Posts Tagged ismir2009
ISMIR 2009 is over – but it will not be soon forgotten. It was a wonderful event, with seemingly flawless execution. Some of my favorite things about the conference this year:
- The proceedings – distributed on a USB stick hidden in a pen that has a laser! And the battery for the laser recharges when you plug the USB stick into your computer. How awesome is that!? (The printed version is very nice too, but it doesn’t have a laser).
- The hotel – very luxurious while at the same time, very affordable. I had a wonderful view of Kobe, two very comfortable beds and a toilet with more controls than the dashboard on my first car.
- The presentation room – very comfortable with tables for those sitting towards the front, great audio and video and plenty of power and wireless for all.
- The banquet – held in the most beautiful room in the world with very exciting Taiko drumming as entertainment.
- The details – it seems like the organizing team paid attention to every little detail and request – they had taped numbers on the floor so that the 30 folks giving their 30 second pitches during poster madness would know just where to stand, to the signs on the coffeepots telling you that the coffee was being made, to the signs on the train to the conference center welcoming us to ISMIR 2009. It seems like no detail was left to chance.
- The food – our stomachs were kept quite happy – with sweet breads and pastries every morning, bento boxes for lunch, and coffee, juices, waters, and the mysterious beverage ‘black’ that I didn’t dare to try. My absolute favorite meal was the box lunch during the tutorial day – it was a box with a string – when you are ready to eat you give the string a sharp tug – wait a few minutes for the magic to do its job and then you open the box and eat a piping hot bowl of noodles and vegetables. Almost as cool as the laser-augmented proceedings.
- The city – Kobe is a really interesting city – I spent a few days walking around and was fascinated by it all. I really felt like I was walking around in the future. It was extremely clean, the people will very polite, friendly and always willing to help. Going into some parts of town was sensory overload, the colors, sounds, smells, the sights were overwhelming – it was really fun.
- the Keynote – music making robots – what more is there to say.
- The Program – the quality of papers was very high – there was some outstanding posters and oral presentations. Much thanks to George and Keiji for organizing the reviews to create a great program. (More on my favorite posters and papers in an upcoming post)
- f(mir) – The student-organized workshop looked at what MIR research would look like in 10, 20 or even 50 years (basically after I’m dead and gone). The presentations in this workshop were quite provactive – well done students!
I write this post as I sit in the airport in Osaka waiting for my flight home. I’m tired, but very energized to explore the many new ideas that I encountered at the conference. It was a great week. I want to extend my personal thanks to Professor Fujinaga and Professor Goto and the rest of the conference committee for putting together a wonderful week.
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:
- MIR 1K
- Back Chorales
- Chord and Segmentation datasets
- Mood dataset
Evalutron 6K – Human evaluations – this year, 50 graders / 7500 possible grading events.
- 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.
- (PS3-1) Automatic Identification for Singing Style based on Sung Melodic Contour Characterized in Phase Plane
Tatsuya Kako, Yasunori Ohishi, Hirokazu Kameoka, Kunio Kashino and Kazuya Takeda
- (PS3-2) Automatic Identification of Instrument Classes in Polyphonic and Poly-Instrument Audio
Philippe Hamel, Sean Wood and Douglas Eck
Looks very interesting
- (PS3-3) Using Regression to Combine Data Sources for Semantic Music Discovery
Brian Tomasik, Joon Hee Kim, Margaret Ladlow, Malcolm Augat, Derek Tingle, Rich Wicentowski and Douglas Turnbull
- (PS3-4) Lyric Text Mining in Music Mood Classification
Xiao Hu, J. Stephen Downie and Andreas Ehmann
lyrics and modod – surprising results!
- (PS3-5) Robust and Fast Lyric Search based on Phonetic Confusion Matrix
Xin Xu, Masaki Naito, Tsuneo Kato and Hisashi Kawai
Phonetic confusion – misheard lyrics! KDDI – must see this.
- (PS3-6) Using Harmonic and Melodic Analyses to Automate the Initial Stages of Schenkerian Analysis
Schenkerian analysis – what is this really?
- (PS3-7) Hierarchical Sequential Memory for Music: A Cognitive Model
James Maxwell, Philippe Pasquier and Arne Eigenfeldt
Cognitive model for online learning.
- (PS3-8) Additions and Improvements in the ACE 2.0 Music Classifier
Jessica Thompson, Cory McKay, J. Ashley Burgoyne and Ichiro Fujinaga
Open source MIR in java
- (PS3-9) A Probabilistic Topic Model for Unsupervised Learning of Musical Key-Profiles
Diane Hu and Lawrence Saul
topic mode for key finding
- (PS3-10) Publishing Music Similarity Features on the Semantic Web
Dan Tidhar, György Fazekas, Sefki Kolozali and Mark Sandler
SoundBite – distributed feature collection
- (PS3-11) Genre Classification Using Bass-Related High-Level Features and Playing Styles
Jakob Abesser, Hanna Lukashevich, Christian Dittmar and Gerald Schuller
- (PS3-12) From Multi-Labeling to Multi-Domain-Labeling: A Novel Two-Dimensional Approach to Music Genre Classification
Hanna Lukashevich, Jakob Abeßer, Christian Dittmar and Holger Großmann
Fraunhofer – autotagging
- (PS3-13) 21st Century Electronica: MIR Techniques for Classification and Performance
Dimitri Diakopoulos, Owen Vallis, Jordan Hochenbaum, Jim Murphy and Ajay Kapur
Automated ISHKURS with multitouch – woot
- (PS3-14) Relationships Between Lyrics and Melody in Popular Music
Eric Nichols, Dan Morris, Sumit Basu and Chris Raphael
Text features vs melodic features – where do the stressed syllables fall
- (PS3-15) RhythMiXearch: Searching for Unknown Music by Mixing Known Music
Makoto P. Kato
Looks like an echo nest remix: AutoDJ
- (PS3-16) Musical Structure Retrieval by Aligning Self-Similarity Matrices
Benjamin Martin, Matthias Robine and Pierre Hanna
- (PS3-17) Exploring African Tone Scales
Dirk Moelants, Olmo Cornelis and Marc Leman
No standardized scales – how do you deal with that?
- (PS3-18) A Discrete Filter Bank Approach to Audio to Score Matching for Polyphonic Music
Nicola Montecchio and Nicola Orio
- (PS3-19) Accelerating Non-Negative Matrix Factorization for Audio Source Separation on Multi-Core and Many-Core Architectures
Eric Battenberg and David Wessel
Runs NMF on GPUs and openMP
- (PS3-20) Musical Models for Melody Alignment
Peter van Kranenburg, Anja Volk, Frans Wiering and Remco C. Veltkamp
alignment of folks songs
- (PS3-21) Heterogeneous Embedding for Subjective Artist Similarity
Brian McFee and Gert Lanckriet
Crazy ass features!
- (PS3-22) The Intersection of Computational Analysis and Music Manuscripts: A New Model for Bach Source Studies of the 21st Century
Masahiro Niitsuma, Tsutomu Fujinami and Yo Tomita
Music Recommendation and playlisting
Session Chair: Douglas Turnbull
CONTINUOUS PLSI AND SMOOTHING TECHNIQUES FOR HYBRID MUSIC RECOMMENDATION
by Kazuyoshi Yoshii and Masataka Goto
- Unexpected encounters with unknown songs is increasingly important.
- Want accurate and diversifed recommendations
- Use a probabilistic approach suitable to deal with uncertainty of rating histories
- Compares CF vs. content-based and his Hybrid filtering system
Approach: Use PLSI to create a 3-way aspect model: user-song-feature – the unobservable category regading genre, tempo, vocal age, popularity etc. – pLSI typical patterns are given by relationships between users, songs and a limited number of topics. Some drawbacks: PLSI needs discrete features, multinomial distributions are assumed. To deal with this formulate continuous pLSI, use gaussian mixture models and can assume continuous distributions. A drawback of continuous pLSI – local minimum problem and the hub problem. Popular songs are recommended often because of the hubs. How to deal with this: Gaussian parameter tying – this reduces the number of free parameters. Only the mixture weights vary. Artist-based song clustering: Train an artist-based model and update it to a song-based model by an incremental training method (from 2007).
Here’s the system model:
Evaluation: They found that using the techniques to adjust model complexity significantly improved the accuracy of recommendations and that the second technique could also reduce hubness.
STEERABLE PLAYLIST GENERATION BY LEARNING SONG SIMILARITY FROM RADIO STATION PLAYLISTS
François Maillet, Douglas Eck, Guillaume Desjardins, Paul Lamere
This paper presents an approach to generating steerable playlists. They first demonstrate a method for learning song transition probabilities from audio features extracted from songs played in professional radio station playlists and then show that by using this learnt similarity function as a prior, they are able to generate steerable playlists by choosing the next song to play not simply based on that prior, but on a tag cloud that the user is able to manipulate to express the high-level characteristics of the music he wishes to listen to.
- Learn a similarity space from commercial radion staion playlists
- generate steerable playlists
Francois defines a playlist. Data sources: Radio Paradise and Yes.com’s API. 7million tracks,
Problem: They had positive examples but didn’t have an explicit set of negative examples. Chose them at random.
Learning the song space: Trained a binary classifier to determine if a song sequence is real.
Features: Timbre, Rhythmic/dancability, loudness
EVALUATING AND ANALYSING DYNAMIC PLAYLIST GENERATION HEURISTICS USING RADIO LOGS AND FUZZY SET THEORY
Klaas Bosteels, Elias Pampalk, Etienne Kerr
Abstract: In this paper, we analyse and evaluate several heuristics for adding songs to a dynamically generated playlist. We explain how radio logs can be used for evaluating such heuristics, and show that formalizing the heuristics using fuzzy set theory simplifies the analysis. More concretely, we verify previous results by means of a large scale evaluation based on 1.26 million listening patterns extracted from radio logs, and explain why some heuristics perform better than others by analysing their formal definitions and conducting additional evaluations.
- Dynamic playlist generation
- Formalization using fuzzy sets. Sets of accepted songs and sets of rejected songs
- Why last two songs not accepted? To make sure the listener is still paying attention?
- Interesting observation that the thing that matters most is membership in the fuzzy set of rejected songs. Why? Inconsistent skipping behavior.
SMARTER THAN GENIUS? HUMAN EVALUATION OF MUSIC RECOMMENDER SYSTEMS.
Luke Barrington, Reid Oda, Gert Lanckriet
Abstract: Genius is a popular commercial music recommender sys- tem that is based on collaborative filtering of huge amounts of user data. To understand the aspects of music similarity that collaborative filtering can capture, we compare Genius to two canonical music recommender systems: one based purely on artist similarity, the other purely on similarity of acoustic content. We evaluate this comparison with a user study of 185 subjects. Overall, Genius produces the best recommendations. We demonstrate that collaborative filter- ing can actually capture similarities between the acoustic content of songs. However, when evaluators can see the names of the recommended songs and artists, we find that artist similarity can account for the performance of Genius. A system that combines these musical cues could generate music recommendations that are as good as Genius, even when collaborative filtering data is unavailable.
Great talk, lots of things to think about.
Lots of very interesting posters, you can see some of my favorites in this Flickr slide show.
Session Chair: Juan Pablo Bello
SCALABILITY, GENERALITY AND TEMPORAL ASPECTS IN AUTOMATIC RECOGNITION OF PREDOMINANT MUSICAL INSTRUMENTS IN POLYPHONIC MUSIC
By Ferdinand Fuhrmann, Martín Haro, Perfecto Herrera
- Automatic recognition of music instruments
- Polyphonic music
- scale existing methods to higlh ployphonci muci
- generalize in respect to used intstruments
- model temporal information for recognition
- Unifed framework
- Pitched and unpitched …
- (more goals but I couldn’t keep up_
Neat presentation of survey of related work, plotting on simple vs. complex
Ferdinand was going too fast for me (or perhaps jetlag was kicking in), so I include the conclusion from his paper here to summarize the work:
Conclusions: In this paper we addressed three open gaps in automatic recognition of instruments from polyphonic audio. First we showed that by providing extensive, well designed data- sets, statistical models are scalable to commercially avail- able polyphonic music. Second, to account for instrument generality, we presented a consistent methodology for the recognition of 11 pitched and 3 percussive instruments in the main western genres classical, jazz and pop/rock. Fi- nally, we examined the importance and modeling accuracy of temporal characteristics in combination with statistical models. Thereby we showed that modelling the temporal behaviour of raw audio features improves recognition per- formance, even though a detailed modelling is not possible. Results showed an average classification accuracy of 63% and 78% for the pitched and percussive recognition task, respectively. Although no complete system was presented, the developed algorithms could be easily incorporated into a robust recognition tool, able to index unseen data or label query songs according to the instrumentation.
MUSICAL INSTRUMENT RECOGNITION IN POLYPHONIC AUDIO USING SOURCE-FILTER MODEL FOR SOUND SEPARATION
by Toni Heittola, Anssi Klapuri and Tuomas Virtanen
Quick summary: A novel approach to musical instrument recognition in polyphonic audio signals by using a source-filter model and an augmented non-negative matrix factorization algorithm for sound separation. The mixture signal is decomposed into a sum of spectral bases modeled as a product of excitations and filters. The excitations are restricted to harmonic spectra and their fundamental frequencies are estimated in advance using a multipitch estimator, whereas the filters are restricted to have smooth frequency responses by modeling them as a sum of elementary functions on the Mel-frequency scale. The pitch and timbre information are used in organizing individual notes into sound sources. The method is evaluated with polyphonic signals, randomly generated from 19 instrument classes.
Source separation into various sources. Typically uses non-negative matrix factorization. Problem: Each pitch needs its own function leading to many functions. The system overview:
The Examples are very interesting: www.cs.tut.fi/~heittolt/ismir09
HARMONICALLY INFORMED MULTI-PITCH TRACKING
by Zhiyao Duan, Jinyu Han and Bryan Pardo
A novel system for multipitch tracking, i.e. estimate the pitch trajectory of each monophonic source in a mixture of harmonic sounds. Current systems are not robust, since they use local time-frequencies, they tend to generate only short pitch trajectories. This system has two stages: multi-pitch estimation and pitch trajectory formation. In the first stage, they model spectral peaks and non-peak regions to estimate pitches and polyphony in each single frame. In the second stage, pitch trajectories are clustered following some constraints: global timbre consistency, local time-frequency locality.
Here’s the system overview:
Poster madness! Version 2 – even faster this time. I can’t keep up
- Singing Pitch Extraction – Taiwan
- Usability Evaluation of Visualization interfaces for content-based music retrieval – looks really cool! 3D
- Music Paste – concatenating music clipbs based on chroma and rhythm features
- Musical bass-line pattern clustering and its application aduio gener classification
- Detecting cover sets – looks nice – visualization – MTG
- Using Musical Structure to enhance automatic chord transcription –
- Visualizing Musical Structure from performance gesture – motion
- From low-level to song-level percussion descriptors of polyphonic music
- MTG – Query by symbolic example – use a DNA/Blast type approach
- sten – web-based approach to determine the origin of an artist – visualizations
- XML-format for any kind of time related symbolic data
- Erik Schmidt – FPGA feature extraction. MIR for devices
- Accelerating QBH – another hardware solution – 160 times faster
- Learning to control a reverberator using subjective perceptual descriptors – more boomy
- Interactive GTTM Analyzer –
- Estimating the error distribution of a tap sequence without ground Truth – Roger Dannenburg
- Cory McKay – ACE XML – Standard formats for features, metadata, labels and class ontologies
- An efficient multi-resolution spectral transform for music analysis
- Evaluation of multiple F0 estimation and tracking systems
BTW – Oscar informs me that this is not the first ever poster madness – there was one in Barcelona