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Ismir 2009 Music Industry Panel

October 29, 2009


The 2009 Music Industry Panel has just kicked off, and since Paul Lamere is on the panel, he won’t be able to blog or twitter about the events like he has so far… so I figured maybe I should do a brief write up instead.

The panel is comprised of:

  1. Paul Lamere – The Echo Nest
  2. Tom Butcher – Microsoft
  3. Peter (?) – Gracenote
  4. Oscar Celma – Barcelona Music and Audio Technologies
  5. Norman Casagrande –
  6. Keiichiro Hoashi – KDDI R&D Laboratories
  7. Kunio Kashino – NTT Laboratories
  8. Malcolm Slaney – Yahoo

Paul is acting as an ad hoc moderator, and started off asking:  What are the tough problems that people in the industry are facing right now?

Tom :  We’re actively trying to grow our music market, and come up with recommendation engines that operate at large scales.  Overcoming cold start problems, and evaluating user assessments in a general way is difficult.

Peter : We  have special challenges as a b2b company, since we have a lot of external constraints.  We have to cater to the needs of the customers, and accommodate what they want to pay for.

Oscar : In our case, we have a lot of practical problems:  metadata cleaning, etc.  I’m interested in semantic music descriptions, and music discovery in the long tail (less popular music).  We have clients & music from smaller markets like Turkey.

Norman : Metadata is one of our biggest problems as well. We try to leverage our users to correct some of the metadata, but you cannot rely on this.  We also face scalability data, in the face of real-time data needs for our users.  We have to be able to allow users to build a profile, and then provide recommendations immediately.  We also focus on understanding user “types” and general listening behaviors.  We also face the limitations that the record labels impose on us.  We are provided a pool of tracks (several thousand), and then we apply some user listening behavior metrics on this data.  Then, once we have a collection of candidate tracks (from the label), and the label restricts which songs can be played where.  We end up filtering a lot of the music out because of the limitations of how we can match against their catalog. Evaluation is also really important, but AB test can be tricky… it is easy to become too biased, and it’s important to design user tests correctly.

Peter : There is a lot of weird grey areas here, you can’t put cover art in embedded devices for instance.  The music publishers have the right to lyrics, which we have to explicitly clear with the publisher, and sometimes they are too slow, or not willing to clear rights in certain cases.

Keiichiro : We do our own music distribution service, and we are trying to compete with the “giants” such as iTunes, to increase sales and preserve the rights of the artists.  On the research side, we are trying to convince the music people to know that MIR is a “good thing”.  Our service has a lot of MIR functionality, but most of the services are based on conventional search.  My research is on more discovery search, but many business people are not convinced that you can present unknown songs to the users as recommendations.

Kunio : In Japanese language, there is many different ways of writing the titles of different songs.  So, we can’t always identify music based on simple labels.  We also have difficulties with dealing with all the different variations in recordings, and deciding if the recordings are actually the same.  We are actively researching this.

Malcolm : We don’t have a big problem scaling, but we have a problem deciding on how to provide the right content to the right user.  On average we get 2.2 words per query… so we have only 2 words to figure out what the user wants… Madonna the music star?  Madonna the painting?  etc.

Paul : If you look at mirex as where people are spending their time, and we see classification of genre, artist, etc… and then we look at the companies actually doing something with music (apple & itunes, guitar hero, google music, pandora), they don’t tend to use these techniques.  For the panel, the question is… why do you think that is?  Has the industry not caught up, or is research in the wrong area.

Malcolm : None of my people are begging for these technologies

Tom : We are not doing content analysis yet, it’s hard to do currently with our dataset.  When we launched the collaborative filtering algorithm, we had a lot of good data for that.  The product people don’t come to us and ask about content analysis technology, they want to know how to launch a service in Germany.  I disagree with the fact that people aren’t using MIR techniques in the industry (SongSmith).  It takes someone that knows about the research to able to see the solutions and opportunity for MIR techniques.

Keiichiro : There were two concerns for using MIR techniques:  The cost of performing content analysis, and the fact that we didn’t have the rights for songs.  We had to ask record labels and the artists if they agreed to allow us to analyze/use their content.

Oscar : I don’t think the mirex tasks need to be useful for the industry, maybe in 10 years it will prove useful or will be mature.

Peter : In all of these situations, industry comes down to a question of profitability.  The customers will only buy if they’re convinced of profitability.  Apple made decisions on recommendation technology, and the decision was cost based on the incremental sales it would provide.  Also, number one, everyone is working on new things.  The first is that different combined techniques are being considered over individual approaches.  The second is to ensure that the solutions work very well globally.  It can’t just work for certain types of music.  You can’t exclude yourself from certain international markets.

Tom : You mentioned Harmonix (Guitar Hero).  The technology in these games has changed how we consume music.  Not only are they fun, but the music in these games is monetized and consumed in new ways.  It’s more interactive, so you can charge more for it.  That’s a good way to make money.

Paul :  A nice thing about the industry is that we have more direct contact with users.  There’s not as much research around taking advantage of  user data.  What kinds of data do you have that you would want to share with researchers.

Malcolm :  I think AOL proved that giving out user data is a firable offense.  We give out CF data that has been thoroughly scrubbed.  We have a rating database available of 700 million rating data.

Norman : We have an API for a lot of data.  But it’s also true that I’m not a user of this API.  Quite often, we’re not offering something that’s not useful to researchers.  For instance, we only return the last 200 tracks for a user.  It used to be a practical limitation, but not necessarily anymore.  We are willing to negotiate directly with researchers if they require more information.

Tom : What Netflix did with their rating data really opened the door.  The contest really drove innovation in the field.  The other thing I’d like to mention is that Microsoft has a great internship program with the research labs.  In that setting you have access to all the data you want.

Paul : We’ll open up from questions from the audience

Question : What kinds of skills are you looking for?

Malcolm : I interview a lot of people.  From the research jobs, I’m looking for someone who can teach me something new.  For the engineering jobs, I want to see that you can build something.  show your passion.  Show something nobody has done before.

Oscar : We are looking for music lovers.  We use typical tools like C++ , but we mainly need people with open minds who are willing to try new things

Keiichiro : We need people that can think of things that corporate people don’t.  However, we only hire people who speak Japanese… so that might disinterest most here (it’s a small problem).

Norman : When we are hiring at, we are looking for people that have previous experience with small toy datasets.  Something that shows passion, etc.  Our team is very small, so the person we want is someone that can do a lot of coding, research, and development.  Also ping-pong skills are important.

Peter : The people that work in Markus’s team have to be Jacks of all trades.  We need people that can build complete systems.  People with motivation and drive.

Paul : When I was at Sun Microsystems, we were in a feud with Microsoft.  It never ended to surprise me how many resumes I got in Microsoft Word.  Paying attention to the company you are applying to makes a lot of difference :)  The people that we hire we tend to invite first.  What that translates to is that we are looking for people already doing stuff in their spare time, and making it public somehow.  That is a good indicator that they have a lot of passion.

Kunio : We are looking for people with a vision of technology and services.  The successful students are expected to have their own core technologies, and an ability to express their visions.

Question : I’d like to ask you about query by humming technologies, as well as illegal downloading issues.

Tom : I haven’t seen any commercial applications of this (query by humming).  It’s not in the works for our mobile services.

Keiichiro : We actually have a query by humming service.  If anyone can beat our service, we would be interested.

Paul : In the west, there doesn’t seem to be much interest in query by humming.  Is there more interest in Japan?

Keiichiro : I’m not sure how much it is actually being used, but we like to try a number of approaches.

Peter : We don’t have a working example, but it is something we’re interested in.  As the technology improves, we will start considering it more strongly.

Question : What are the differences between the use of symbolic data and content data?

Malcolm : I think symbolic data means text to us.

Paul : We use all of the data we can.

Norman : One of the biggest challenge we face is how to combine those different sources.  It’s hard to figure out what is best.  Which one do you trust?  What are the priorities.

Question : Are you scared of Google’s music service?

Malcolm : We already compete with them.

Oscar : We are looking forward to crawling their data.

Norman : We are interested in stealing their engineers.

Tom : I think Apple is really the big player in music.  Google music is based in iLike, which was started in Seattle.  If you’re feeling entrepenurial, that’s a great example of how to make some money.

Paul : I like what Google is doing.  If you search for a banner there, there’s music links in there, which lets you listen once or twice to a given track.  Some of the PR said that people went to Google first for “discovery”.  I don’t think that anyone will actually find anything new given how Google works.

Question : What are you all excited about these days?

Tom : From my experience, what we are looking for is ways to augment the way they consume music.  We don’t own the content, we don’t generate the content.  Any revenue we generate is around the services we build around consumption.

Keiichiro : I agree with Tom.  When the music service started at my company, nobody knew how people were really going to use music (as an alarm clock, or as an e-mail notification, etc.).  We are looking for new experiences and ideas (like our radio example in the demo session).

Paul : One thing I’m really excited about is services like Spotify’s new API.  As Spotify spreads its domains, other companies might do so as well.  I think labels might realize that music can flow more freely, and they might relax their copyright control.  That will lead to a lot of new ways of interacting and consuming music.

Question : I’m interested in music creation/composition.  What can you say about supporting tools for music creation?  What do you think is the next generation of MIR technologies useful for composition?

Tom : We don’t represent the manufacturers of music creation software.  We had this application called SongSmith.  You sing unaccompanied into a microphone, and the application generates the accompaniment.  That idea was generated by one of the developers.

Oscar : The Yamaha Vocaloid technology came out of MTG at UPF, which we spun off of.

Question : How good does a technology need to be to make it as a commercial product?  What is your criteria for quality?

Tom : That’s a good question.  However, it doesn’t matter.  The technology could be good or bad, but if it satisfies a consumer need, then it’s “good enough”.

Malcolm : At the high level I agree, but at the low level, we just look at traffic on our website.

Question : A lot of you to one extent or another have been following this conference for some time.  What do you think is a highlight from this conference that has helped at your company?

Oscar : I’m interested in merging content with context, which has been presented here.

Malcolm : We’ve come a long way since using simple MFCC features… but it’s hard to point out a single example.

Norman : The MIR has been building up datasets and tools over time, so bits and pieces come from many places.

Paul : All of the work on tags has been very useful.

Question : Do you work with Non-western music?  Do you have specific problems with that?

Norman : We are in that situation.  We have problems with label changes due to languages.  Those are issues we have to deal with.

Keiichiro : Most of the music we work with is Non-western.  We had some cases when we worked with ringtones.  Some are not music at all.  That messes up the music features we were expecting.  If metadata was present, we could solve that problem.

Norman : There is also an issue with bias.  The majority of our users are in a few countries.  When you compute similarities, you are biasing towards the cultures there.

Peter : We put a lot of effort in getting to a baseline performance of good coverage, and good support of local cultural sensibilities.  We have over 2000 genres, and we try to use local experts for editorializing in certain contexts so that everything is consistent to the region.  This also applies for mood data and classifier.  We try to do our best to make sure the training data has information from all those places in the world.  There is a long ways to go still.  We spend a lot of time in how to map classes and categories to labels and terminologies appropriate for different markets.

Malcolm : Yahoo’s goal is to organize all the world’s information.  Most of the revenue and users come from bigger countries, so it’s doubly hard to work elsewhere.

Paul : We talked about long term, grand challenges earlier.  What are the big problems being faced in the next 5 years?

Malcolm : Maybe give us 10 years.  What our product people are asking for is understanding content.  We want to understand user content.  We want to be able to identify music embedded in other content (music in videos).  How do we match music in other media?

Norman : One of the big problems is finding the “little gems” in the long tail.  Some of the popular music deserves to be less so.  From a research perspective, coming up with a better qualitative measurement would be really cool.  I don’t think there’s a generic value for quality, so it should be based on user interest.  I don’t think the quality of the content based recommendation is there yet.  I also like analysis of sub-song quality… identify smaller parts of songs that people really like.

Oscar : We are interested in understanding the user better.  We’d really like to improve that.

Peter : I also have user profiling and personalization at the top of my list.  There’s been some great advances, but it would be nice to have a solid personality profile for people, that would enable a better understanding of what people like, etc.  Navigation and visualization is also needed, and there are a lot of unsolved problems in this domain, especially in mobile devices, cars, etc… not only to look cool, but to actually be usable.

Tom : I’d like to add to that.  I’d like a way to bring things to customers, without them having to ask for it.  The more the user has to ask for, the more chance there is for frustration.  We think about recommendation experience as a single modality.  It may not fit in all situations, but closing the gap on user expectations and needs is important.

Malcolm : In the real world, you don’t get much information from the user.  We need more technologies that need less data to drive recommendations.

Question : How do you feel about interoperability of the different services?

Norman : I think it’s awesome.  As long as our users are using it, and are aware of and provide new data, then it’s great.

Paul : There are some need for standards.  Some standards are proposed, but businesses have been slow to adopt.  I think there may be some interesting things come down the line there.

Tom : I’m very disappointed with my company’s stance on this issue.  We are a closed model like Apple.  I’d like our group to take a leadership role in this area.

Question : Do you know a technology that was good enough on its own without any marketing?

Paul : Shazam was an example like that

Malcolm : Most of Google’s stuff is like that

From → Music, Recommendation

  1. Elias permalink

    Thanks a lot for taking and publishing these notes!

  2. Thanks Elias, we missed you this year.

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  1. ISMIR 2009 – The Industry Panel « Music Machinery

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