music information retrieval information universe

Music Information RetrievalInformation Universe

Seongmin Limhovern@snu.ac.kr

Dept. of Industrial EngineeringSeoul National University

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contents

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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”

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Stages of First Paper “A Music Search Engine Built upon Audio-based and

Web-based Similarity Measures”

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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

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Stage 2: Web based features addition Search on the web for- “artist”music- “artist”“album”music review- “artist”“title”music review –lyrics

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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.

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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

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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

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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

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Stage 5: Vector Adaptation Smoothing for tracks where no related information

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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

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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 AudioScrobbler groundtruth is used

227 tags are used as test queries

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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

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Performance Evaluation -I audio-based term

selection has a very positive impact on the retrieval

setting 2/50 yields best results

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Performance Evaluation -II Effect of re weighting using various re weighting

techniques

the impact of audiobased vector re-weighting is only marginal

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Performance Evaluation –III (other metrics)

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Examples

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System design of Second paper “Music structure based vector space retrieval”

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Music Layout : The Pyramid

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Stage 1: MUSIC INFORMATION MODELING Music Segmentation by smallest note length

Cord modeling

Music region content modeling

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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

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Stage 3: Music information retrieval

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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

Seongmin Limhovern@snu.ac.kr

Dept. of Industrial EngineeringSeoul National University