Duke Listens!: On the stairway to the Celestial Jukebox - a blog by Paul Lamere

If you ever need to explain the Microsoft Playsforsure DRM fiasco to your Mom - this guy can help: The day the music died (via Colin)

I was going to respond to Mark Cohen's post on the Myth of Music Discovery. Based on recent survey data showing that the plurality of people still discovery new music via radio, Marc concludes that music discovery is passive and that downloaded ad-supported music will be the superior vehicle for music discovery. I was going to write about how I thought Marc's conclusions were just a lot of bunk. But luckily David Jennings (of Net, Blogs and Rock 'n' Roll fame) has written a cogent (and less insulting) response. So, instead of writing a response - I'm going to just redirect you to David's: The myth, science and craft of music discovery.

Two weeks from today is the second SanFran MusicTech Summit. The summit is a convergence of musicians, technologists, business and legal folk that are in someway connected with the world of music. The event is organized by Brian Zisk, one of the founders of the Future of Music coalition. This summit is lining up to be even better than the last. Lots of really interesting panelists from companies such as Pandora, Apple, CD Baby, YouTube, SeeqPod, iMeem, Google, Rhapsody, Songza, Gracenote, and Sony, as well as some A-List bloggers including Mike Arringtion (Techcrunch), Pete Crashmore (Mashable), and Om Malick (GigaOm). (See the full speaker list)

I'm lucky enough to be on the agenda to give a short technology demo of Search Inside the Music and Project Aura. I just can't imagine a better-suited audience for this demo - I'm really looking forward to it.

A music visualization based on Processing - Just watch. Solar, with lyrics. on Vimeo

Solar, with lyrics. from flight404 on Vimeo.

Read how this was made.

(Via the aardvark blog recommender)

It looks like Spotify got a little taste of reality. In today's blog post Andreas reports "As part of our ongoing negotiations with rights holders, we have pruned the catalog. We’re sorry if this means your favorite artist is missing. Our long-term goal is to have all the music." Sure enough, a search for The Beatles results in just Beatles' covers, no real Beatles. It is hard to tell how deep the pruning is, but I really hope it wasn't too deep - one of Spotify's biggest value has been their bottomless cup of music. I'm sure Spotify will be working hard to get the music back. (And don't tell anyone, but the Beatles still seem to be there, they've just been removed from the Spotify search index. All my old playlists that had Beatles songs still play just fine.)

Via Spoitfy Blog

Update: Well, it looks like the Beatles are completely gone now. The Beatles are now MIA from all my playslist too. Sigh.

Companies like Google have spent years fighting search engine optimizers that will try to inflate the search rankings in exchange for money. The next wave of this foolishness is upon us - recommender engine optimizers. Companies will take your money in exchange for inflating the playcounts for your tracks. For $747 a company called Tune Boom will increase the playcounts on your MySpace tracks by 300,000. The tuneboom pro site just reeks of sliminess:

More Plays Get You Attention. It's the Number One Factor To Get On The MySpace Charts! While Having Lots Of Friends On MySpace Is Important - It's Only Useful If You Have Targeted a REAL Fan Base - It's Proven That the Very First Information Looked At Are Your Song Plays! It ALL Starts With The Plays! *Important: Don't let fans come to your page and see 20, 30, or 50, plays! People like to be a part of something big and don't want to miss out. When they see a high song play count and see that your song plays are increasing, they will be more likely to click and listen to your music and become friends and fans.

Via TuneBoom Pro Apparently Inflates Artists' MySpace Plays | Listening Post from Wired.com

Brian Zisk and friends have already scheduled a followup to the very successful SanFran MusicTech Summit The summit will be on May 8th, right after the 50th anniversary of NARM (the National Record Merchandising Association). Note that this also happens to be right in the middle of JavaOne week, so if you are geeking out at one conference, you are just a cable car ride away from the other. I'm going to JavaOne, and that will no doubt keep me busy all week, but if I do get tired of hearing about yet another closures language extension for Java, I may try to sneak over to the summit. They've already signed up a good set of speakers for the summit, so it is sure to be an enriching experience.

After years of development, the Echo Nest is online and doing business. Yesterday, they announced that www.thisismyjam.com  is using the Echo Nest to create beat-matched mixes for sharing online.   Here's a 'this is my jam' mix that I made with some of the artists I've been listening to recently:

 

There's lots of neat stuff going on under the hood. They are adjusting song tempos to get a match (I can hear the shift in the song transition from Bjork to Rodrigo Y Gabriela).  When I have a bit more time, I'll be taking a much closer look.  Congrats Brian and Tristan!

Anyone who has worked with a large music collection knows that there are all sorts of difficulties in dealing with the metadata that is associated with the music. The data is often wrong, misspelled, or missing.  Getting the metadata right is a hard problem to solve even with common songs  (should "Hey Jude!"  have an exclamation point or not?) or artists (Is it 'Prodigy' or 'The Prodigy'?). Some artists however seem to go out of their way to make things difficult.  Take our friend Aphex Twin - on his album Windowlicker, track number two is called: 

"ΔM_i⁻¹ = −αΣ_n=1^ND_i[n][Σ_j∈C{i}F_ji[n − 1] + Fext_i[[n⁻¹]]"

I wondered how some of the various Music 2.0 sites were able to handle this track - here are my findings.

First, here's the song in the iTunes store - they don't even try to render it

 

Amazon.com does a bit better, they don't try to render the mathematical characters, but it is readable:

 

iLike falls back on the streetname for the song

 

Google skips the track completely:

As does All Music:

There are some that get it right - MusicBrainz for one:

Our Search Inside the Music research database (curated by Doug Eck) gets it right too, no surprise since we resolve our music against musicbrainz:

 As does last.fm (I think they resolve against MusicBrainz too):

Spotify gets it right too:

 

So it looks like about 1/3 of the sites got it right, and those are the ones that are using MusicBrainz to clean up their Metadata.   This is one of the many reasons why I really like MusicBrainz .

And by the way, this track is also noted for the fact that Aphex Twin has embedded his image in the audio.  An FFT of the track reveals his mug
 

xkcd points out yet another problem with shuffle play.  iPod whiplash indeed.

On her blog, Anita Lillie (master's candidate at MIT's media lab)  has asked for help finding projects and papers about spatially-based organization of digital music collections. I've posted a few comments pointing to ones that I know about - but I'm sure there are more.  If you know of other interesting spatial interfaces to music add a comment here or over at Anita's research blog.  And as an extra credit assignment, use the Apple SDK to port some of these to the iPhone.

 Here are some of my favorites:

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 Hannes Jentche's inteface:

 

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Justin Donaldson's visualization of MyStrands Data

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 Fidg't': Visualizer:

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 PlaySom

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nepTune:

 

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 Musicovery

 

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 Musicream

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 Music Rainbow:

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Electronic Boom

 

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TuneGlue

 

 

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Search Inside the Music

I visited the iTunes music store this weekened.   I was surprised to see that Jeff Buckley's Hallelujah was the number one song.  That certainly was curious since the song has been around for over 14 24 years and Jeff has been gone for over 10.  This morning I visited Amazonmp3 - and there I saw Jeff Buckley's Hallelujah at #3.  Wtf?  Why was a 14 year old song recording suddenly at the top of the charts?  A quick blog search gave the answer.  American Idol contestant (and next teen heart throb, dreads and all) Jason Castro sang "Hallelujah''  last week.  Jason almost seemed to be channeling Buckley with his ethereal singing voice.  American Idol judges Randy and Simon both stated that the Jeff Buckley version of that song was one of their all time favorite songs.  No doubt these comments drove thousands of idol viewers to iTunes and Amazon to check out Jeff Buckley's version of the song.  I am amazed that this little television moment had such an effect on the charts.  It is a good song .. and Jason does a pretty good 2 minute version of the song.

Here's the moment, captured on YouTube.

This week  I zip up to Toronto to take part in a Canadian Music Week panel called 'Recommendation Engines: The ubersolution to Managing Music Ultra Surplus'.  They've put together a great set of panelists:

• Sandy Pearlman - Visiting Scholar, McGill University, Montreal, QC
• Michael McGuire - Research VP, Gartner G2 Media Group, San Jose, CA
• Tom Conrad - Chief Technology, Pandora Media Inc, Oakland
• Robert Gjerdingen - Professor, Northwestern, Evanston
• Paul Lamere - Principle Investigator, Sun Microsystems,
  

The only panelist that I've not met is Dr. Gjerdingen - but I am looking forward to meeting him.  He's the co-author of one of the most cited studies in the Music Information Retrieval academic community that looked at how well humans can classify music into music genres.  Just about every paper on machine classification of music cites this study.

 Here's the panel abstract:

New music discovery will be unlike anything any generation has ever experienced. As more music becomes available online and the long tail gets longer, recommendation engines with improved search filters are going to play a more important role in online commerce. With an aggregate of over one billion song versions now available somewhere online (according to Cache Logic), implementing recommendation engineering navigation for this functionally infinite database seems absolutely necessary. This panel features experts who'll bring you up to date on the latest discovery and recommendation news on what would otherwise prove an ungraspable chaos of music overload.

The panel is on Thursday at 2:40 in the Manitoba room of the  Royal York hotel in Toronto.  Be sure to stop by and say hello if you attend.

It is hard to evaluate music recommendation systems. Current evaluation techniques will often use web mining for artist co-occurrence on web pages or playlists as a way to infer artist similarity to compare against a recommender, or will try to predict a a set of ratings (1 star or 4 star) such as we see with the Netflix prize.  However, these types of evaluations generally don't measure several aspects that are associated with a good recommendation.   For instance,  evaluations that measure how well a recommender system can predict how a user will rate songs are tested against songs that the user has already rated.  This penalizes recommenders that generate good but novel recommendations.  Since the user hasn't rated these novel recommendations yet (since they are novel), the recommender doesn't receive any credit for the recommendations.  A good recommender should recommend novel items, but most recommender evaluations don't evaluate this aspect of recommendation at all.  A recommender that tells you that if you like 'The Beatles' you might like 'The Rolling Stones' may be accurate, and may be evaluated highly, but it is not a great recommender if all it tells you about are artists that you all ready know about.

Probably the best way to evaluate recommenders are with user studies.  Simply ask people how they like they recommendations to rate the recommendations.  However this can skewed results as well.  People will tend to rate recommendations that include many familiar relevant items as better than recommendations that contain a number of unfamiliar items.  Since it can take a good deal of time to evaluate a recommended item (such as a song, artist, movie or book), it is hard to get accurate evaluations for recommendations that contain large numbers of unfamiliar items.

My co-tutorist, Oscar Celma has created a personalized survey for evaluating a number of different types of music recommendation.  Unlike previous evaluations, Oscar's survey recognizes the importance of novel recommendations.  The survey will offer you a number of  music recommendations (based upon your last.fm listening behavior) and ask you questions about the recommendations, including whether or not you've heard the artist or the track before, and to what degree do you like the music.   With this evaluation Oscar can learn which recommenders tend to recommend familiar music, which recommend novel music - as well as which recommenders are recommending relevant music (that is, music that the user will like to listen to).

Oscar has done a good job designing the survey - you can evaluate as many or as few recommendations as you'd like.  The more participants in the survey, the better the results, so I encourage all of my readers to take the survey.  As a reward, tt the end of it all, you may get a few novel recommendations from a state-of-the-art music recommender to expand your music horizons.

Take Oscar Celma's Music Recommendation Survey

Yesterday was the first (hopefully of many) SanFran Music Tech Summit.  This was a gathering of musicians, producers, lawyers, radio heads, and technologists.  The summit was held at the Kabuki Hotel in Japan Town.  Kudos to Brian Zisk and the rest of the organizers for putting this all together. 

I was lucky enough to get to moderate a panel on recommendation and discovery.  The panelists included two technologists that build automated recommendation systems (Michael Troiano - matchmine, CEO and Benjamin Masse - Double V3) and two human recommender systems:  Bill Goldsmith - Radio Paradise, Founder/Coder and Balance - Main Urban Buyer, Rasputin Music.  I thought this was a great mix of panelists.  The human recommenders have a real understanding of what it takes to engage their audience.  Those of us who are trying to build automated recommenders can learn a great deal from these guys.  The panel talked about the characteristics of a good music recommendation.  Some of the observations:

Building trust is extremely important - for human-based recommenders, this trust is built up over a long period of time.  Bill talked about how his listeners over a period of days and weeks grow to trust his taste in music - they know Bill won't lead them too far astray. Similarly, Balance interacts with his customers on a weekly basis, building a relationship over a many weeks and months.  Contrast that with automated systems, Benjamin suggests that they only have 30 seconds or so to gain some level of trust with a web user before the user is ready to click onto another page. 

Bill talked about the ''Tyranny of the Bored'' - where the opinion of people who have nothing to do all day but browse the web, digg stories, tag music, and write reviews have an inordinate amount influence on our taste.  The taste and opinions of busy people, those that don't have time to spend on the social webs is not counted.

They've recorded the panels and have put them online.  The recommendation  panel is here:

 

 

Colin took some good shots of the panelists. 

Balance