documents in s without t
  • D is the number of documents in d without t
  • N is the total number of examined documents
stage 5 vector adaptation
Stage 5: Vector Adaptation
  • Smoothing for tracks where no related information
querying the music search engine
Querying the Music Search Engine
  • method to find those tracks that are most similar to a natural language query
  • extend queries to the music search engine by the word music and send them to Google
  • Query vector is constructed in the feature space from the top 10 pages retrieved
  • Euclidean distances are calculated from the collection tracks and a relevance ranking is got
evaluating the system
Evaluating the System
  • to evaluate on “real-world” queries, a source for phrases which are used by people to describe music is needed
  • Tags provided by AudioScrobblergroundtruth is used
  • 227 tags are used

as test queries

goal of the evaluation
Goal of the evaluation
  • Goals
    • Effect of dimensionality on the feature space
    • Retrieving relevant information
    • Effect of re weighting of the term vectors
    • Effect of query expansion
  • Metrics used : precision values for various recall levels
performance evaluation i
Performance Evaluation -I
  • audio-based term selection has a very positive impact on the retrieval
  • setting 2/50 yields best results
performance evaluation ii
Performance Evaluation -II
  • Effect of re weighting using various re weighting techniques
  • the impact of audiobased vector re-weighting is only marginal
system design of second paper
System design of Second paper
  • “Music structure based vector space retrieval”
stage 1 music information modeling
Stage 1: MUSIC INFORMATION MODELING
  • Music Segmentation by smallest note length
  • Cord modeling
  • Music region content modeling
stage 2 music indexing and retrieval
Stage 2: MUSIC INDEXING AND RETRIEVAL
  • Harmony Event and Acoustic Event
    • each song’s cord and music region information is represented as a Gaussian Mixture Model (GMM) of the distribution of MFCCs
  • n-gram Vector
    • The harmony and acoustic decoders serve as the tokenizers for music signal
    • an event is represented in a text-like format
summary
Summary
  • Natural query vs. query by example
  • Information from web and audio
  • Audio frame segmentation
  • KL divergence vs. vector space modeling
  • Analyzing audio features
  • Data itself vs. metadata
  • domain knowledge of music
end of document

End of Document

Seongmin Lim

[email protected]

Dept. of Industrial Engineering

Seoul National University

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