Archive for category music information retrieval
Upbeat and Quirky, With a Bit of a Build: Interpretive Repertoires in Creative Music Search
Charlie Inskip, Andy MacFarlane and Pauline Rafferty
ABSTRACT Pre-existing commercial music is widely used to accompany moving images in films, TV commercials and computer games. This process is known as music synchronisation. Professionals are employed by rights holders and film makers to perform creative music searches on large catalogues to find appropriate pieces of music for syn- chronisation. This paper discusses a Discourse Analysis of thirty interview texts related to the process. Coded examples are presented and discussed. Four interpretive re- pertoires are identified: the Musical Repertoire, the Soundtrack Repertoire, the Business Repertoire and the Cultural Repertoire. These ways of talking about music are adopted by all of the community regardless of their interest as Music Owner or Music User.
Music is shown to have multi-variate and sometimes conflicting meanings within this community which are dynamic and negotiated. This is related to a theoretical feedback model of communication and meaning making which proposes that Owners and Users employ their own and shared ways of talking and thinking about music and its context to determine musical meaning. The value to the music information retrieval community is to inform system design from a user information needs perspective.
What Makes Beat Tracking Difficult? A Case Study on Chopin Mazurkas
Peter Grosche, Meinard Müller and Craig Stuart Sapp
ABSTRACT – The automated extraction of tempo and beat information from music recordings is a challenging task. Especially in the case of expressive performances, current beat tracking approaches still have significant problems to accurately capture local tempo deviations and beat positions. In this paper, we introduce a novel evaluation framework for detecting critical passages in a piece of music that are prone to tracking errors. Our idea is to look for consistencies in the beat tracking results over multiple performances of the same underlying piece. As another contribution, we further classify the critical passages by specifying musical properties of certain beats that frequently evoke trac ing errors. Finally, considering three conceptually different beat tracking procedures, we conduct a case study on the basis of a challenging test set that consists of a variety of piano performances of Chopin Mazurkas. Our experimental results not only make the limitations of state-of-the-art beat trackers explicit but also deepens the understanding of the underlying music material.
An Audio Processing Library for MIR Application Development in Flash
Jeffrey Scott, Raymond Migneco, Brandon Morton, Christian M. Hahn, Paul Diefenbach and Youngmoo E. Kim
The Audio processing Library for Flash affords music-IR researchers the opportunity to generate rich, interactive, real-time music-IR driven applications. The various lev-els of complexity and control as well as the capability to execute analysis and synthesis simultaneously provide a means to generate unique programs that integrate content based retrieval of audio features. We have demonstrated the versatility and usefulness of ALF through the variety of applications described in this paper. As interest in mu sic driven applications intensifies, it is our goal to enable the community of developers and researchers in music-IR and related fields to generate interactive web-based media.
Music21: A Toolkit for Computer-Aided Musicology and Symbolic Music Data
Michael Scott Cuthbert and Christopher Ariza
ABSTRACT – Music21 is an object-oriented toolkit for analyzing, searching, and transforming music in symbolic (score- based) forms. The modular approach of the project allows musicians and researchers to write simple scripts rapidly and reuse them in other projects. The toolkit aims to pro- vide powerful software tools integrated with sophisticated musical knowledge to both musicians with little pro- gramming experience (especially musicologists) and to programmers with only modest music theory skills.
Music21 looks to be a pretty neat toolkit for analyzing and manipulating symbolic music. It’s like Echo Nest Remix for MIDI. The blog has lots more info: music21 blog. You can get the toolkit here: music21
State of the Art Report: Audio-Based Music Structure Analysis
Jouni Paulus, Meinard Müller and Anssi Klapuri
ABSTRACT – Humans tend to organize perceived information into hierarchies and structures, a principle that also applies to music. Even musically untrained listeners unconsciously analyze and segment music with regard to various musical aspects, for example, identifying recurrent themes or detecting temporal boundaries between contrasting musical parts. This paper gives an overview of state-of-the- art methods for computational music structure analysis, where the general goal is to divide an audio recording into temporal segments corresponding to musical parts and to group these segments into musically meaningful categories. There are many different criteria for segmenting and structuring music audio. In particular, one can identify three conceptually different approaches, which we refer to as repetition-based, novelty-based, and homogeneity- based approaches. Furthermore, one has to account for different musical dimensions such as melody, harmony, rhythm, and timbre. In our state-of-the-art report, we address these different issues in the context of music structure analysis, while discussing and categorizing the most relevant and recent articles in this field.
This presentation is an overview of the music structure analysis problem, and the methods proposed for solving it. The methods have been divided into three categories: novelty-based approaches, homogeneity-based approaches, and repetition-based approaches. The comparison of different methods has been problematic because of the differring goals, but current evaluations suggest that none of the approaches is clearly superior at this time, and that there is still room for considerable improvements.
Notes from the ISMIR business meeting – this is a meeting with the board of ISMIR.
- President: J. Stephen Downie, University of Illinois at Urbana-Champaign, USA
- Treasurer: George Tzanetakis, University of Victoria, Canada
- Secretary: Jin Ha Lee, University of Illinois at Urbana-Champaign, USA
- President-elect: Tim Crawford, Goldsmiths College, University of London, UK
- Member-at-large: Doug Eck, University of Montreal, Canada
- Member-at-large: Masataka Goto, National Institute of Advanced Industrial Science and Technology, Japan
- Member-at-large: Meinard Mueller, Max-Planck-Institut für Informatik, Germany
Stephen reviewed the roles of the various officers and duties of the various committees. He reminded us that one does not need to be on the board to serve on a subcommittee.
- website redesign
- Other communities hardly know about ISMIR. Want to help other communities be aware of our research. One way is to make more links to other communities. Entering committees in other communities.
Hosting Issue – will formalize documentation, location planning, site selection.
Name change? There was a nifty debate around the meaning of ISMIR. There was a proposal to change it to ‘International Society for Music Informatics Research’. I recommend, given Doug’s comments about Youtube from this morning that we change the name to: ‘ International Society for Movie Informatics Research’
Review Process: Good discussion about the review process – we want paper bidding and double-blind reviews. Helps avoid gender bias:
Doug snuck in the secret word ‘youtube’ too, just for those hanging out on IRC.
- Douglas Eck (Google)
- Greg Mead (Musicmetric)
- Martin Roth (RjDj)
- Ricardo Tarrasch (Meemix)
- moderator: Rebecca Fiebrink (Princeton)
- rjdj – music making apps on devices like iphones
- musicmetric tracks 3 areas: Social networks, network analysis (influential fans), text via focused crawlers, p2p networks
- memix – music recommendation, artist radio, artist similarity, playlists. Pandora-like human analysis on 150K songs – then they learn these tags with machine learning. Look at which features best predict the tags. Important question is ‘what is important for the listeners’. Their aim is to find best parameters for taste prediction.
- google – goal is organize the world’s information. Doug would like to see an open API for companies to collaborate
Rebecca is the moderator.
What do you think is the next big thing? How is tech going to change things in the near future?
- Doug (Google) thinks that ‘music recommendation is solved’ – he’s excited about the cellphone. Also excited about programs like chuck to make it easier for people to create music (nice pandering to the moderator, doug!)
- Ricardo (MeeMix) – the laid back position is the future – reach the specific taste of a user. Personalized advertisements.
- Greg (MusicMetric) – Cloudbased services will help us understand what people want which will yield to playlisting, recommendation, novel players.
- Martin (RjDJ) – Thinks that the phone is really exciting – having all this power in the phone lets you do neat thing. He’s excited about how people will be able to create music – using sensory inputs, ambient audio.
How will tech revolutionize music?
- Doug – being able to collaborate with Arcade Fire on online
- Martin – musically illiterate should be able to make music
- Ricardo – we can help new artists reach the right fans
- Greg – services for helping artists, merchandising, ticket sales etc.
What are the most interesting problems or technical questions?
- Greg – interested in understanding the behavior of the fans. Especially by those on P2P networks. Huge amount of geographic-specific listener data
- Ricardo – more research around taste and recommendation
- Doug – a rant – he had a paper rejected because the paper had something to do with music generation.
- Rebecca – has a MIR for music google group :MIR4Music
- Martin – engineering:increase performance in portable devices – research:how to extract music features from music cheaply
- Ricardo – drumming style is hard to extract – but actually not that important for taste prediction
How would you characterize the relationship between biz and academia
- Greg – there is lots of ‘advanced research’ in academia, while in industry there look at much more applied problems
- Doug – suggests that the leader of an academic lab is key to bridging the gap between biz and academia. Grad students should be active in looking for the internships in industry to get a better understanding of what is needed in industry. It is all about getting grad students jobs in industry.
- what tools can we create to help producers of music? – Answer: Youtube. Martin talks about understanding how people use music creation tools. Doug: “Don’t build things that people don’t want.” – to do this you need to try this on real data.
Hmmm … only one audience q/a. sigh …
Good panel, lots of interesting ideas. Here is the future of music: