MusicBrainz

MusicBrainz is a great start. The idea is to uniquely mark (I would use the term "fingerprint" but ...) individual recordings. Let that sink in and think about what you could do if you could unambiguously say that you had just listened to x, or that you liked x. First off, it makes a project like MoodMusic easier and better (especially the collaborative filtering part), but also it makes Fisher's plan easier to implement. And, a lot more. [via Archipelago].

Lincoff's Music Plan

As Copyfighter also writes, Bennett Lincoff's music licensing plan is stuck in the past. Copyfighter points out that Lincoff still believes in the old distribution model (from trusted -- read "big" -- sources) and I would add that Lincoff is also still trying to remunerate musicians for copying, rather than listening and using. Lincoff writes: "Royalty distribution would be based on a full census of licensed transmissions." Why transmissions? I get no value from "copying" music or music "transmissions," so why should artists be remunerated on that basis. , Why not base pay on what we do with music: listen, mix, and derive new music.

If we are reforming copyright and looking for a fair way to recompense artists, then there is no reason to dogmatically stick to the old rules.

Promises to Keep

Just finished my first read of the Introduction to Professor Fisher's new book "Promises to Keep: Technology, Law, and the Future of
Entertainment" [via John Palfrey] It restates some of the arguments Professor Fisher previously made at The Future of Music Policy Summit and which I discussed earlier and even earlier. It also outlines the monstrous assignment Professor Fisher has given himself.

Introduction

Chapter 1: The Promise of the New Technology

Chapter 2: The Baseline: Entertainment Law and Practice in 1990

Chapter 3: What Went Awry

Chapter 4: Taking Property Rights Seriously

Chapter 5: Online Entertainment as a Regulated Industry

Chapter 6: An Alternative Compensation System

Conclusion

Seems like a good way to go about the analysis and I love the title. Can't wait to read the rest.

Fisher's Price Continued

Seth has replied to Ed's post on Fisher's watermark music payment proposal (more on that in my original post).

Here I want to take issue with Seth's question: whether Fisher has suggested the "dreaded pay-per-view society." To which I would answer, not necessarily. As I understand Fisher's suggestion, money is collected as a tax/license on media or other digital music associated goods (bandwidth etc) and then is allocated based on use. So I pay what amounts to a fee for use of bandwidth or other goods and then the money I pay gets distributed (along with everyone else's) based on my (and everyone else's) listening etc. to the music. Artists are paid-per-view (although sampling may be more appropriate here and I don't know which Fisher proposes) but I pay-per-related-good. Some uses will in fact be pay-per-use (streaming if bandwidth is taxed and metered for example) but many will be flat rate (listening to an mp3 on a taxed hard drive for example).

Fisher's Price

Terry Fisher's proposed solution to the problem of music piracy has been picked up on Copyfight and commented on by Seth Finkelstein among others.

One of the main questions is, how will the compulsory license revenues be distributed. In Fisher's model, there will be some sort of counting done. Since I have been working on a voluntary version of this for some time in order to help me keep track of what I listen to and listen more often to tunes I don't hear as much, I can certainly attest to how difficult it is to do rigorously. So, Seth's point is certainly well taken. The devil is in the details and 100% measurement sounds Fritzian, (or Orwellian). However, on many platforms it is relatively easy -- hey if I can code it, it must be easy. Particularly if you don't have to worry about cheating. So, if we aren't worried about being accurate, but only about sampling and estimating, I think it can be pretty easily done (see Neilson's).

Update: Ed Felten has picked up the thread with a similar but different post which takes into account why people might want to cheat.

Update 2: Seth has replied to Ed's post. Seth has more nits to pick, writing:

But even if there was perfect sampling, I think there's a major obstacle in making the money numbers work. ... I'd really like to see some numbers attached to the idea. These are the sorts of grubbly details that tend to sink appealing speculations.

See also my comments on Seth's reply.

New Way

Phish is trying a new way of distributing music for their most recent tour. At Live Phish you can buy MP3s and SHNs from the tour and receive them by download two days after each show. No DRM. Interesting experiment, especially since Phish allows taping of their shows. I just downloaded the New Year's Eve show.

From their FAQ:

Why should I pay for what I can get for free?

Primary advantages of Live Phish Downloads over audience recordings are: pristine soundboard quality, faster and more reliable downloading and quick turnaround. In most cases, shows will be available for download within forty-eight hours. Each show is carefully indexed and comes with printable booklets (containing liner notes), tray inlays and CD labels. The files are served by a robust delivery network capable of serving thousands of simultaneous downloads, ensuring the fastest and most reliable download experience for the user. We also offer quick response customer service to help sort out any problems that may arise. That being said, audience taping has always been and will continue to be a defining element of the Phish experience. Live Phish Downloads offers an alternative, convenient route to obtaining live recordings and is in no way intended to supplant or undermine the taping community.

Rating Systems

Glenn Fleishman (of GlennLog) posts an interesting summary of his discussion with Howard Rheingold about Intentional or Implicit Rating. This is something near and dear to my heart as well, though I dream of the type of rating by action, I think that once we have the AI required to get there (and correctly interpret my actions) we may already have the AI required to correctly guess my actions and figure out ratings from my previous (and unrelated) information dump to the AI. Of course another response is that there is a third way (what GrepMoodMusic uses) that takes intentional ratings and then uses knowledge about those ratings to make a few of them go a long way.

Bob Dylan's Law

By way of the great How Appealing Web Log is a New Yorker article on Professor Michael Perlin's quest to understand the law of Bob Dylan.

Sharing in the Groove

Interesting web log entries from Scott Andrew LePera and Shannon Campbell. They are both musicians but didn't know each other until Scott downloaded one of Shannon's mp3s and added backing to it in the form of bass, harmony vocals, drums and some reverb. He then e-mailed it to Shannon and got her reaction. She loves it and Scott has posted the final product. Surely this is what the RIAA is afraid of when people "steal" mp3s.

GrepMoodMusic Screenshots

Here are some screenshots of GrepMoodMusic that will let me talk a little about functionality before I get more actual documents up (such as source code).

First off, the object of GrepMoodMusic is to manage a large music collection and enable playlists to be automatically generated based on a person's mood, how much the person likes different recordings and whether the person has recently listened to particular recordings. The other goal of the system is to do all of this with as little human rating of recordings as possible.

This is the main interface to GrepMoodMusic. GrepMoodMusic is a combination of several layers of functionality. The database layer (not pictured) stores all the information relating to the recordings, ratings and listenings. The database access layer (not pictured) performs the appropriate queries on the database and returns objects. The interface layer and the layout/format layer (pictured here) displays the information for a user and allows for streaming of the music.
The database and database access layer are the base of the system which can be used with many different interfaces, such as SliMP3, web, winamp, shoutcast or portable device, such as the diamond rio.

Storing information in a normalized relational database with indexes makes retrieving the data easy. There are indexes for genres, artists (pictured here), albums and songs. Each index can be used to look up recordings, stream recordings or shuffle and stream recordings. (Yes, I know the color scheme is horrible).

This shot shows the session specification. The listening user is "macgill", the sample rate is "medium" and the mood is "Wake Up". Everything in GrepMoodMusic is keyed to individual users. It is currently a personal jukebox though I would like to add the ability to have small groups listen and their ratings be combined. It can resample (the code was borrowed from Apache::MP3 as were many of the ideas and the inspiration for this program). Finally, and most importantly, the mood is "Wake Up". Moods can be defined by the user and can be anything the user chooses. Mine are "Wake Up," "Work," "Party," "Mellow" and "Snooze."

This is the Recent Tunes box, which shows the recent tunes played (the last three were part of the randomly generated "Wake Up" setlist that GrepMoodMusic created on the fly based on my preferences. Each of those were rated highly in that mood. This is not always the case. GrepMoodMusic makes educated guesses based on what it knows about the ratings of songs on the same album, by the same artist or of the same genre (it also discounts songs that were recently listened to often). I would like to add tempo to the mix as well but haven't found a good tempo calculator that works in batch mode.

My current to dos are:

1. Squash the remaining little bugs


2. Clean up the code


3. Make an installer


4. Release it


5. Write some external docs


6. Make improvements including:




• Multiple concurent users
  
• Other interfaces
  
• Editing MP3 Tags
  
• XML feeds and interactions