We’ve added some new items in our Artist Instructions pages, and updated some of the existing items with simpler steps to get things done. If you’re an artist using SNOCAP, check it out!

SNOCAP Opt-Tunz is a service that monitors and enables varying business models between content rights holders and content distributors. Opt-Tunz identifies music and provides information about how that content can be used by Distributors. When Rights Holders participate in the business model facilitated by Opt-Tunz, they generate revenue from the Distributor based on the amount of their content that is consumed.

Read more about Opt-Tunz in this whitepaper.

Thanks to our partnerships with CD Baby and MySpace, SNOCAP MyStores have been created automatically for CD Baby artists and are ready to be integrated on artists’ MySpace pages. If you are a CD Baby artist and haven’t already integrated your MyStore on your MySpace page, here are the instructions for doing so.

MyStore is about giving artists the freedom to promote themselves. One of the things that makes MyStore unique is that it puts ecommerce in context—you don’t need to leave the page you’re on to buy the music you want. When you combine this with the viral features of the storefront, you have something that capitalizes on social network dynamics and conforms to the social norms of these networks. To take full advantage of these viral capabilities, we’ve introduced an open API that lets web developers create mashups that combine MyStores and their own web content. For example, a web site may wish to add MyStores on pages that discuss a particular artist or song, allowing users to sample, purchase and download relevant tracks.

The MyStore Search API accepts artist names and/or track titles passed as URL parameters and returns results as an object in javascript object notation (JSON). The results include MyStore IDs that can be used to render MyStore storefronts. The API is documented here.

We’ve added a new entry to our BETA Software page! The MyStore Search interface allows you to search for music available for purchase on MyStores. You supply artist name and/or track titles and get a list of MyStores that match. As with all MyStores, you can buy the tracks directly where the stores appear.

Do social networks humanize content ownership and deter unsanctioned distribution?

As more and more artists register their tracks with SNOCAP and begin selling unencrypted audio files on social networks like MySpace, questions arise about whether that content will be redistributed against the wishes of the rights holders. We believe that while this possibility exists, the personal connection created by social network interaction will significantly affect the way people treat this content. We believe that people are less likely to redistribute copyrighted material when they purchase it from an artist’s profile page on a social network versus when they get it of an anonymous peer to peer network. Leading psychology researchers in the field of social responses to communication technology concur, and there’s an interesting body of research to support this.

Social networks have created an environment where individuals connect to other individuals, whether those individuals are real, or facsimiles managed by representative entities for promotional or commercial purposes. In either case, the social model created by these systems is one where the representation of the individual on the network is the individual, and interactions with that representation are done understanding that the recipient will know their source. The reciprocity afforded by these systems impacts the social dynamic—anything you say about an individual can be rebutted or commented on by that individual or friends of that individual. By removing anonymity from the communication (although false identities abound) and making the interaction mode one to one, participant behavior is affected.

Peer to peer file sharing networks offer a different sort of social dynamic. While some networks allow individual users to connect to other individuals and groups with similar interests, the activity of sharing and obtaining content is generally anonymous. When you search for and download a shared file, you don’t know who it’s coming from—in fact it’s probably coming from a group of anonymous users.

We believe that the different interaction models of social networks and anonymous file sharing networks affect the way users behave with respect to the unauthorized distribution and consumption of content. Users presented with the opportunity to purchase unencrypted digital content from an artist on a social network are less likely to distribute that content against the wishes of the artist as they might if that content were obtained on an anonymous file sharing network. We are presently working in conjunction with Stanford psychology professor Clifford Nass to review past research and are planning to conduct new studies to further explore human behavior surrounding these issues. We’ll post our findings here on SNOCAP Labs.

SNOCAP uses acoustic fingerprinting technology to identify audio content submitted for registration. In this article, I explain a little about acoustic fingerprinting, how it differs from other methods of content identification, and why this level of identification is important for SNOCAP and the integrity of our open registry.

First, it’s important to know exactly what is represented in the SNOCAP Registry. The Registry contains three types of information: 1) sound recordings, as embodied by audio files, 2) rights holder information, describing the artists and labels who own sound recording rights, and 3) distribution terms, describing how the rights holders want to make their audio content available. The SNOCAP Registry is described as an “open registry”, meaning any rights holder is free to create a SNOCAP account (providing the rights holder information) and register tracks (providing the audio files representing sound recordings for which they own or control rights). To prevent misrepresentation of rights ownership, it is important to compare newly submitted content against content that has already been registered. This is where the content identification technology comes into play. We have to make the comparison based on acoustic properties of the file submitted, since it is the sound recording that is being registered, not just a specific file containing the sound recording.

If audio files were all created the same way and could not be altered without significantly degrading their quality, perhaps acoustic fingerprinting would not be necessary. Files could be identified by using a standard hashing algorithm based on the bits in the file. But there are many ways to create a digital audio file and the tools and parameters available for doing so generate files that contain different bits, but sound more or less the same to the human ear. So, standard hashing techniques are inadequate to serve SNOCAP’s content identification needs.

Audio file formats contain the ability to add metadata describing the file contents. If this metadata were always accurate and could not be changed, then perhaps this information would be sufficient to identify a particular sound recording. But metadata fields are easy to change, and the tools and services that supply the metadata (based on track information ripped off a CD and looked up in a database, for example) vary. Metadata is certainly not sufficient when making definitive claims about copyright ownership.

Acoustic fingerprinting is like the automated equivalent of having a person listen to all the audio files submitted to SNOCAP and remembering which ones have been heard before. Just like a human, the fingerprinting system must accommodate noise or variations in sound quality, or even versions that have been shortened by eliminating portions of the song. To accomplish this, the fingerprinting system slices the audio file into short frames, and for each frame calculates information about the acoustic features contained within. When this data is reduced into numerical values, it can be compared to other data frames in the system. If enough of the frames from a new track match a previously registered track, we determine that it is sufficiently similar to constitute the same sound recording.

Some acoustic fingerprinting systems only analyze a small subset of a track against a database of fingerprints. This approach is fine if you only want reasonable assurance that the track in question is contained in your database (to look up track metadata or get recommendations for other, related music, for example). But when validating representations of ownership, it is necessary to analyze the entire file and compare all analyzed segments against the database. This is a computationally intensive process, but it is necessary for our purposes. Additionally, the acoustic fingerprinting technology we use is built to scale linearly, so that lookup time is constant no matter how many fingerprints are in the system.

There is another use of acoustic fingerprinting technology in the SNOCAP system. SNOCAP provides services that identify user-submitted content or content made available by individual users on a file sharing network. In these cases, it is necessary to obtain an acoustic fingerprint for the files in question and use this information as a key to look up ownership and distribution rights specified in the SNOCAP Registry. When dealing with user submitted content, the effectiveness of the system in matching altered versions of audio files against the registered masters is paramount. And to scale to the volumes associated with such systems, it is critical that fingerprints can be generated efficiently on the client systems and used for lookup purposes.

The ability to reliably identify content is central to the integrity of the SNOCAP Registry. All retailer and consumer-facing offerings enabled by SNOCAP rely on this capability. This solution must work in the case where masters submitted by rights holders are compared to previously registered content, and also the case where user-submitted and shared content is identified and the resulting fingerprint used to look up corresponding distribution rights. The acoustic fingerprinting technology employed by SNOCAP was selected based on its ability to perform in these cases and do so in an efficient and scalable manner.

See also: http://ismir2002.ismir.net/proceedings/02-FP04-2.pdf, a description of the acoustic fingerprinting algorithms that form the basis of the SNOCAP content identification system.

SNOCAP Labs is a place to learn more about how our technology works, see how it can be used in real-world applications, and get hands-on experience with beta and prototype software that puts SNOCAP to work. We’ve kicked things off by posting some whitepapers describing the technological underpinnings of our digital licensing and copyright management services. Later, we’ll post additional whitepapers, links to software that makes use of the SNOCAP system, and articles about digital music technology.

If there are specific questions about our technology that you have, or topics of interest you’d like to see discussed, please leave a comment.