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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


Stage 3 music information retrieval
Stage 3: Music information retrieval


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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