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Andreas Rauber Vienna University of Technology ifs.tuwien.ac.at/~andi

Multi-modal and Multi-functional Aspects of Information and their Effects on Findability, Information-Hiding, and Implicit Interaction. Andreas Rauber Vienna University of Technology http://www.ifs.tuwien.ac.at/~andi. Introduction.

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Andreas Rauber Vienna University of Technology ifs.tuwien.ac.at/~andi

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  1. Multi-modal and Multi-functional Aspects of Information and their Effects on Findability, Information-Hiding, and Implicit Interaction Andreas Rauber Vienna University of Technology http://www.ifs.tuwien.ac.at/~andi

  2. Introduction • Where and what is information, and what is it used for? • Three core areas of focus • Multimodality: what aspects of a piece of information are there? • Multifunctionality: why was a piece of information created and why is it being searched for? • Information: what is it? • Adressed in the context of 3 thematic areas • Music IR • Web Archiving • Digital Preservation • Going top-down from different high-level incarnations of information in different modalities via different functions to the actual building blocks – and back again

  3. Outline (1) Music retrieval: • Audio and what else? Multimodality issues • (What is the function of a particular musical fragment?) • (What is the intention of the user searching or finding it?) (2) Web Archive retrieval: • (Obviously multimodal) • Information functions, privacy and the need for information hiding? • Search and the searcher‘s intention (3) Digital Preservation: • Significant properties & atomic information • Nothing new, but: is there a conceptual model rather than ad-hoc experimentation

  4. Symbolic: MIDI, mod, ... www.samplesmith.com Scores: Scan, MusicXML www.westminster.gov.uk Music IR – Music? What is „Music“? • Music, of course! Audio: wav, au, mp3, ...

  5. Music IR – Music? PlaySOM & PocketSOMPlayer MIREX • Feature Extraction: • Frequency spectra analysis • Psycho-acoustic models • www.ifs.tuwien.ac.at/mir/audiofeatureextraction.html

  6. Text • Song lyrics • Artist Biographies • Websites: Fanpages, Album Reviews, Genre descriptions • Community data • Playlists • Market basket • Band evolution • Video/Images • Album covers • Music videos Music IR – Music? What is „Music“? • Music, of course! • Audio: wav, au, mp3, ... • Symbolic: MIDI, mod, ... • Scores: Scan, MusicXML www.samplesmith.com www.westminster.gov.uk

  7. Music IR – Music? Text: Song lyrics • Convey a lot of musical information • Some genres strongly related with texts • Semantics of music: love songs, christmas songs, ... • Standard Text-IR: content analysis • Genre-Analysis: style, rhymes, stop-words,.. • Lyric portals • 2 SOMs: Music, Text • Analysis of cluster structure

  8. Text and Audio Christmas songs

  9. Text and Audio Speech Reggae

  10. Text and Audio Hip-Hop Pop

  11. Text and Audio Lyrics-based audio classification • BOW, tfxidf • Text Genre Features: • ExclamationMark, colon, singleQuote, comma, questionMark, full-stop, hyphen, semicolon • Counts of digits d0-d9 • CharsPerWord • WordsPerLine, UniqueWordsPerLine, UniqueWordsRatio • WordsPerMinute • PartOfSpeech: nouns, verbs, pronouns, prepositions, adverbs, articles, modals, adjectives • Rhyme Features: phoneme transcription + rhyme schemes words per minute Rhymes AABB

  12. Text and Audio • 25 combinations of feature sets (RP, RH, SSD, BOW, Rhyme, Part-of-Speech, Text genre statistic) • Different classifiers: k-NN, Naive Bayes, Decision Trees, Support Vector Machines • Similar trends with all classifiers • Assuming SSD as best audio-only classier to be baseline • Statistical significance tests against that baseline • 10-fold cross-validation (Rudolf Mayer, Robert Neumayer, and Andreas Rauber. Combination of Audio and Lyrics Features for Genre Classification in Digital Audio Collections. ACM Multimedia 2008.)

  13. Text and Audio

  14. Text • Song lyrics • Artist Biographies • Websites: Fanpages, Album Reviews, Genre descriptions • Community data • Playlists • Market basket • Band evolution • Video/Images • Album covers • Music videos Music IR – Music? What is „Music“? • Music, of course! • Audio: wav, au, mp3, ... • Symbolic: MIDI, mod, ... • Scores: Scan, MusicXML www.samplesmith.com www.westminster.gov.uk

  15. Music IR – Music? • There is more to music than sound and text • Which genre is this album?

  16. Music IR – Music? • There is more to music than sound and text • Which genre is this album?

  17. Music IR – Music? • There is more to music than sound and text • Which genre is this album?

  18. Music IR – Music? • There is more to music than sound and text • Which genre is this album?

  19. Music IR – Music? • There is more to music than sound and text • Which genre is this album?

  20. Music IR – Music? • There is more to music than sound and text • Which genre is this album?

  21. Music IR – Music? • There is more to music than sound and text • Which genre is this album?

  22. Music IR – Music? • There is more to music than sound and text • Which genre is this album?

  23. Outline (1) Music retrieval: • Audio and what else? Multimodality issues • (What is the function of a particular musical fragment?) • (What is the intention of the user searching or finding it?) • „Modalities“ in other domains: Text (formatting, layout, references) • General concept of perspectives of information instead of ad-hoc? (2) Web Archive retrieval: • (Obviously multimodal) • Privacy functions and the need for information hiding? • Search and the searcher‘s intention (3) Digital Preservation: • Significant properties & atomic information

  24. Web Archiving & Ethics Web archiving initiatives crawl and archive web data Essential activity to ensure valuable content is being preserved But: Currently most archives are closed to public Mostly due to legal reasons Need a legal solution Is this all? Ethical implications? Privacy? Can we analyze data & searches to guarantee acceptable usage? (Andreas Rauber, Max Kaiser, Bernhard Wachter. Ethical Issues in Web Archive Creation and Usage: Towards a Research Agenda. Proceedings International Workshop on Web Archiving and Digital Preservation (IWAW 2008)

  25. Web Archiving & Ethics • Web is both publication and communication platform • Can we identify, which pages are “published” and which are “posted”? • Can we distinguish public data vs. private information? • Web Archive as eternal memory • Can we identify who posted something and when? • Can we tell children/teenagers postings? • Can we identify potentially sensitive (snippets of) information? • Can we model “forgetting” or fuzziness? • Web Archives as sources of valuable information • Can we identify what somebody is doing in a Web Archive? (HR check-up vs. family history research vs. information look-up) • What is acceptable usage? acceptable queries?

  26. Web Archiving & Ethics • Web Archives and IR • If we can do all of the above: do we know what to do with it? • Can we design an IR system that serves information in an ethically acceptable manner? • What to do with it: limiting/blocking access orexcluding from archive, excluding from index, etc.? • Information hiding in retrieval? Censorship? • Will become more critical as power of multimedia search increases • Goal: establish the context of information & usage • who created it • for what reason was it created • what kind of information does it contain • what is it being used for?

  27. Web Archiving: Classification Case Study • Analyze function of a piece of information • Identifying potentially private segments • Approach: • Take text documents, identify which ones potentially private • Pages • Paragraphs • Train a classifier (SVM, Bayesian Networks, …) • Need to integrate more fine-granular analysis (POS, snippets) • Similar to Genre Classification • Works, but more open questions than solutions • Combine with query analysis, domain analysis, usage…

  28. Outline (1) Music retrieval: • Audio and what else? Multimodality issues • (What is the function of a particular musical fragment?) • (What is the intention of the user searching or finding it?) (2) Web Archive retrieval: • (Obviously multimodal) • Privacy functions and the need for information hiding? • Search and the searcher‘s intention • Functions in music: emotions, ringtones, audio track for illustration or text/presentation • What is the intention of the user searching for it? • Analyze and match functions and users / usecases / needs (3) Digital Preservation: • Significant properties & atomic information

  29. Digital Preservation • Ensure that digital objects remain accessible in the future • Bit-level preservation: storage • Logical preservation: Objects -> Software -> OS -> HW • Approaches: Migration, Emulation-> some aspects lost in the process • Question: What to preserve? -> Preservation Planning • Significant properties: • Technical: format characteristics, functionalities,... • Intellectual: content, meaning, usage, ... • Authenticity

  30. Digital Preservation • Digital Preservation raises a lot of IR questions from a different perspective • IR Research activities in DP (in our group) • Establishing context of information objects • Identifying significant properties • Measuring how well certain significant properties are preserved e.g. after migration or during emulation • Core questions • What is (an atomic piece of) information?(textsnippet + formatting + position + semantic + action) • What does it evolve to given it‘s environment (groups of objects, usage, different aspects/views of information,...)?

  31. Core Questions and Challenges • Definition of information • What is a piece of information? Smallest building block? • What can it evolve to if combined? • What is the context of information? • Does the concept of Memes apply? • What are the significant properties of information objects? • Functions of information • What different functions does a piece of information have? • Who created it for which purpose? • How can they be modeled? Matched with user needs? • Multi-modality • Which modalities are there? • How are they represented? • Which features can describe them

  32. Core Questions and Challenges • (4) Retrieval consequences: • Can information be found? (How? by which aspect of information?) • Is it designed to be found? When should it be found? • Where / in which modality shall we look for it? • (5) How can we establish a match between • The function of (a piece or a collection of) information • The functional needs of a user • The modalities and representations to use for searching it • (6) How to test / evaluate? • Use cases? Tasks? (clearly defined? generic?) • Benchmark collections? • From building blocks of information, via which function does it exhibit, to which modalities and representations to combine to retrieve and present it – in a single model?

  33. Thank You!

  34. Text and Audio • Western popular music: 10 genres • Country, Folk, Grunge, Hip-Hop, Metal, Pop, Punk Rock, R&B, Reggae, Slow Rock • `Small' Collection: 600 songs • 159 artists • Classes of equal size (60 songs per class) • Lyrics manually cleansed • `Large' Collection: 3010 songs • 188 artists • Unbalanced, 180-380 songs per class • Lyrics automatically fetched, no manual cleansing

  35. Text and Audio

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