1 / 25

Music Information Retrieval Information Universe

Music Information Retrieval Information Universe. Seongmin Lim hovern@snu.ac.kr 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.

dash
Download Presentation

Music Information Retrieval Information Universe

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Music Information RetrievalInformation Universe Seongmin Lim hovern@snu.ac.kr Dept. of Industrial Engineering Seoul National University

  2. contents

  3. 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”

  4. Stages of First Paper • “A Music Search Engine Built upon Audio-based and Web-based Similarity Measures”

  5. 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

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

  7. 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.

  8. 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

  9. 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

  10. 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

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

  12. 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

  13. 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

  14. 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

  15. Performance Evaluation -I • audio-based term selection has a very positive impact on the retrieval • setting 2/50 yields best results

  16. Performance Evaluation -II • Effect of re weighting using various re weighting techniques • the impact of audiobased vector re-weighting is only marginal

  17. Performance Evaluation –III (other metrics)

  18. Examples

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

  20. Music Layout : The Pyramid

  21. Stage 1: MUSIC INFORMATION MODELING • Music Segmentation by smallest note length • Cord modeling • Music region content modeling

  22. 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

  23. Stage 3: Music information retrieval

  24. 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

  25. End of Document Seongmin Lim hovern@snu.ac.kr Dept. of Industrial Engineering Seoul National University

More Related