tk42

Thanks :) Hope it will be of use to people. We don't use solr, we use elasticsearch and we also don't hold the unzipped fingerprints in the same format the default echoprint does. But for this dump we exported everything in the official echoprint format so people can use it with the default cluster.

- Now works with vimeo & dailymotion

- Exact time for analysis can be passed

- Added additional third party fingerprinting technology(doreso) to find snippets echonest can't identify

- false positives reduced drastically as we can be stricter about thresholds due to above point

- works much better on mixtapes with altered BPMs due to above 2 points

- Finds and embeds additional sources for identified songs

So far we've been hesitant to go that route as the current 7m fingerprints come down to 42m+ documents in Lucene. I.e. If we were to save 20 versions of each fingerprint in different bpms we'd quickly have about a billion+ documents on our hands to be searched on every query (with ~1000-2000 hashes per document)

One-click hosters and cloud hosters with sharing options(such as docs.google.com), several music streaming sites, usenet. Also the amount of mp3s hosted on regular webservers which are indexed by and easily found using the usual search engines is mindblowing

Hug of death occured faster than expected :) scaling now

The user can paste a youtube url which will then be analysed, fingerprinted and matched against a database of 7+ million audio fingerprints. It does not only identify a single song but is able to identify multiple songs contained in a single file or video and generates a timeline listing which tracks it contains at which time.

Our matching algorithm is based on the open source echoprint-codegen fingerprinting method, which we have built our own stack around:

- Replaced Solr/Tokyo Tyrant with Elasticsearch

- Reimplemented matching-logic

- Crawlers search multiple sources for audio files to be indexed (mp3s arent stored long term, only fingerprinted then deleted)

- Indexing about 1 new track per second

- Found method to verify unrealiable ID3 tags (in progress, current database also includes unferified)

- mogilefs as primary data store for fingerprints

- perl everything

We also provide a free music identification API.

Any feedback would be much appreciated!

My software engineering process works as follows:

I try to code as much as possible as early as possible. I throw away lots of stuff and recode it. Besides that I have an eye for stuff that is "similar" and can be abstracted. If someone wants an estimate, I guess as good as possible.

Big code is idealy split into one-person-chunks each with a documented API, but sometimes many people have to work on the same "files". Then big code is split between multiple people that sit nearby and communicate personally while discussing implementations based on technical arguments.

How to make a product of software is a different story. But I guess it works when you design your product in estimateable pieces and adapt fast to changing requirements.

Also I am pretty sure I forgot one or two things...

But don't forget the cards.

The state of JavaScript in 2015: it still sucks. I just wish the webbrowser creators would implement a way of other languages to be executed as a replacement of JavaScript. Not gonna happen, I know...