Archive for 2009
ISMIR Oral Session – Folk Songs
Session Title: Folk songs
Session Chair: Remco C. Veltkamp (Universiteit Utrecht, Netherland)
Global Feature Versus Event Models for Folk Song Classification
Ruben Hillewaere, Bernard Manderick and Darrell Conklin
Abstract: Music classification has been widely investigated in the past few years using a variety of machine learning approaches. In this study, a corpus of 3367 folk songs, divided into six geographic regions, has been created and is used to evaluate two popular yet contrasting methods for symbolic melody classification. For the task of folk song classification, a global feature approach, which summarizes a melody as a feature vector, is outperformed by an event model of abstract event features. The best accuracy obtained on the folk song corpus was achieved with an ensemble of event models. These results indicate that the event model should be the default model of choice for folk song classification.
Robust Segmentation and Annotation of Folk Song Recordings
Meinard Mueller, Peter Grosche and Frans Wiering
Abstract: Even though folk songs have been passed down mainly by oral tradition, most musicologists study the relation between folk songs on the basis of score-based transcriptions. Due to the complexity of audio recordings, once having the transcriptions, the original recorded tunes are often no longer studied in the actual folk song research though they still may contain valuable information. In this paper, we introduce an automated approach for segment- ing folk song recordings into its constituent stanzas, which can then be made accessible to folk song researchers by means of suitable visualization, searching, and navigation interfaces. Performed by elderly non-professional singers, the main challenge with the recordings is that most singers have serious problems with the intonation, fluctuating with their voices even over several semitones throughout a song. Using a combination of robust audio features along with various cleaning and audio matching strategies, our approach yields accurate segmentations even in the presence of strong deviations.
Notes: Interesting talk (as always) by Meinard about dealing with real world problems when dealing with folk song audio recordings.
Supporting Folk-Song Research by Automatic Metric Learning and Ranking
Korinna Bade, Andreas Nurnberger, Sebastian Stober, Jörg Garbers and Frans Wiering
Abstract: In folk song research, appropriate similarity measures can be of great help, e.g. for classification of new tunes. Several measures have been developed so far. However, a particular musicological way of classifying songs is usually not directly reflected by just a single one of these measures. We show how a weighted linear combination of different basic similarity measures can be automatically adapted to a specific retrieval task by learning this metric based on a special type of constraints. Further, we describe how these constraints are derived from information provided by experts. In experiments on a folk song database, we show that the proposed approach outperforms the underlying basic similarity measures and study the effect of different levels of adaptation on the performance of the retrieval system.
ISMIR 2009 – The Future of MIR
Posted by Paul in ismir, Music, recommendation on October 29, 2009
This year ISMIR concludes with the 1st Workshop on the Future of MIR. The workshop is organized by students who are indeed the future of MIR.
09:00-10:00 Special Session: 1st Workshop on the Future of MIR
MIR, where we are, where we are going
Session Chair: Amélie Anglade Program Chair of f(MIR)
Meaningful Music Retrieval
Frans Wiering – [pdf]
Notes
- Some unfortunate tendencies: anatomical view of music – a dead body that we do autopsies, time is the loser Traditional production-oriented/
- Measure of similarity: relevance, surprise
- Few interesting applications for end-users
- bad fit to present-day musicological themes
- We are in the world of ‘pure applied research’ – no truth interdisciplinary between music domain knowledge and computer science.
- Music is meaningful (and the underlying personal motivation of most MIR researchers).
- Meaning in musicology – traditionally a taboo suject
- Subjectivity: an indivds. disposition to engage in social and cultural interactions
- Meaning generation process – we have a long-term memory for music –
- Can musical meaning provide the ‘big story line’ for MIR?
The Discipline Formerly Known As MIR
Perfecto Herrera, Joan Serrà, Cyril Laurier, Enric Guaus, Emilia Gómez and Xavier Serra
Intro: Our exploration is not a science-fiction essay. We do not try to imagine how music will be conceptualized, experienced and mediated by our yet-to-come research, technological achievements and music gizmos. Alternatively, we reflect on how the discipline should evolve to become consolidated as such, in order it may get an effective future instead of becoming, after a promising start, just a “would-be” discipline.Our vision addresses different aspects: the discipline’s object of study, the employed methodologies, social and cultural impacts (which are out of this long abstract because of space restrictions), and we finish with some (maybe) disturbing issues that could be taken as partial and biased guidelines for future research.
Notes: One motivation for advancing MIR – more banquets!
- MIR is no more about retrieval than computer science is about computers
- Music Information Retrieval – it’s too narrow
- Music Information or Information about Music?
- Interested in the interaction with music information
- We should be asking more profound questions
- music
- content tresasures in short musical exceprts, tracks performances etc.
- context
- music understanding systems
- Most metadata will be generated in the creation / production phase (hmm.. don’t agree necessarily, all the good metadata (tags, who likes what) is based on context and use which is post-hoc)
- Instead of automatic analysis – build systems to help humans help humans
- Music like water? or Music as dog!!! – a friend – companion –
- Personalization, Findability
- Music turing test
Good, provocative talk
Oral Session 2: Potential future MIR applications
Session Chair: Jason Hockman (McGill University), Program Chair of f(MIR)
Machine Listening to Percussion: Current Approaches and Future Directions – [pdf]
Michael Ward
Abstract: approaches have been taken to detect and classify percussive events within music signals for a variety of purposes with differing and converging aims. In this paper an overview of those technologies is presented and a discussion of the issues still to overcome and future possibilities in the field are presented. Finally a system capable of monitoring a student drummer is envisaged which draws together current approaches and future work in the field.
Notes:
- Challengs: Onset detection of isolated drum strokes
- Onset detection and classification of overlapping drum sounds
- Onset detection and classification in the presence of other instruments
- Variability in Percussive sounds . Dozens of criteria effect the sounds produced (strike velocity, angle, position etc.)
- Future Research Areas
- Extension of recognition to include the wide variety of strokes. (open hh, half-open hh, hh foot splash etc)
MIR When All Recordings Are Gone: Recommending Live Music in Real-Time – [pdf]
Marco Lüthy and Jean-Julien Aucouturier
Recommending live and short lived events. Bandsintown, Songkick, gigulate … pay attention to this paper.
Notes:
- Recommendation for live music in real-time
- Coldplay -> free album when you get a ticket to a coldplay concert – give away the music
- NIN -> USB keys in the toilet – which had strange recording on the file – strange sounds – an FFT of the sounds showed phone number and GPS coordinates – turned into a treasure hunt to a NIN nails concert.
- Komuso Tokugawa – an avatar for a musiciaon in second life. Plays in second life, twitters concert announcements (playing wake for Les Paul in 3 minutes)
- ‘How do we get there in time?’
- JJ walked through how to implement a recommender system in second life
- Implicit preference inferred from how long your avatar listens to a concert (Nicole Yankelovich at Sun Labs should look at this stuff)
- Great talk by JJ – full of energy – neat ideas. Good work.
Poster Session
- Global Access to Ethnic Music: The Next Big Challenge?
Olmo Cornelis, Dirk Moelants and Marc Leman - The Future of Music IR: How Do You Know When a Problem Is Solved?
Eric Nichols and Donald Byrd
ISMIR 2009 – The Industry Panel
On Thursday I participated in the ISMIR industrial panel. 8 members of industry talked about the issues and challenges that they face in industry. I had a good time on the panel, the panelists were all on target and very thoughtful, and there were great questions from the audience. I’m happy too that the IRC channel offered a place for those to vent without the session turning into SXSW-style riot.
Justin Donaldson kept good notes on the panel and has posted them on his blog: ISMIR 2009 Industry Panel
Taiko at the ISMIR 2009 Banquet
During the ISMIR Banquet (held in the most beautiful place in the world, the Kobe Kachoen) we were entertained by the Maturishu a Taiko performance group. They were just fantastic:
ISMIR Oral Session 7 – Harmonic & Melodic Similarity and Summarization
10:30-12:30 Oral Session (OS7) – Harmonic & Melodic Similarity and Summarization
Session Chair: Emilia Gómez (Universitat Pompeu Fabra, Spain)
Nicola Orio and Antonio Rodà
- high-level music dimensions are not reliably computed from audio
- musicologists are more interested in scores
- results with symbolic formats can be a reference for audio-based approaches
- melodic similarity is not a solved problem
- Overview of the approach:
by Bas de Haas, Martin Rohrmeier, Remco Veltkamp and Frans Wiering
- Extract chord labels from audio and symbolic data (not the research focus)
- Not all info is in the data. Need a grammatical model of tonal harmony
- Conveying musical structure (slowing down at boundaries for example)
- Prosody ( stress, direction, grouping) – the heart of the matter
- Musical Affect (happy, sad, etc) – not easy, so ignores this one
This was a very good talk.
Maksim Khadkevich and Maurizio Omologo
Sam Ferguson and Densil Cabrera
Cynthia C.S. Liem and Alan Hanjalic
ISMIR Keynote – Wind instrument-playing humanoid robots
What’s not to love!?!? Robots and Music! This was a great talk.
Wind instrument-playing humanoid robots
Atsuo Takanishi
Some history of robots:
Wabot-2 – early music playing robot
Wabian-2 – walking robots
Emotional Robots
Kobian: Emotional humanoid robot
Voice Producing Robots
Music Performance Robots
(Compare)
Google’s new music search
The news wires are abuzz with Google’s new music search feature. The new Google feature will allow users to search for an artist, song, album or lyric and get a music result that will include album art and a ‘play’ button that will let you listen to the music. MySpace and Lala will be serving up the music and you’ll be able to play any song in full just once. The music results will also include links to Pandora, imeem and Rhapsody. Lyrics search is provided by Gracenote.
Here’s the video announcement:
It’s about time that Google starts to include the ability to listen to search results – this will help. It’s pretty cool, but I don’t think it changes the music discovery game too much. Search is not discovery.
Update: The Register is particularly unimpressed: “Trying to forcefeed punters a lousy service is a bad idea, amplified by the assumption that if Facebook and Google are the feeding tube, we’ll suck it up.”
The SQL Join is destroying music
Posted by Paul in events, Music, The Echo Nest on October 28, 2009
Brian Whitman,one of the founders of the Echo Nest, gave a provocative talk last week at Music and Bits. Some excerpts:
Useless MIR Problems:
- Genre Identification – “Countless PhDs on this useless task. Trying to teach a computer a marketing construct”
Hard but interesting MIR Problems:
- Finding the saddest song in the world
- Predicting Pitchfork and All Music Guide ratings
- Predicting the gender of a listener based upon their music taste
On Recommendation:
- “The best music experience is still very manual… I am still reading about music, not using a recommender.”
- “If we only used collaborative filtering to discover music, the popular artists would eat the unknowns alive.”
- “The SQL Join is destroying music”
Brian’s notes on the talk are on his blog. The slides are online here. Highly recommended:
ISMIR Oral Session 6 – Similarity
Oral Session 6 – Similarity
Chair: Roger Dannenberg
ON RHYTHM AND GENERAL MUSIC SIMILARITY
Tim Pohle, Dominik Schnitzer, Markus Schedl, Peter Knees and Gerhard Widmer
Paper: pdf
Abstract: The contribution of this paper is threefold:
First, we propose modifications to Fluctuation Patterns [14]. The resulting descriptors are evaluated in the task of rhythm similarity computation on the “Ballroom Dancers” collection.Second, we show that by combining these rhythmic descriptors with a timbral component, results for rhythm similarity computation are improved beyond the level obtained when using the rhythm descriptor component alone.Third, we present one “unified” algorithm with fixed parameter set. This algorithm is evaluated on three different music collections. We conclude from these evaluations that the computed similarities reflect relevant aspects both of rhythm similarity and of general music similarity. The performance can be improved by tuning parameters of the “unified” algorithm to the specific task (rhythm similarity / general music similarity) and the specific collection, respectively.
Notes:
- B&O recommender used OFAI
- Nice results
GROUPING RECORDED MUSIC BY STRUCTURAL SIMILARITY
Juan Pablo Bello
Paper: PDF
Abstract: This paper introduces a method for the organization of recorded music according to structural similarity. It uses the Normalized Compression Distance (NCD) to measure the pairwise similarity between songs, represented using beat-synchronous self-similarity matrices. The approach is evaluated on its ability to cluster a collection into groups of performances of the same musical work. Tests are aimed at finding the combination of system parameters that improve clustering, and at highlighting the benefits and shortcomings of the proposed method. Results show that structural similarities can be well characterized by this approach, given consistency in beat tracking and overall song structure.
Notes:
- Normalized Compression Distance (NCD) a universal distance metric.
- Experimental setup – all classical music
A FILTER-AND-REFINE INDEXING METHOD FOR FAST SIMILARITY SEARCH IN MILLIONS OF MUSIC TRACKS
Dominik Schnitzer, Arthur Flexer, Gerhard Widmer
Paper: PDF
ABSTRACT We present a filter-and-refine method to speed up acous- tic audio similarity queries which use the Kullback-Leibler divergence as similarity measure. The proposed method rescales the divergence and uses a modified FastMap [1] implementation to accelerate nearest-neighbor queries. The search for similar music pieces is accelerated by a fac- tor of 10−30 compared to a linear scan but still offers high recall values (relative to a linear scan) of 95 − 99%. We show how the proposed method can be used to query several million songs for their acoustic neighbors very fast while producing almost the same results that a linear scan over the whole database would return. We present a work- ing prototype implementation which is able to process sim- ilarity queries on a 2.5 million songs collection in about half a second on a standard CPU.
Notes: Gaussian similarity features can be expensive.
ISMIR – MIREX Panel Discussion
Stephen Downie presents the MIREX session
Statistics for 2009:
- 26 tasks
- 138 participants
- 289 evaluation runs
Results are now published: http://music-ir.org/r/09results
This year, new datasets:
- Mazurkas
- MIR 1K
- Back Chorales
- Chord and Segmentation datasets
- Mood dataset
- Tag-a-Tune
Evalutron 6K – Human evaluations – this year, 50 graders / 7500 possible grading events.
What’s Next?
- NEMA
- End-to-End systems and tasks
- Qualitative assessments
- Possible Journal ‘Special Issue’
- MIREX 2010 early start: http://www.music-ir.org/mirex/2010/index.php/Main_Page
- Suggestions to model after the ACM MM Grand challenge
Issues about MIREX
- Rein in the parameter explosion
- Not rigorously tested algorithms
- Hard-coded parameters, path-separators, etc
- Poorly specified data inputs/outputs
- Dynamically linked libraries
- Windows submissions
- Pre-compiled Matlab/MEX Submissions
- The ‘graduation’ problem – Andreas and Cameron will be gone in summer.
Long discussion with people opining about tests, data. Ben Fields had a particularly good point about trying to make MIREX better reflect real systems that draw upon web resources.
Music Machinery 