Shades of Music. Projektarbeit
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1 Shades of Music Projektarbeit Tim Langer LFE Medieninformatik Betreuer: Dominikus Baur Verantwortlicher Hochschullehrer: Prof. Dr. Andreas Butz LMU Department of Media Informatics Projektarbeit Shades of Music Slide 1 / 17
![Goal Recommendation of relevant objects (based on their similarity to a user s choice [Query-by-Example]) Measure musical similarity for song segments instead of whole songs to consider the inner](/docs-images/90/101388875/images/2-1.jpg "diversity of a song Visualize the emerging links between songs Music recommendation by last.fm [1] LMU Department of Media Informatics Projektarbeit Shades of Music tim.langer@campus.lmu.")
2 Goal Recommendation of relevant objects (based on their similarity to a user s choice [Query-by-Example]) Measure musical similarity for song segments instead of whole songs to consider the inner diversity of a song Visualize the emerging links between songs Music recommendation by last.fm [1] LMU Department of Media Informatics Projektarbeit Shades of Music tim.langer@campus.lmu.de Slide 2 / 17
3 Overview Related Work Similarity Measuring Segmentation Shades of Music General Concept Backend Calculations Graphical User Interface Conclusion & Outline LMU Department of Media Informatics Projektarbeit Shades of Music Slide 3 / 17
4 Symbolic Similarity Measuring Use existent data such as Lyrics Scores Midis Rhythmic patterns Tags (e.g. ID3-Tags) Intuitive Easy to use and understand German national anthem [2] LMU Department of Media Informatics Projektarbeit Shades of Music Slide 4 / 17
5 Acoustic Similarity Measuring Measure data derived from the audio file, such as Loudness Pitch Timbre Bar & Beat (~ Rhythm) Tempo Raw input no faults But: limited technique, no subjectivity LMU Department of Media Informatics Projektarbeit Shades of Music Slide 5 / 17
6 Subjective Similarity Measuring Likeness is subjective and music is emotional Music classification should include listener s subjectivity Collaborative Filtering LMU Department of Media Informatics Projektarbeit Shades of Music tim.langer@campus.lmu.de Slide 6 / 17
![representation Segmentation by [4] Self-Similarity [3] LMU Department](/docs-images/90/101388875/images/7-2.jpg "of Media Informatics Projektarbeit Shades of Music tim.langer@campus.")
7 Segmentation General idea: repetition Self-Similarity Verse & Chorus scheme Audio thumbnails A: Midi representation B: Texture representation Segmentation by [4] Self-Similarity [3] LMU Department of Media Informatics Projektarbeit Shades of Music Slide 7 / 17
8 Shades of Music Query-by-Example paradigm Faithless God Is a DJ used for this presentation Recommend music Discover unknown links Web-based multi-user system Subjective Similarity through Feedback process Collaborative Filtering possible User-Clustering possible (not used) LMU Department of Media Informatics Projektarbeit Shades of Music tim.langer@campus.lmu.de Slide 8 / 17
9 Shades of Music Use Case Users upload their songs Analyze songs & calculate similarities (own collection only) Listen to songs and find sections from other song similar to the one currently playing LMU Department of Media Informatics Projektarbeit Shades of Music Slide 9 / 17
10 Shades of Music - Backend Calculation using The Echonest.com framework Segmentation of a song into song sections Acoustic attributes Aggregate Segments (milliseconds) and their measured attributes with the sections (seconds) Similarity indicated by the absolute difference between the attribute s values, of two sections, proportional to the maximum Sections of Faithless God is a DJ LMU Department of Media Informatics Projektarbeit Shades of Music tim.langer@campus.lmu.de Slide 10 / 17
11 Shades of Music - Backend Calculation example Calculation of the average pitch chroma-vector for the last section of Faithless God is a DJ Absolute pitch difference between the last section from God is a DJ and a 80 second long section from Thriller LMU Department of Media Informatics Projektarbeit Shades of Music tim.langer@campus.lmu.de Slide 11 / 17
12 Shades of Music - Backend Feedback integration Feedback overrides acoustic measurement Stored value = (feedback scale value 1 ) * 0.25 range from 0.0 to 1.0 just like the calculations Use the accumulated average value from all known votes for an entry belonging to section A and section B Vote on total adjust all other attributes User links via duplicated songs Detection through Levenshtein distance Use existing entry that is already linked to other users LMU Department of Media Informatics Projektarbeit Shades of Music tim.langer@campus.lmu.de Slide 12 / 17
13 Shades of Music - Interface LMU Department of Media Informatics Projektarbeit Shades of Music tim.langer@campus.lmu.de Slide 13 / 17
14 Shades of Music - Interface Interaction possibilities Play a song (of course!) Select similarity attributes to display Rate a recommendation (scale 1 to 5) Voting option Play a recommended song Detail view of the pitch-wise recommendations for Faithless God is a DJ LMU Department of Media Informatics Projektarbeit Shades of Music tim.langer@campus.lmu.de Slide 14 / 17
15 Conclusion Problems Scalability: (n*(n-1))/2 comparisons for each attribute Echonest segmentation unsatisfactory Acoustic measuring can only be a first step (feedback included but requires a lot of work to improve the system) Varying section length (e.g. compare a 2s section with a 20s section) leads to low meaningful results No local file upload No section labeling (e.g. Verse & Refrain) would be helpful LMU Department of Media Informatics Projektarbeit Shades of Music tim.langer@campus.lmu.de Slide 15 / 17
16 Possible Extensions & Outlook Extensions Allow to create, delete and edit section borders Integrate user spanning song similarities (currently excluded) Alternative use cases Compare a calculation-only system with a user-only system Visualize larger collections with song-section links (no Query-By- Example) Find music samples (similar to Whosampled.com) and/or split remixes or mixed songs (e.g. DJ Sets) LMU Department of Media Informatics Projektarbeit Shades of Music tim.langer@campus.lmu.de Slide 16 / 17
17 Quellen (1) (2) (3) J. Foote. Visualizing music using self-similarity. In Proceedings of the seventh ACM international conference on Multimedia (Part 1), pages ACM New York, NY, USA, 1999 (4) J. Aucouturier and M. Sandler. Segmentation of musical segments using hidden Markov models. Preprints-Audio Engineering Society, 2001 LMU Department of Media Informatics Projektarbeit Shades of Music Slide 17 / 17
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