Semantic Web Application: Music Retrieval

1. Semantic Web Application: Music Retrieval Ying Ding SLIS, IU

2. What is the Semantic Web? • “An extension of the current Web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.” • Sir Tim Berners-Lee et al., Scientific American, 2001: tinyurl.com/i59p

3. Semantic Web -- Web 3.0 • How to realize that: • machine-understandable semantics of information, and • millions of small specialized reasoning services that provide support in automated task achievement based on the accessible information

4. The current (syntactic / structural) Web

5. Was the Web meant to be more? Hyperlinks – typed hyperlinks Document - data

6. Ontology • The semantic Web is essentially based on ontologies • ontologies are formal and consensual specifications of conceptualizations… • providing a shared and common understanding of a domain that can be communicated across people and application systems

7. Metadata and Semantics

8. Semantic Web - Language tower

9. What is Semantic Web for? • Integrating - trying to solve the problem of data and service integration • Searching - Providing better communication between human and computers by adding machine-processable semantics to data. • Form keyword search  data search  query answer

10. What is current Semantic Web effort? • Lifting document web to data web • Weaving the data web through semantic links (types hyperlinks)

11. Bubbles in April 2008 >2B RDF triples Around 3M RDF links

12. http://www.elec.qmul.ac.uk/easaier/ Enabling Access to Sound Archives through Integration, Enrichment and Retrieval

13. The EASAIER Project EASAIER - Enabling Access to Sound Archives through Integration, Enrichment and Retrieval EU funded project, 30month duration (started May 2006)‏ Partners:

14. EASAIER - Goals Overcome problems for many digital sound archives concerning online access sound materials and related media often separate searching audio content limited EASAIER Framework Integration of Sound Archives Low level audio feature extraction (speech/music)‏ Intelligent User Interface Enhanced Access Tools looping, marking of audio sound source separation time and pitch scale modification Semantic Search Evaluation

15. Semantics in EASAIER Description of metadata using an ontology High-level metadata e.g. title, author of an audio asset sources are databases, files in e.g. DC, MARC Low-level metadata e.g. speech event occurs at timestamp xyz feature extractor tools Semantic Search Search across variety of metadata Search across multiple archives Similarity Search Related content acquisition from the Web

16. The EASAIER System

17. Music Ontology • Overview • Merging existing related ontologies • Developed by QMUL • Cover the major requirements • Widely-adopted • Four core MO components • FRBR • FOAF • Event • Timeline http://musicontology.com/

18. The Music Ontology: Timeline Ontology Expressing temporal information, e.g. This performance happened the 9th of March, 1984 This beat is occurring around sample 32480 The second verse is just before the second chorus

19. The Music Ontology: Event Ontology Event — An arbitrary classification of a space/time region This performance involved Glenn Gould playing the piano This signal was recorded using a XXX microphone located at that particular place This beat is occurring around sample 32480

20. The Music Ontology: FRBR & FOAF FRBR – Functional Requirements for Bibliographic Records Work — e.g. Franz Schubert's Trout Quintet Manifestation — e.g. the "Nevermind" album Item — e.g. my "Nevermind" copy • FOAF – Friend of a Friend • Person • Group • Organization

21. The Music Ontology – Music Production Concepts On top of FRBR: MusicalWork, MusicalManifestation (Record, Track, Playlist, etc.), MusicalItem (Stream, AudioFile, Vinyl, etc.)‏ • On top of FOAF: • MusicArtist, MusicGroup, Arranger, Engineer, Performer, Composer, etc. — all these are defined classes: every person involved in a performance is a a performer... • On top of the Event Ontology: • Composition, Arrangement, Performance, Recording • Others : • Signal, Score, Genre, Instrument, ReleaseStatus, Lyrics, Libretto, etc.

22. The Music Ontology – Music Production Workflow

23. Low-level metadata is output in RDF using Music Ontology Audio Feature extractor Speech recognition service Emotion detection service High-level metadata import DB Schema Mapping e.g. D2R, Virtuoso RDF Views Standardized Metadata import DC, MARC, METS, ... Linked Data ? DBPedia, Geonames, ... Metadata in RDF

24. Use Case: Archive Publication - HOTBED Publishing Extending Hotbed Database Music Ontology Instruments Taxonomy Querying Query Interface the Semantic Archivist Sound Accesstools FeaturesExtraction,Visualization,... Hotbed RDF

25. 1) editing the ontology using WSMT editor to extend the ontology Music Ontology Graphical Edit Music Ontology Text Edit

26. 2) performing tests on the new extension What are the instruments in my taxonomy ? Did i forget any kind of [pipe] ?

27. 3)mapping Scottish Instruments to a general Instruments taxonomy

28. 4) relating and publishing Hotbed Relate tables from hotbed to concepts from the MO Publish on the semantic web via the D2R tool Mapping Music Ontology Hotbed Database RDF Publicationvia D2R tool • The server offers a SPARQL end-point for external apps

29. Mapping Metadata to the Music Ontologies :music a mo:Signal ; dc:title "File 2" ; dc:author "Oliver Iredale Searle" ; :music-performance a mo:Performance ; mo:recorded_as :music ; mo:composer :OliverIredaleSearle ; mo:instrument mo:flute ; mo:performer :KatiePunter ; mo:bpm 50 ; mo:meter "4/4" ; mo:key #BFlatMajor. :KatiePunter a foaf:Person . :ss1 a af:PersonPlaying; af:person :KatiePunter; event:time [ tl:onTimeLine :tl1234; tl:beginsAt "PT0S"; tl:duration "PT16S"; ]. Title: File 2 Author: Oliver Iredale Searle Perfomers: Katie Punter Source Type: Audio Source: File 2 Instrument: Flute Instrument occurrence timings: 0"-16" Time Signature: 4/4 Beats per minute: 50 Tonality: Bb major Searle Testbed

30. Mapping Metadata to the Music Ontologies ALL web service output <xml version="1.0" encoding="UTF-8"> <speech_retrieveResult> <speech_descriptor word="power" audio_material="c:/hotbed/performance/1004.wav" position_sec="10" duration_sec="5" confidence="89" /> </speech_retrieveResult> </xml> <http://www.myarchive.org/signal/1234/event/powerPT10S> a af:Text; af:text "power"; af:confidence "89"; event:time [ a time:timeInterval; tl:onTimeline <http://www.myarchive.org/signal-timeline/1234>; tl:beginsAtDuration "PT10S"; tl:durationXSD "PT5S"; ].

31. Mapping Metadata to the Music Ontologies Vamp Output <metadata type="audio"> <category name="vamp"> <feature name="beats" type="variablerate" description="Detected Beats" unit="N/A"> <data> <event idx="0" timestamp=" 0.0928" duration="0" label="224.69 bpm"/> </data> </feature> </category> </metadata> event:time [ a time:Instant ; tl:onTimeLine :tl898; tl:at "PT0.0928S"; ]; mo:bpm "224.69";

32. Built on top of OpenRDF Sesame 2.0 Query interfaces Web Service (Servlet)‏ HTTP SPARQL Endpoint Web Service provides predefined SPARQL query templates Themes Music, Speech, Timeline, Related media, Similarity Dynamic FILTER constructs Results in SPARQL Query  Results XML Format Interface for RDF metadata import using the Archiver application RDF Storage and Retrieval Component

33. Enhanced Client

34. Web client

35. Related media Double-click

36. Related media on the web (1)‏ Track selection Web related media search launched automatically according to the name of the author Result search for author “Coltrane”

37. Related media on the web (2)‏

38. Demo • http://www.elec.qmul.ac.uk/easaier/index-3.html • http://easaier.deri.at/demo/

39. Demo • Time and Pitch Scale Modification (demo) • Sound source separation (demixing/remixing, Noice reduction, etc.) (demo) • Video time stretching (to slow down or speed up images while retaining optimal sound) (demo)

40. Scenario 1 – Artist Search • Aggregation of music artist information from multiple web sources • Ontology based search: • MusicBrainz data mapped to the MusicOntology • MusicBrainz Web Service: • allows to retrieve artist URI by literal based search • MusicBrainz RDF Dump: • retrieve RDF • use SPARQL to perform queries (e.g. resolve relationships) • Web2.0 Mashups: • Retrieve data (videos, images) from external sources • utilize RSS Feeds, APIs etc. from Youtube, LyricWiki, Google • more accurate results using references from MusicBrainz RDF data

41. Scenario 1 – Artist Search WS Interface “Beatles” <URI> <URI> process data... RDF Dump

42. Scenario 1 – Artist Search

43. Scenario 1 – Artist Search

44. Scenario 2 – Instrument Reasoning • Reasoning over HOTBED instrument scheme • Ontologize data from HOTBED (Scottish Music Archive) • Usage of D2R to lift data from legacy DBs to RDF • Ontologies: • MusicOntology • Instrument Ontology (domain related taxonomy) • Subsumption reasoning: • Retrieve instrument tree • Search for persons that play an instrument • Subclass relations: resolve persons playing more specific instruments • Example: Wind-Instrument < WoodWind < Flute

45. Scenario 2 – Instrument Reasoning • Example: • Search for people playing instrument of type Woodwind

46. Demo 3 – Rules • Infer new knowledge with rules • Domain Rule • Sophisticated Query • Albums based on certain Band/Artist/Instrument • UseCase: The Velvet Underground discography • Available information: • Membership durations • Album release dates • „Founders“ of the band ? • exist _artist, <_band, hasMember, _artist>, <_artist, onDuration, _duration> • forall ?x, <_band, hasMember, ?x>, < ?x, onDuration, ?time> • <?time, notBefore, _duration>  <_band, founder, _artist> • Albums & corresponding members

47. Demo 3 – Rules Basic Information Band Founder Band Duration (Members & Albums) Album Tracks

48. Thanks • Contact • Ying Ding • LI029 • (812) 855 5388 • dingying@indiana.edu