Mashups for last.fm

Here are some more or less useful mashups (-lists) I stumbled upon that seam to be nice:

last.fm’s events function together with fusecal could be very usefull for bands’ websites to give visiters an easy way to be noticed only for concerts around there home town, e.g.

Convert Videos, Extract Audio from Videos and The Like

Just a short note on this right now:

Audio can be extracted with mplayer using -dumpaudio option (low quality), ffmpeg tool with -ab and -ar options or online for youtube videos at vixy.net.

Quick video converter flv2mpg using ffmpeg is shown at Linux by Example, too.

Update: Another great open source program is MediaCoder which even has a special audio edition.

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The Ultimate Lossless Compression Format with Hybrid Mode and Open Source: WavPack

Way the hack didn’t I trip over WavPack (wv) earlier — it’s been around for some time now and astonishingly ultimate. To make it short and obvious (see hydrogenaudio.org for complete list):

Pros

  • Open sourc
  • Good efficiency (fwd is even better than mp3′s on foobar2000)
  • Hybrid/lossy mode (see below)
  • Tagging support (ID3v1, APEv2 tags)
  • Replay Gain compatible (which is no deal with fb2k anyway, but still)

Cons

  • Limited hardware player support
  • does take long to encode with optimal settings (not really because one time procedure)

Other features

  • Supports embedded CUE sheets
  • Includes MD5 hashes for quick integrity checking
  • Can encode in both symmetrical and assymmetrical modes
  • Supports multichannel audio and high resolutions
  • Fits the Matroska container
  • streaming support
  • Error robustness

So it’s open source! Most distros even come along with WavPack preinstalled. The only other lossless formats that I know are open sourced are two: FLAC which has bad tagging support and Shorten (to put it short: out dated). MAC (Monkey Audio Codec, ape) has an open sourced version but it’s not developed any longer.

The next great feature is hybrid mode which means you decode to lossy small file and an additional file containing “the rest” of the information. Only one other format is capable of this: OptimFROG (ofr) in DualMode. That means, putting both files together you get your 100% original back. The lossy file can be used entirely on it’s own. While encoding there is a second file, called correction file, that stores the difference between lossy and original — compressed that is. So what that means is you don’t have to convert your files each time you’re to shove them onto your portable. The only bad thing is you need to ensure the device can decode (read: play) them.

WavPack Properties after encodeTo give an example: If you convert a 27.5 MB ofr file to wv, hybrid enabled with lossy bitrate set to 192, you’ll get one 6.31 MB sized .wv file and a second 22.8 MB sized .wvc file. It took 12 min. 15 sec (playtime 4:25). Insane settings where: Compression Mode “high”, Processing Mode “6″ (best encoding quality), Hybrid Lossy Mode “192kbps”. When you open the .wv file only in fb2k it’ll handle correction files automatically (see picture). However moving the file with foobar2000′s dialog “” will only move .wv files. I cannot speak for other software players but this way it’s just easy to handle two qualities of one file — one hifi one and on “to go”! After I now have converted an entire album here are the approximate file sizes comparing MAC, OptimFROG and WavPack and MP3/OggVorbis as lossy counterpart to “portable wv” (to be added to .ape/.ofr):

.ape 311 MB
.ofr 306 MB
.wv 70 MB
.wvc 255 MB
.wv+.wvc 325 MB
.mp3 (V2, ~190kbps) 74 MB
.ogg (q5, ~160kbps) 61 MB

Tagging: Unlike FLAC it uses APEv2 (or ID3v1) so tags can be used with most players, software and portable devices’ ones, without intervention.

While I ran encoding test’s with foobar2000 (which has decoding WavPack “build-in” by the way) I noticed when converting from, say, OptimFROG to WavPack fb2k went right at it. No temporary wav files as with OptimFROG to MAC, for example! But mind you it does take a long time if you use optimization for file size and quality. It seams to be somewhere around 0.7x (slightly slower than plain play time). I don’t see why this really is an issue because in most cases you’ll only encode once as it’s true for all lossless formats anyway.

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First Impression on Musicovery

Even though I’m into last.fm, if any (of those), I do understand if some Panora maniac cheets on them the alternative has to be worth a glimpse. So, it was Musicovery.com that also impressed me … at first. And I’ll admit it was mostly because of the blinky-blinky. But it’s more to it than just effects attention. From a HMI conceptional point of view Musicovery have really made an effort. It is easy to start listening to what you want — without any or, if you really want to, very little reading. In one word: I’d call it intuitive.

You are presented those, and only those, selections you need to do and combine (what you can) to make your choice distinct enough to gather correct songs. Also the other direction of “communication”, machine to human, has some promissing approaches like the “neighbourhood map” and colours for genres. One can even drag (move) that map around. The playlist is shown as path through the graph of audio tracks.

But then, of course, the hacker in me came to surface and I had to test that stuff. After a few clicks I was presented with Shakira’s “Objection” after hitting “dark” mood. Sure, no accounting for taste, but I wouldn’t call “Objection” a dark mood song. And also there was Black Eyed Peas’ “Shut up” to come… I don’t know about you; I couldn’t keep my feed still while listening and there where absolutely no “I hate the world” and “Where is my gun to get a rampage going” (just being sarcastic here). While the “energetic” direction has worked fine for a while dark more and more seams to be a bad label.

To conclude Musicovery.com nevertheless sounds very promising. I’d really like to know the “music selection techniques” behind it, though, since the more I listen to the tracks that are picked for a selected mood don’t satisfy me just like the other lot.

Edit: I just caught myself letting imaginary drift away: Wouldn’t it be possible to have, in a few years time, some HMI stuff so one brachiates though a play list just like the one displayed at Musicovery but as some sort of hologram or only imaginary (not directly visible) but more like that Wii stuff? So if one wants to ffw to a track on the playlist (displayed in some sort of 3D neighbourhood map/grid as a ball, e.g.) you grab it and drag it to the middle of the cube or punch it to play it, pet it to let information been displayed about it, …

One Step further towards Diplom Thesis

While doing yet again a search for companies or institutes, i.e. a attendant, possibly related to what I’m looking for (automated music similarity analysis) I got one big step forward finding projects at the Fraunhofer IDMT (Institute Digital Media Technologies) that sound really interesting. What I’m interested in is doing some sort of wave form analysis and find different characteristics, different descriptive measures that make two music pieces “sound similar” independent of genre, same artist or whatever and those that make two other pieces different. Most interesting would be to derive them from how we humans would decide it which, of course, is not always deterministic, i.e. fuzzy. The long term dream would be to have an automate find the right music for a given emotional atmosphere, e.g. candle lite dinner, BBQ, reception event…

  • SoundsLike — Sounds like it comes close to what I’m interested in; derived from AudioID fingerprinting mechanism.
  • GenreID – more the category based approach similar to acoustic fingerprinting. Still interesting, though.
  • Query by Humming — Finding music tracks by humming the characteristic melody. But what exactly is characteristic to a certain track?
  • Semantic HiFi — interesting project; combines multiple tools to have the stereo generate personalized playlists on demand via call by voice, interacts with other media devices. Reads very promising. The project itself went from 2003-2006. And what’s really interesting is a cooperation with, among others, the University of Pompeu Fabra, Barcelona, Spain.
    I also could imagine automated adjustment of volume level by sound level in the room if actually it’s wise and no feedback effekt takes place, e.g. at a cockail party: conversation louder -> music louder -> conversation louder…
  • Retrieval and Recommendation Tools — just the attempt I’m looking for.

I also stumbled upon news that the mp3 format has been enhanced to encode surround sound information while only enlarging file size by about 10% (see also mpegsurround.com). And secondly an attempt to use P2P to legally ship music content and to utilize it to encourage people to by the better quality version called Freebies.

How ebay Germany helps us to learn whom we buy from

Have you ever wanted to know what size of bra your opponent has you should consider buying at ebay Germany. The German radio station B5 aktuell pointed out that in their broadcast “Das Computermagazin” on April 8th this year ebay happily send address data to those whose bid was accepted. Using google earth, google maps respectively and ebays’ lists of previous bids it seamed an easy task to find out about the seller’s erotic favours and optic abilities. The best bit is the ironic manner in which the journalist talks about his findings. If you know German you should digg right into Minute 10 of B5s’ podcast and find out about the Bremen newspaper.

Foobar2000 From It’s Pretty Side

A little proud to exhibit my foobar’s new look adopted from FofR’s really nice PanelsUI configuration (thank’s to Jason, who pointed me to it). Not only is foobar2000 by far the best audio player for the windows platform I came across so far. Once you get used to all the neat bits and pieces of functionality and comfort in addition to the rich out-of-the-box features over time you’ll with no regret invest the time-consuming configuration tasks. With a little enthusiastic code digging it can even be made pretty. And as you can see below saying that means really pretty.

f2k in it’s FofR gown playing noisome paste.f2k in it’s FofR gown playing noisome paste.

f2k in it’s FofR gown playing noisome paste.f2k in it’s FofR gown playing noisome paste.

By the way, noisome paste is gaining ground not only in the north of Germany were they come from but also in web charts. If you like their rock-hard sounds (demo version) help them by voting for them.

I Had a Dream

… a day dream that was. I was walking through my flat listening to music of my favourite kind. Well, tell me something new you might think. Here it comes: Each time I walked from one room to another the music speakers in the next room would be activated by the N95 (or any other Upnp aware device) playing the music. So the music would only be played in that sorounding I was in. Of course the music was not locally stored on the N95 but came via Upnp (or whatever) from my file storage via ether.

That’s not so unrealistic as I thought at first. I vaguely remember reading about a sound system that can address all present speakers individually via remote control even. That, of course must have been centrally controlled, though, and should have been proprietary, i.e. only working of all hardware comes from the same manufacturer. But how about if that worked via Upnp (or anything the like)? I guess the tricky bit would be the hand over of the signals transporting the sound information, i.e. manage gapless playback as if everything was wired and feed by broadcasting the sound to the speakers. Of course the speakers most likely would have to support some kind of wireless technique that the signal could be transmitted by.

Also, some mode that does address each speaker individually should be implemented, regulating the sound volume for each box (or at least each room) so the toilette is not blasted away… But I guess that would be rather easy and does not even need a modulated infrastructure as needed for the “sound hand over” scenario described above. What a cockaigne world it would be to have an abstract layer every manufacturer sticks to and supports to handle “cross-platform-interaction” like needed here. Well, I’m looking forward to building that cockaigne and living in it. How about you?

What would be even greater to have a stationary player, say in the living room, controlled via, eg. Upnp, but still be able to have the hand over working, i.e. have the system notice where the sound should be played in which volume. Another attempt could be some kind of tracker that knows where the person listening to the music is sojourning. I don’t think that would be the best approach since most likely it needs complex structures. Plus I can’t think of a way to keep that tracker “inter-platformly” scalable for many scenarios and systems.

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Music Analysis — On The Way to Diploma Thesis Topic

To step onwards in finding a subject for my diploma thesis I’ve googled a littel and found the following:

First of all I looked for what topics are being worked on at my uni to maybe narrow it that way. Our Institute for Digital Media seamed the best guess showing a seminar by Dr. Dieter Trüstedt called “Elektronische Musik in Theorie und Praxis” (electronic music in theorie and in practice”). Only after a while I noticed that it emphasis on, or I should say is making music, not analysing it. Nevertheless I was pointed to a book by Miller Puckette (Dept. of Music, University of California, San Diego) called “The Theory and Technique of Electronic Music” including some parts about wave analysis in generell, digital music, etc.

Issues I’m looking for are as described before, more precisely finding similar music as a starting point. I also found a few (not yet reviewe) papers:

  • Music Database Retrieval Based on Spectral Similarity by Cheng Yang
  • Pattern Discovery Techniques for Music Audio by Roger B. Dannenberg and Ning Hu
  • Toward Automatic Music Audio Summary Generation from Signal Analysis by Geoffroy Peeters, Amaury La Burthe and Xavier Rodet
  • Audio Retrieval by Rhythmic Similarity by Jonathan Foote, Matthew Cooper and Unjung Nam

Also, what came to my mind what to maybe take into account how humans (mammals) distinguish music (or complex sounds) and thus learn more about the brain, also.

Another thought that hit my mind concerning the use of such an analysis was to use it in, say meeting recording scenarios as some kind of search algorithm. Imagine you have some 3 hours of meeting recorded (possibly conference call) and need some certain part of but cannot find the time position by any means. Maybe by the analysis spread out above one can use a search just as we do nowadays with text: Speak the word or phrase one is looking for (with a different voice — your own) and find the position in the audio file.

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