Showing posts with label iTunes. Show all posts
Showing posts with label iTunes. Show all posts
Friday, February 10, 2006
Black Sabbath + Madeleine Peyroux
I was browsing the iTunes music store tonight and happened across Geezer Butler's celebrity playlist. He's the longtime bassist of Black Sabbath -- a band famous for ushering in a new era of hard rock, with a macabre subtext :) In contrast to the dark sound Sabbath is known for, Geezer has quite an ecclectic mix of blues (e.g. madeleine peyroux, etta james), country (e.g. alison krauss), electronica (e.g. lamb), and, of course, slayer.
Wednesday, May 04, 2005
iPod Froze, What to do...
My iPod froze while walking to UW today. Thoughts of Cassandra's problems with her third generation iPod immediately entered my head. For Christmas 2003 I gave her a 3rd generation 20gb iPod that, I swear, was infested by a mischievous Chimera -- luckily the Apple Store took it back and replaced it on the spot ~3 months later. I found this link on Apple's website that has step by step instructions on how to recover from a frozen iPod.
For 4th generation iPods 
1. Toggle the Hold switch on and off. (Slide it to Hold, then turn it off again.)
2. Press and hold the Menu and Select buttons until the Apple logo appears, about 6 to 10 seconds. You may need to repeat this step.
Tuesday, May 03, 2005
Argh, inTune & Google Web API
inTune currently uses amazon.com's (very cool) web api to find links to cover art for music files. However, in the case when no cover art exists (most likely because amazon.com does not sell any albums by the given artist) I wanted to use Google's API as a backup. Ah, yes, sounds like a reasonable plan BUT that's assuming Google's API actually exposes their image search functionality. It does not! So, I probably will not support Google in inTune's initial release.
I guess I could, of course, build a scraper that simply appends the artist and/or album onto the end of 'http://images.google.com/images?q=' and grabs the first image that pops up.
But I don't feel like it.
Update (10:42PM May 3, 2005): Fine! So, I have been obsessing over actually using Google's image search. I'm sorry. I'm coding and thinking at the same time (yes, that actually happens sometimes). I typed in "scraping images.google" into Google and, shameslessly, Google answered my call. The first result was Mr. Dan Fernandez who is also Mr. Visual C# Product Manager and in promoting said language hacked together an images.google.com scraper. Ack. I just noticed that this is for VS2005 which I don't have and is also for Visual C# Express. (link)
Update (9:36PM May 6, 2005): OK. Google does not expose their image search capabilities in their API, but yahoo does. I might still write a Google image scraper (for curiosity's sake) but I'd rather not spend too much time on it given that (1) it's questionably legal and (2) harder to maintain given that you're dependent on the format of the webpage.
I guess I could, of course, build a scraper that simply appends the artist and/or album onto the end of 'http://images.google.com/images?q=' and grabs the first image that pops up.
But I don't feel like it.
Update (10:42PM May 3, 2005): Fine! So, I have been obsessing over actually using Google's image search. I'm sorry. I'm coding and thinking at the same time (yes, that actually happens sometimes). I typed in "scraping images.google" into Google and, shameslessly, Google answered my call. The first result was Mr. Dan Fernandez who is also Mr. Visual C# Product Manager and in promoting said language hacked together an images.google.com scraper. Ack. I just noticed that this is for VS2005 which I don't have and is also for Visual C# Express. (link)
Update (9:36PM May 6, 2005): OK. Google does not expose their image search capabilities in their API, but yahoo does. I might still write a Google image scraper (for curiosity's sake) but I'd rather not spend too much time on it given that (1) it's questionably legal and (2) harder to maintain given that you're dependent on the format of the webpage.
Thursday, April 28, 2005
Music History as Context
Have not had a chance to work on inTune for a few days; however, thoughts have been ruminating.
I'm thinking I might build another application this summer for iTunes. This time using mySQL or some other local database technology that archives the iTunes library.xml file. Each change (in the form of a rating, playcount, addition, removal, etc.) will be stored in the database. The idea being that we could use this data to figure out exactly what and when music was listened to. Could we then, for example, draw a temporal map of songs played in the past sixth months to get a sense of someones fluctuating emotional state during that period.
Obviously we would be able to see certain trends in the ways that people use their iPods or iTunes. What could this tell us? Could music be used as an additional contextual feature to inform UbiComp applications?
I'm thinking I might build another application this summer for iTunes. This time using mySQL or some other local database technology that archives the iTunes library.xml file. Each change (in the form of a rating, playcount, addition, removal, etc.) will be stored in the database. The idea being that we could use this data to figure out exactly what and when music was listened to. Could we then, for example, draw a temporal map of songs played in the past sixth months to get a sense of someones fluctuating emotional state during that period.
Obviously we would be able to see certain trends in the ways that people use their iPods or iTunes. What could this tell us? Could music be used as an additional contextual feature to inform UbiComp applications?
I Sweat Your Music Collection
Using iTunes "Rendezvous" system (recently renamed to Bonjour), iTunes users can share their library of music over their local network. And by share I mean stream -- that is, others can only listen to your shared music, not copy it. Anyway, a few people have studied the social nuances that surround this technology (e.g. Grinter at GTech). For example, users are afraid of what other's might think of them based on their music collection (even though the sharing is somewhat anonymized).
I found this anecdote of appropriating iTunes sharing over wireless networks to meet people (and as a sort of low bandwidth instant messaging client) to be quite interesting.
The scene is both postmodern and Reineresque: young man with PowerBook in coffee shop discovers amazing music collection shared via iTunes. He hopes to God it belongs to the raven-haired hotty in the corner. He proceeds to share a library of his own, no songs, but simply titled, “Maria, I sweat your music collection.” She takes note, creates a library called “try_the_new_dalek”. And so began a library-name-only flirting session that will no-doubt make Mac haters seethe and AppleHeadz swoon. Either way, it’s a way to pass the time between scones and textbook chapters on a lonely Tuesday eve. Try it before it becomes some new, creepy form of cyberstalking. from engadet
I found this anecdote of appropriating iTunes sharing over wireless networks to meet people (and as a sort of low bandwidth instant messaging client) to be quite interesting.
The scene is both postmodern and Reineresque: young man with PowerBook in coffee shop discovers amazing music collection shared via iTunes. He hopes to God it belongs to the raven-haired hotty in the corner. He proceeds to share a library of his own, no songs, but simply titled, “Maria, I sweat your music collection.” She takes note, creates a library called “try_the_new_dalek”. And so began a library-name-only flirting session that will no-doubt make Mac haters seethe and AppleHeadz swoon. Either way, it’s a way to pass the time between scones and textbook chapters on a lonely Tuesday eve. Try it before it becomes some new, creepy form of cyberstalking. from engadet
Wednesday, April 27, 2005
inTune With Me
I've recently begun a literature search in the space of "recommendation engines." These are systems that leverage information about you (and perhaps others like you) to make a suggestion about things you might like; think amazon.com's recommender.
Quite honestly, I can't say I have ever been truly impressed with the current crop of recommenders. Though, admittedly, I probably haven't used that many of them -- perhaps out of lack of opportunity. However, in the fall of last year (at the beginning of my first term at UW) I subscribed to the Yahoo LAUNCHcast music service (~$3 a month). Note that LAUNCHcast also has a free service with a limited number of radio stations. Song skipping is also crippled (you can, i think, skip 5 songs in a day rather than an unlimited amount). You also then have to listen to a few short commercials every hour.
Launchcast's tagline is "the music that listens to you." Using a method called collaborative filtering, Launchcast introduces new music into your customized radio station that the system thinks you might like. How does it work? The success of the recommender system rests largely on the willingness of the user to actively rate songs. Song, album, artist, and genre ratings are combined with the existing launchcast userbase to find "similar listeners." These listeners have roughly the same ratings as you. Launchcast then begins to play songs that they have rated highly but that you've not rated (or perhaps heard).
Users will be more likely to participate if they feel like the value returned is high. However, the return value is rather low until you (and others, as the system uses collaborative filtering) begin rating songs. This is called the bootstrapping problem -- an issue that exists in many recommendation systems.
I don't mind rating songs. I rate songs in my iTunes library all the time so I have a refined approach to rating songs on launchcast. In addition, music is my passion. It is one of the few things in life that I freely admit to loving without flinching. So, the work that I do to rate songs is worth it to me because I will (hopefully) learn of new music that I like in return.
And, in this case, it's actually worked. I would say that a fair amount (perhaps as much as 60%) of the new music I hear on launchcast, I like. A subset of that (~15%), I really like. In fact, if you look at the music that I have purchased in iTunes in the past six months, a majority of it was introduced to me by launchcast. That fact alone has enormous implications for e-commerce. Suddenly, technology has taken the role of my "trusted musicphile" friend who sends me mix tapes in the mail; though I'm not sure it is yet an adequate replacement.
Of course, the iTunes music store itself has yet to really take advantage of user's metadata. You could imagine iTunes scanning your music library to derive your likes and dislikes (perhaps as a combination of playcount, ratings, and simply number of songs by an artist) and suggest new songs that you might want to buy. They do, however, make recommendations to you based on what is currently in your cart. This obviously has shortcomings.
Lately I've been imagining an iTunes based online dating service where you pair two clients together based on their music tastes. Or, perhaps something more benign, like a friendster that connects people together into the same social network based on similarity clustering of their music libraries.
Still working on what I've dubbed "inTune." A friend yesterday told me the name sucks. Perhaps it does. Not that it matters at this point.
Quite honestly, I can't say I have ever been truly impressed with the current crop of recommenders. Though, admittedly, I probably haven't used that many of them -- perhaps out of lack of opportunity. However, in the fall of last year (at the beginning of my first term at UW) I subscribed to the Yahoo LAUNCHcast music service (~$3 a month). Note that LAUNCHcast also has a free service with a limited number of radio stations. Song skipping is also crippled (you can, i think, skip 5 songs in a day rather than an unlimited amount). You also then have to listen to a few short commercials every hour.
Launchcast's tagline is "the music that listens to you." Using a method called collaborative filtering, Launchcast introduces new music into your customized radio station that the system thinks you might like. How does it work? The success of the recommender system rests largely on the willingness of the user to actively rate songs. Song, album, artist, and genre ratings are combined with the existing launchcast userbase to find "similar listeners." These listeners have roughly the same ratings as you. Launchcast then begins to play songs that they have rated highly but that you've not rated (or perhaps heard).
Users will be more likely to participate if they feel like the value returned is high. However, the return value is rather low until you (and others, as the system uses collaborative filtering) begin rating songs. This is called the bootstrapping problem -- an issue that exists in many recommendation systems.
I don't mind rating songs. I rate songs in my iTunes library all the time so I have a refined approach to rating songs on launchcast. In addition, music is my passion. It is one of the few things in life that I freely admit to loving without flinching. So, the work that I do to rate songs is worth it to me because I will (hopefully) learn of new music that I like in return.
And, in this case, it's actually worked. I would say that a fair amount (perhaps as much as 60%) of the new music I hear on launchcast, I like. A subset of that (~15%), I really like. In fact, if you look at the music that I have purchased in iTunes in the past six months, a majority of it was introduced to me by launchcast. That fact alone has enormous implications for e-commerce. Suddenly, technology has taken the role of my "trusted musicphile" friend who sends me mix tapes in the mail; though I'm not sure it is yet an adequate replacement.
Of course, the iTunes music store itself has yet to really take advantage of user's metadata. You could imagine iTunes scanning your music library to derive your likes and dislikes (perhaps as a combination of playcount, ratings, and simply number of songs by an artist) and suggest new songs that you might want to buy. They do, however, make recommendations to you based on what is currently in your cart. This obviously has shortcomings.
Lately I've been imagining an iTunes based online dating service where you pair two clients together based on their music tastes. Or, perhaps something more benign, like a friendster that connects people together into the same social network based on similarity clustering of their music libraries.
Still working on what I've dubbed "inTune." A friend yesterday told me the name sucks. Perhaps it does. Not that it matters at this point.
Sunday, April 24, 2005
iTunes Hax0r Links!
iTunes COM C# Hax0r Stuff
Brace yourself for a revelation. I have not yet used C#. I programmed my Master's thesis in Java (which is a fair amount of code). Of course, when I began my thesis project I thought I knew Java -- by the end I knew I didn't know Java. I like Java though. It's comfortable. I can do the C++ thing (see CSE557 and a fair amount of undergrad and intern work) but I prefer Java.
However, I have always been interested in learning C# -- some people have always been C# evangelists and they are getting to me. What's more though, I feel like it will be beneficial in my career and now I've found a reason to actually sit down and learn it... in the form of an iTunes hack.
I want to publish near real time information about my iTunes library -- I think I'll call it "inTune." Both because it analyzes INformation about iTunes and because it publishes them to your webspace thereby keeping others "in tune" with your music.
It will provide metadata on my entire library of songs, a few examples
Total number of songs in my library
Total number of songs rated
Total number of songs listened to
Hot songs of the week
Hot artists of the week
Most recently added songs/artists
Top 25 lists (bands, songs, etc.)
I was thinking about how to use iTunes metadata to come up with some metric for calculating favorite artist. Something like:
Favorite Artist =
# of times songs played by artist
# of songs ranked - # of songs unranked
# of total songs by artist
weight 3, 4 & 5 star rankings positively
weight 1 & 2 star rankings negatively
I'll throw in some pluses/minuses/multiplication and maybe some division in there to make it a leet formula sometime soon.
However, I have always been interested in learning C# -- some people have always been C# evangelists and they are getting to me. What's more though, I feel like it will be beneficial in my career and now I've found a reason to actually sit down and learn it... in the form of an iTunes hack.
I want to publish near real time information about my iTunes library -- I think I'll call it "inTune." Both because it analyzes INformation about iTunes and because it publishes them to your webspace thereby keeping others "in tune" with your music.
It will provide metadata on my entire library of songs, a few examples
Total number of songs in my library
Total number of songs rated
Total number of songs listened to
Hot songs of the week
Hot artists of the week
Most recently added songs/artists
Top 25 lists (bands, songs, etc.)
I was thinking about how to use iTunes metadata to come up with some metric for calculating favorite artist. Something like:
Favorite Artist =
# of times songs played by artist
# of songs ranked - # of songs unranked
# of total songs by artist
weight 3, 4 & 5 star rankings positively
weight 1 & 2 star rankings negatively
I'll throw in some pluses/minuses/multiplication and maybe some division in there to make it a leet formula sometime soon.
Friday, April 22, 2005
Jesus Shaves!
Heard a cover of this song on NPR today by the Roches. It's originally by "Paranoid Larry." You can grab it here for iTunes. I did.
Jesus Shaves
Joins corporate America
Gets laid off
Grows his beard back
Stays up late
Sees a TV commercial
About being a welder
Makes a phone call
Jesus Shaves
Goes on an interview
Does real well
He's got a way with people
Two years on
He's still an apprentice
But not for long
It's graduation
Jesus Shaves
Puts his best suit on
Gets a certificate
Makes it official
Now he's a welder
Wears a big helmet
And twice a week now
Jesus Shaves
*Chorus*
Blessed are the ones who make peace
Blessed are the ones who scrape by
Blessed are the ones livin' holy lives
And here's to the rest of us who try
Jesus Shaves
On Sunday morning
Decides to go fishing
After goin' to church
It's winter time
So he walks on water
Digs a hole in the ice
And fishes for Perch
Jesus Shaves
And scales off the fishes
Has a few filets
And a couple of brewskies
Thinks about the girl
Magdelena in payroll
Decides that he's gonna
Ask her out for dinner
Jesus Shaves
For work on Monday
Goes to the office
Gather's his courage
He's still not sure
Just how he's gonna ask her
Walks over to her desk
And sees a picture of her boyfriend
*Chorus*
She says 'hello'
'How are you this morning'
He asks if she knows
A little snow or rain
She notices him lookin'
At the picture on her desktop
She says it's her cousin
Who's past away
He says 'that's great!'
She looks at him funny
He says 'I didn't mean that'
She says 'It's ok'
And then she smiles
And they both start laughing
And that's when he knows
It's his lucky day
Jesus Shaves
Smilin' in the mirror
Magdelena is sayin'
That they need to hurry
They will be late for work
And the school bus is comin'
But their daughter likes watchin'
As Jesus Shaves
*Chorus*
Jesus Shaves
Joins corporate America
Gets laid off
Grows his beard back
Stays up late
Sees a TV commercial
About being a welder
Makes a phone call
Jesus Shaves
Goes on an interview
Does real well
He's got a way with people
Two years on
He's still an apprentice
But not for long
It's graduation
Jesus Shaves
Puts his best suit on
Gets a certificate
Makes it official
Now he's a welder
Wears a big helmet
And twice a week now
Jesus Shaves
*Chorus*
Blessed are the ones who make peace
Blessed are the ones who scrape by
Blessed are the ones livin' holy lives
And here's to the rest of us who try
Jesus Shaves
On Sunday morning
Decides to go fishing
After goin' to church
It's winter time
So he walks on water
Digs a hole in the ice
And fishes for Perch
Jesus Shaves
And scales off the fishes
Has a few filets
And a couple of brewskies
Thinks about the girl
Magdelena in payroll
Decides that he's gonna
Ask her out for dinner
Jesus Shaves
For work on Monday
Goes to the office
Gather's his courage
He's still not sure
Just how he's gonna ask her
Walks over to her desk
And sees a picture of her boyfriend
*Chorus*
She says 'hello'
'How are you this morning'
He asks if she knows
A little snow or rain
She notices him lookin'
At the picture on her desktop
She says it's her cousin
Who's past away
He says 'that's great!'
She looks at him funny
He says 'I didn't mean that'
She says 'It's ok'
And then she smiles
And they both start laughing
And that's when he knows
It's his lucky day
Jesus Shaves
Smilin' in the mirror
Magdelena is sayin'
That they need to hurry
They will be late for work
And the school bus is comin'
But their daughter likes watchin'
As Jesus Shaves
*Chorus*
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