Yahoo have recently introduced nice features such as news delivery as RSS feeds. They now offer an extensive audio search which encompasses on-line music stores as well as smaller sites and self-promoting bands. It appears to work well because it managed to pick up some long-forgotten files — the stuff I recorded in 2002 or thereabouts. It’s all quite embarrassing, but I am flattered to find my name among the results pages. Raves and insults in the box below….

Spreadsheet representation of music history (click to enlarge)

ome weeks ago I wrote about an XMMS extension that I had devised. This collection of steps which I described could make XMMS store simple log files for music (music ‘history’). That work has now been thoroughly extended to produce spreadsheets (comma-separated values (CSV) to be precise) rather than flat log files.

In brief, what is obtained using this trick are music history logs, one file for each day. Each file contains full, detailed history of the tracks listened to. Interesting information can be derived from these, e.g. most frequently played tracks, tracks quickly skipped (implying the possibility of deletion or removal from playlist). As already said in last entry:

A music log file results in even more redundant data to store. According to a rough calculations, I will see it growing by 1 megabyte every month or so. Compression, however, should make it only 10-20% of its original size.

You can find all the details on this work (installation instructions included) in my Linux Utilities page.

ow does one identify music which has potential of being liked? Music, unlike textbooks, does not contain text or keywords. Its tags are not always valuable either. An interesting paper from Trinity College Dublin describes a method by which music adapts to the preferences of listeners (PDF). However, can this be done purely based on prior data? Data that is provided in advance unlike in real-time? A List of records maybe? Playlists perhaps? We seem to be coming closer to realisation of this idea.

Image similarity measures are one focus point of my research; also sparks to mind is Google’s notion of ‘Similar Pages’. Why not apply similar principles to music? I now collect big daily dumps of music that I listen to (output to files using the following technique ). Bound to each entry is the time when a track started. From this, one can infer which tracks are being skipped. Alternatively, full, raw playlists can be of use and might, in fact, be more manageable as well. By exploiting a large collection of playlists, the nature of the genres can be better understood.

Given all of this data, it can potentially be used for collabortive playlist sharing, somewhat like del.icio.us (see previous reference to del.icio.us with a gentle introduction). Users can then discover other songs they might like based on other people’s playlists. The more data, the more accurate statistics will be. Getting large lumps of input (playlists) is effortless too. Just imagine yourself the scenario:

You can automatically find playlists most similar to yours and recognise the most-played tracks on that playlist. Social software has seen great success recently, so exchange of music preferences and recommendations is probably the way to proceed.

There is a true advantage to having audio stored locally, as in the case of off-line podcasting. There is full control, in general, over the audio data in question. This means that podcasts can be sped up to fit the preferences of the listener. There is an XMMS plug-in called SndStretch (shown to the left) which I absolutely adore. It can speed up audio to be up to twice as fast, without change in pitch. No more squeaky voice due to change in pace. An half-hour show can be listened to it in only 15 minutes. This also works for music, which makes it more entertaining and variable.

Ever wondered how much you listened to a particular song?

XMMS possesses a great extent of flexibility. It provides facilities which, when customised appropriately, can dump complete song descriptions. XMMS can then progressively, accumulatively add song data to log files. Timestamps imply the duration of listening, which lead to possible future extensions, e.g.:

• Automatically parse (scan, hash, then analyse) the log file/s
• Compute an average listening time — that is before skipping to another track — for each individual song
• Erase music that is frequently skipped or just output a ‘prospective deletion list’ to another file

Input/output operations are made quite simple. These can be invoked as command-line strings whenever a new track begins. It is a built-in XMMS plug-in, which can be found under Preferences » Song Change » Configure (see illustration below)

Add the following line to the “Song change” Command field:

(date; echo %s) >> ~/My_playlists/playlist.log

I am providing an example log file, assembled naturally (not artificially. Honest!) over 20 minutes or so. I still need to get rid of the doubly appearances, apparently caused by xmms-kde in my dock (screenshot on the right). As well as giving a minimal user interface, xmms-kde enables control of the player with CTRL+SHIFT+keypad even if the player is neither visible nor in focus.

A music log file results in even more redundant data to store. According to a rough calculations, I will see it growing by 1 megabyte every month or so. Compression, however, should make it only 10-20% of its original size.

UPDATE 26/06/2005: The following command, which can be set to become a nightly cron job, will slice the large log file so that it is composed of daily ‘chunks’:

mv ~/[Path]/playlist.log ~/[Path]/playlist-`date +%Y-%m-%d`.log

This can be extended to form an annual and monthly hierarchy of directories.

See the later extension: producing history spreadsheets

Some months ago, an automatic and effective way of obtaining music was described: no need to run any heavy graphical program; no need to select music (only sources of music of the desired genre); no limits on bandwidth or volume.

Taking this further, Dr. Scott Tobkes explains about his method of bringing commericals to a halt.

Something I’ve had fun doing lately is recording live of archived streaming audio from radio stations around the country

There are innumerable subjects and programs. I record it to mp3 or wav, and listen in my car or PC, while fast-forwarding through the commercials. I know that there are aggregators that will make this a seamless process like Ipodder.

I don’t like to call them podcasts. I use this program…

eer-to-peer (P2P) and the World Wide Web (which we most commonly perceive as hypertext — HTTP) are separate. When one uses an Internet connection to directly communicate with a remote machine, then traffic is uncensored, often not monitored, and there are no restrictions imposed on the exchanged material. This justifies the potential illegality of P2P networking.

There are less controversial ways of obtaining media. When it resides on the World Wide Web and hosted by a trusted source, it is then subjected to copyright laws. For this particular reason, it is safer to download MP3 files from trusted Web sites, many of which distribute music for free in interests of self-promotion. Downloads are also far faster because data is delivered by a Web server. An older item explains how to automatically (READ: recursively) download music from the Web.

I have just discovered an inexhaustible source of music, which is also delivered as an RSS feed. That source is the MP3Blogs Aggregator. You must read this entry on the subject in order for everything to make sense. The gist: infinite amount of freely-distributed music reaching your hard-drive on a daily basis.