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Speaker 1: Brought to you by the reinvented two thousand twelve camera.

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Speaker 1: It's ready. Are you get in touch with technologies? With

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Speaker 1: tech stuff from how stuff works dot com. You've heard

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Speaker 1: the rumors before, perhaps, and whispers written between the lines

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Speaker 1: of the textbooks. Conspiracies, paranormal events, all those things that

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Speaker 1: disappear from the official explanations. Tune in and learn more

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Speaker 1: of the stuff they don't want you to know in

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Speaker 1: this video podcast from how stuff works dot com. Hello, everybody,

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Speaker 1: welcomes to text stuff. My name is Chris Platt, and

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Speaker 1: I'm an editor here at how stuff works dot com,

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Speaker 1: sitting across from me as usual as senior writer Jonathan Strickland.

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Speaker 1: Hey there, do you think people can identify our voices

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Speaker 1: pretty easily? Well, I would imagine, so we don't sound

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Speaker 1: exactly alike or anything at all alike, really, I know,

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Speaker 1: But I mean, do you think people could listen to us,

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Speaker 1: people who have listened to the podcast before and say,

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Speaker 1: I know that's Jonathan and that's Chris. Possibly, although I

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Speaker 1: have heard at least one person claim after we did

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Speaker 1: a phone interview that I was the only person who

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Speaker 1: sounded the way I did on the podcast. But you

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Speaker 1: know what makes you look taller. You were sitting further

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Speaker 1: You were sitting further away from the phone, and it

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Speaker 1: was on speaker phone at the time, so that may

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Speaker 1: have played apart. But um, what we're getting to here

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Speaker 1: is kind of working our way slowly around to the

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Speaker 1: topic we're gonna discuss today. But that actually comes courtesy

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Speaker 1: of a little listener mail. This listener mail comes from

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Speaker 1: Mason in Iowa, and he says, Hey, there, love the podcast.

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Speaker 1: How about an episode on how Shazam and Medomi work?

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Speaker 1: How does the program segment compare against a database and

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Speaker 1: return result? It's so darn quickly, especially when I'm the

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Speaker 1: one doing the singing in Medomi's case. Well, Mason, first

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Speaker 1: of all, I should I should point out I would

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Speaker 1: be remiss if I did not point out that we

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Speaker 1: have a sister podcast called Stuff from the B Side,

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Speaker 1: and they actually did an episode about this kind of software. However,

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Speaker 1: we're gonna tackle it ourselves because we actually tend to

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Speaker 1: cover the same topics now and then we've we've done

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Speaker 1: both done the electric guitar, so I see no reason

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Speaker 1: for us not to uh tackle this one. Sure, and

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Speaker 1: there's uh you know, we can add a little stuff

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Speaker 1: that they didn't add. Sure, yeah, like puns that in

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Speaker 1: some updates about what's going on. Oh oh yeah, we

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Speaker 1: can do that too. So let's how about which one

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Speaker 1: do you us start with? Zam or Medomi? Um? Well,

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Speaker 1: she Zam has been around longer, that's true. She Zam

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Speaker 1: has been around since the early two thousands. Like two

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Speaker 1: thousand two was when it first the company first started.

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Speaker 1: In two thousand thirty was really when the service started

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Speaker 1: to get some attention. Actually I had it down as

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Speaker 1: sam starting in two thousand really wow, okay, so I

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Speaker 1: stand corrected. Yeah, the the earliest version of Shazam was

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Speaker 1: an interesting version. You would you would hold your cell

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Speaker 1: phone up to the source of music and you would

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Speaker 1: uh send that to the Shazam service, and you would

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Speaker 1: get a text message back identifying the song. And I

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Speaker 1: can see why this could be considered like some sort

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Speaker 1: of weird magic. I mean, how could a service figure

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Speaker 1: that out so quickly because it was usually just a

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Speaker 1: matter of a few seconds between when you sent the

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Speaker 1: the music and when or when you held the phone

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Speaker 1: up to the music source and when you got the reply.

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Speaker 1: Oh it's magic, you know, right. Uh, you have to

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Speaker 1: believe we are magic. We can do this. You appreciate

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Speaker 1: that you do in the Zany reference, well, yeah, you're welcome.

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Speaker 1: I was gonna go with Loving Spoonful, but even I think, Sanna,

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Speaker 1: do we already lost everybody Loving Spoonful? I don't. There's

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Speaker 1: probably three of you out there who even know who

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Speaker 1: that is. So but yeah, I think you're right. And

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Speaker 1: it's um stuff like this and in voice recognition. I

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Speaker 1: think it's one of those things that just sort of

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Speaker 1: surprises people because you don't think that the computer has

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Speaker 1: enough I don't know intelligence to figure it out. Wait,

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Speaker 1: wait a minute, that's a machine, and the machine figure

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Speaker 1: out what I was doing there. Let's talk about how

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Speaker 1: Shazam does this. Now, what Shazam does It breaks down

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Speaker 1: any recording into um, just some very simple data. They

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Speaker 1: call it fingerprinting. They fingerprints songs. And if you were

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Speaker 1: to if you were to try and if you were

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Speaker 1: to try and and and chart out a song from

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Speaker 1: start to finish with all the different elements that go

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Speaker 1: into that, like you know, you're you're essentially assigning data

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Speaker 1: points to every single frequency in that song, you would

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Speaker 1: end up with a fairly substantial amount of data yeah,

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Speaker 1: and Shazam has something like one point seven one point

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Speaker 1: eight million songs in the database, probably even more than

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Speaker 1: that now because that was the most recent data I

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Speaker 1: saw too, but I think that data was yeah. Yeah.

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Speaker 1: So what sasam does is they take the peaks and

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Speaker 1: the troughs, the highest points in the frequency and the

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Speaker 1: lowest points of the frequency to map out patterns and

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Speaker 1: the fingerprint songs in that way. So they're cutting out

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Speaker 1: a lot of the information in the middle and uh.

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Speaker 1: That actually saves lots of space. It also saves time

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Speaker 1: when you're trying to match one song or or one

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Speaker 1: clip to a database full of songs. So when you

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Speaker 1: hold your phone up to a SEV speakers or uh,

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Speaker 1: you know whatever whatever is making the music, um, it

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Speaker 1: starts to uh. It sends that clip to the Shazam service,

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Speaker 1: which then immediately analyzes it and looks at those peaks

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Speaker 1: and troughs and tries to find a match in the database. Now,

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Speaker 1: the reason why it's so fast is it Shazam keeps

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Speaker 1: all of this in the computer's memory. It's not stored

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Speaker 1: on a hard drive. The the database doesn't have to

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Speaker 1: you know, the process sucsessor doesn't have to search the

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Speaker 1: hard drive to find a match. Everything's in memory, which

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Speaker 1: means they have to have lots and lots of machines

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Speaker 1: to uh to be able to hold all those songs

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Speaker 1: within computer memory, or at least the fingerprints of those songs.

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Speaker 1: And so once it finds a match, it then sends

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Speaker 1: that data back to you, and usually it's it's fairly accurate.

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Speaker 1: It's especially accurate for more modern music, the stuff that's

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Speaker 1: been out for the last couple of years. Um, if

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Speaker 1: you start going back further, you may start encountering songs

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Speaker 1: that just aren't not in the database, and so you

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Speaker 1: won't get a legitimate match. And there are cases where

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Speaker 1: if you have a song that is very similar to

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Speaker 1: another song, it may come back with an incorrect match.

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Speaker 1: But that doesn't happen that often. I suppose if you

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Speaker 1: were to, you know, try and identify a Creed or

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Speaker 1: Nickelback song, it would might come back wrong because all

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Speaker 1: those damn songs sound the same to me. I mean, seriously, kids,

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Speaker 1: find some better music, all right, So get off his lawn, Yes,

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Speaker 1: get off my lawn. While I listened to, you know,

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Speaker 1: music that they made in the good old days, like

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Speaker 1: the late nineteen seventies in London. Um, like the sex pistols.

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Speaker 1: So not that not that modern stuff that's just junk

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Speaker 1: as an email to anyway, there's there are they're actually

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Speaker 1: papers online. They go into very great to tail about

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Speaker 1: the algorithms that they that Shazam uses to identify to

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Speaker 1: match up songs and and to send the data back

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Speaker 1: to you. Yeah, they go into quite a bit of

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Speaker 1: depth and as our actually as our sister podcast pointing out,

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Speaker 1: as uh John and Mark talked about, they used a

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Speaker 1: three dimensional graph to do this. Yeah, it's kind of cool. Yeah,

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Speaker 1: it's called a uh, well you can call it a

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Speaker 1: couple different things of time frequency graph where a spectrogram, um, sorry,

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Speaker 1: spectrogram and uh, basically it's a it's three dimensional and

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Speaker 1: that it goes it shows you over time how the

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Speaker 1: you know, theeks and valleys of the song frequencies change, um.

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Speaker 1: And that's kind of interesting that that it can that

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Speaker 1: it's able to do that in the first place. But

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Speaker 1: that's how it's you know, that's how it's looking at it,

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Speaker 1: and that's having the element of time in there is

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Speaker 1: crucial because otherwise, um, you know, wouldn't be able to

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Speaker 1: uh look at that Yeah. The the cool thing about

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Speaker 1: this services that you can hold your phone up to

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Speaker 1: any point of the song and and as long as

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Speaker 1: you're able to get about fifteen seconds worth of of

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Speaker 1: of content, then that's enough for sam to work with

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Speaker 1: to find a match. So it doesn't have to be

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Speaker 1: the beginning of the song, it doesn't have to be

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Speaker 1: the end. It can be at any point, and as

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Speaker 1: long as it is able to map out those troughs

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Speaker 1: and valleys accurately, then you should be able to get

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Speaker 1: a pretty good result. There are some things that can

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Speaker 1: cause some problems besides the fact that some songs do

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Speaker 1: sound alike. I mean that I was kind of making

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Speaker 1: a joke there, but that could happen if you had

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Speaker 1: if you're holding up a phone to a part where

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Speaker 1: there was it was sampling another song and it was

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Speaker 1: long enough, you could conceivably get the wrong result. But

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Speaker 1: also if there's ambient sound that's interfering in the area,

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Speaker 1: you may not be able to get a result because

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Speaker 1: you know, it can't identify the song because it's getting

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Speaker 1: all these other sound inputs that are mixing it up. Yeah,

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Speaker 1: I mean that's one of the things they sort of advertise.

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Speaker 1: Wishes am is that you could take your phone around

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Speaker 1: the mall, for example, or you know, on the radio

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Speaker 1: in your car, um and hold it up to the

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Speaker 1: speaker and it'll tell you what song that is. Well,

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Speaker 1: I mean, if you're at the mall, you're dealing with

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Speaker 1: all those people at the mall, You're dealing with perhaps

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Speaker 1: conflicting songs coming from different places. We're acoustics, the distance

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Speaker 1: from you to the speaker. I mean, there are all

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Speaker 1: kinds of things can interfere with that. Not to mention

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Speaker 1: your cell phone frequence frequency, you know, cutting out suddenly

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Speaker 1: you're yes, so I've uh yeah, I thankfully there was

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Speaker 1: a visual queue there. You guys don't get to see

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Speaker 1: because this is an audio podcast. Brows did go up

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Speaker 1: about a foot. That was nice. I was like, I know,

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Speaker 1: I'm not supposed to talk now. Um No, I've used

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Speaker 1: this this application several times and uh and I have.

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Speaker 1: I've had varying degrees of success. There's a a theater

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Speaker 1: I go to on a fairly regular basis at a

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Speaker 1: stage play theater, not a cinema theata. I got to

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Speaker 1: the Theatah fairly regularly actually, and they have some They

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Speaker 1: have a pretty cool mix of of pre show and

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Speaker 1: post show music that they play on the sound system,

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Speaker 1: And there have been a couple of times where there's

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Speaker 1: been songs that I played that I just I didn't recognize,

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Speaker 1: and Shazam is pretty good at identifying those. The hard

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Speaker 1: part is being able to get a long enough section

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Speaker 1: where people aren't talking loud so loudly that it's interfering

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Speaker 1: with the sample, right, So there have been times where

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Speaker 1: I've stood next to the speaker and held my phone

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Speaker 1: up to it. But now there's problems with that too,

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Speaker 1: because if the volume is too loud, then you get

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Speaker 1: some distortion and then it doesn't really work in that

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Speaker 1: way either. But also there's this incredibly obscure Japanese song

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Speaker 1: that they play that Shasam just doesn't seem to recognize.

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Speaker 1: So I'm gonna have to break down and ask someone

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Speaker 1: at the theater what the heck it is. Yeah, and

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Speaker 1: then there's the the other factor, you know, the part

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Speaker 1: where you're standing there holding your cell phone up to

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Speaker 1: a speaker and people are starting to wonder what the

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Speaker 1: heck you're doing. But I mean, if I start worrying

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Speaker 1: about what people think of me, now, I mean, come on,

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Speaker 1: I've made it this far, why should I worry now?

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Speaker 1: So let's let's kind of shift over a little bit

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Speaker 1: and talk about Medomi, which is a little different from Shasanna.

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Speaker 1: There's actually a few interesting uh and and fairly major differences,

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Speaker 1: one of which is that the database that that Medomi

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Speaker 1: uses is not it's not collected by the company necessarily,

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Speaker 1: it's user jim rated. Users of Medomi have sung songs

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Speaker 1: or hummed songs or whistled songs into their phones and identified,

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Speaker 1: you know, tagged the file as being a certain song,

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Speaker 1: and the user community tends to comment on these, vote

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Speaker 1: him up or down? Uh, submit their own version. Yeah, yeah,

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Speaker 1: what are you kidding? That doesn't sound like that. Yeah,

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Speaker 1: here's my version, because god lord, the one that's in

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Speaker 1: the database is terrible. And you could you could sing

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Speaker 1: your own version and upload it to medomie and then

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Speaker 1: people can vote on which one is the more representative

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Speaker 1: version of the song. The wiki model, right, and the

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Speaker 1: idea behind Medomi is that, you know, with Shazam, you

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Speaker 1: really need the original source or you need you need

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Speaker 1: to be able to hold the phone up to something

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Speaker 1: that's playing the actual song. You can't necessarily sing it yourself,

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Speaker 1: or even if there's a band playing it live. That

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Speaker 1: doesn't necessarily mean it's going to be able to identify

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Speaker 1: the song because it's looking for a specific pattern, and

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Speaker 1: depending on the way you sing it or the way

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Speaker 1: the band sings it or whatever, it may not match

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Speaker 1: up to anything in their database, even though the song

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Speaker 1: itself maybe in the database. Because of this fingerprint technology,

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Speaker 1: Madomi is different. You know, it may be able to

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Speaker 1: track your the way you sing the song and find

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Speaker 1: a match within its user generated database. Now, the way

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Speaker 1: Medomi explains that the how this works is a little

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Speaker 1: um vague, I guess you could say, I'd say, okay,

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Speaker 1: So in general, what happens is you submit the song

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Speaker 1: and whatever format that you've chosen, whether it's whistling, singing, humming, whatever.

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Speaker 1: The software they use converts this into a special length

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Speaker 1: computer language they call Crystal language. Um, it's actually computer

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Speaker 1: language is probably the wrong term, but it's their own

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Speaker 1: proprietary format format exactly. And so then it looks into

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Speaker 1: the database and sees if there are any files that

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Speaker 1: have a similar, uh similar style to what you just

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Speaker 1: sang or hummed or whistled or whatever, and then it

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Speaker 1: gives you a selection of songs that most likely are

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Speaker 1: going to fit what you sang. I say most likely

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Speaker 1: because um, it's this. Yes, I've actually played with Medomie.

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Speaker 1: Now I don't as far as I know, Medomi doesn't

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Speaker 1: have an Android application. They may have it now, but

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Speaker 1: at the time when I first heard of it, they

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Speaker 1: did not have an Android application, so I don't have

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Speaker 1: it on my phone. But you can use the service

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Speaker 1: on the web. You can use it with a computer,

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Speaker 1: so as long as your computer has a microphone, you

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Speaker 1: can give it a try. And I decided to do this,

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Speaker 1: and the first couple of songs I tried it was

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Speaker 1: surprisingly very accurate. You know, I am not a good singer,

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Speaker 1: as I'm sure many of you can imagine, my singing

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Speaker 1: ability is is very poor. Um. I can do character

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Speaker 1: voices for certain musicals and that's it. But I decided

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Speaker 1: to give it a shot, and the first couple of

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Speaker 1: songs I tried came out pretty accurate. I think the

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Speaker 1: first one I did was uh, Sedated by the Ramans

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Speaker 1: and I want to be Sedated, and it came came

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Speaker 1: back right away. But the more I tried, the more

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Speaker 1: I was encountering um anomalies, or maybe maybe the correct

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Speaker 1: one was the anomaly for me. But I was having

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Speaker 1: issues where I would sing something and I would get

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Speaker 1: a result that was totally not what I was singing.

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Speaker 1: The most egregious of these would be Blue Oyster Cults

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Speaker 1: classic hit Don't Fear the Reaper. Uh My version, apparently

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Speaker 1: to Medomi, sounds an awful lot like the girl from Ipanema.

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Speaker 1: There wasn't nearly enough cow bell. No. I clearly was

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Speaker 1: lacking the cowbell, and therefore Medomi was unable to understand

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Speaker 1: what it was. But uh so, but if I had

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Speaker 1: think of it this way, if I had recorded Don't

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Speaker 1: Fear the Reaper and submitted it to Medomi and then

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Speaker 1: tried to do it again, it would have identified my

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Speaker 1: version of Don't Fear the Reaper as being correct. There

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Speaker 1: you go, so that that's not, you know, difficult at all,

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Speaker 1: just because they accord all your own songs, so that

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Speaker 1: you can go and go, oh, what's the name of

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Speaker 1: that song that I always yeah, exactly, Well, that that

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Speaker 1: would more It would mostly be to impress or distress

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Speaker 1: your friends, because you could sing a song that sounds

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Speaker 1: nothing like the original, but it would come back and

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Speaker 1: know what it was you were singing, because you were

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Speaker 1: the one who provided the template for that song. Do

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00:16:37,200 --> 00:16:40,760
Speaker 1: you see that that seems problematic for me? Well, because

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Speaker 1: it seems like you could intentionally go in and sing

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Speaker 1: a whole bunch of stuff and tag it and just

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Speaker 1: drive people nuts. Well that's that's why you know, it's

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00:16:49,360 --> 00:16:53,920
Speaker 1: it's user user police, so you have to it's one

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Speaker 1: of those services that depends heavily upon the community of users.

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Speaker 1: If the community is being very uh you know, honest

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Speaker 1: and forthright, then they are going to police the different

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Speaker 1: submissions and make sure that only the ones that actually

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Speaker 1: and accurately represent the songs are the ones that make

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Speaker 1: it into the database. Otherwise, uh, you know, they essentially say, well,

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Speaker 1: this is someone who's just goofing around and trying to

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Speaker 1: cause problems, and they'll they'll nix it, right. So, um,

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00:17:26,040 --> 00:17:29,080
Speaker 1: but yeah, I mean, anytime there's a user, anytime there's

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Speaker 1: a service that depends upon the community of users to

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Speaker 1: to keep it going, it makes me a little nervous

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Speaker 1: because you know, you never know when a group of

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Speaker 1: people is just gonna get a little capricious and decide

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Speaker 1: that you know, they that every song needs to be

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Speaker 1: sung to the tune of the Yellow Rose in Texas

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Speaker 1: and why not. Yeah. So, uh as for the new information, well,

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Speaker 1: I mean I didn't know if we were. That's pretty

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Speaker 1: much the way that those and again it's it's referring

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Speaker 1: to a database. It's sending you the results pretty quickly. Um.

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Speaker 1: But they they don't Madonie doesn't share as much of

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00:18:08,400 --> 00:18:12,520
Speaker 1: it's it's uh back end operations as Shazam does, so

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Speaker 1: we can't for sure. Men say that they use the

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Speaker 1: same sort of setup where they you know, everything stored

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Speaker 1: in memory as opposed to hard drive space or whatever.

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Speaker 1: We just don't know because we aren't privy to that information.

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Speaker 1: But um, yeah, that's that's all I have on just

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Speaker 1: the basic operations. So what's this new info you've well, um,

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Speaker 1: you know, in in in general, Jasam basically offers the

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Speaker 1: application for free for you know, iPhones and Nokia phones. Um.

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Speaker 1: But actually, in the as of the ninth don't know,

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Speaker 1: late middle part of October two thousand nine when we're

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Speaker 1: recording this, um Chasm actually just got a new round

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Speaker 1: of funding from Kleiner Perkins, call Field and Buyers. They

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00:18:56,840 --> 00:19:03,119
Speaker 1: actually have their own iPhone Apple Cation fund called the

334
00:19:03,160 --> 00:19:05,720
Speaker 1: I fund this hundred million dollars and uh, you know

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00:19:05,760 --> 00:19:09,639
Speaker 1: it's specifically geared towards iPhone developers. Well, Shasam got some

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00:19:09,680 --> 00:19:12,280
Speaker 1: of that money, and there, um they're going to continue

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00:19:12,320 --> 00:19:16,120
Speaker 1: to offer their services for free for a while. Uh. Well,

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00:19:16,119 --> 00:19:18,840
Speaker 1: they actually charge for the BlackBerry version, but the iPhone

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00:19:18,920 --> 00:19:21,680
Speaker 1: and Nokia Phones version. I don't think they charge for Android,

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00:19:21,720 --> 00:19:24,119
Speaker 1: or if they have, I am totally unaware of it

341
00:19:24,480 --> 00:19:27,040
Speaker 1: because I have Shazam for the Android phone and and

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00:19:27,720 --> 00:19:30,360
Speaker 1: if they're charging me for it, I need to pay

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00:19:30,400 --> 00:19:32,480
Speaker 1: better attention to my bills. Well, you need to pay

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00:19:32,520 --> 00:19:34,080
Speaker 1: attention to your bills by the end of the year

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00:19:34,160 --> 00:19:37,360
Speaker 1: in two thousand nine, because um, there you will start

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Speaker 1: to get five free song identifications a month, and then

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00:19:40,480 --> 00:19:43,440
Speaker 1: four nine a month after that if you want unlimited

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Speaker 1: usage and all the extra goodies. And they're talking about

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Speaker 1: selling application or i'm sorry, selling items, uh you know

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Speaker 1: as part of the application to like you know, banned

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00:19:53,680 --> 00:19:58,120
Speaker 1: gear and you know, possibly selling video. I'm pretty sure

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00:19:59,200 --> 00:20:01,879
Speaker 1: you know what. I can't be certain now, but I could.

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00:20:02,560 --> 00:20:06,320
Speaker 1: I seem to recall the last time that Sasam correctly

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00:20:06,359 --> 00:20:08,400
Speaker 1: identified a song for me. It gave me a link

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00:20:08,440 --> 00:20:13,240
Speaker 1: to where I could purchase the album on Amazon. But

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00:20:13,280 --> 00:20:15,639
Speaker 1: maybe maybe I've got that mistaken now because it has

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00:20:15,680 --> 00:20:17,240
Speaker 1: been a while since I've used it and had it

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00:20:17,320 --> 00:20:21,080
Speaker 1: actually work. Because it turns out that most music that

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00:20:21,119 --> 00:20:24,840
Speaker 1: I really like, I already know what the song is. Yeah,

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00:20:24,920 --> 00:20:27,879
Speaker 1: it's just the really really obscure stuff that tends to

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Speaker 1: like I'm like, wow, that's so cool. I've never heard

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Speaker 1: that before, and apparently neither has Shazam, so I haven't

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Speaker 1: had much call to use it recently. I don't have

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Speaker 1: a Nokia phone, and I don't have an iPhone or

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00:20:38,960 --> 00:20:41,359
Speaker 1: an Android or BlackBerry um, but I do have the

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Speaker 1: iPod and I could download Susanne. But I have the

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00:20:44,560 --> 00:20:48,360
Speaker 1: first generation iPod Touch, and so I could stand there

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Speaker 1: all day holding up my microphone lists iPod Touch up,

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00:20:52,000 --> 00:20:55,960
Speaker 1: and She'sam's just gonna go anytime? You just just I'll

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Speaker 1: be waiting. Actually, I could use a microphone that ugs in,

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00:21:00,560 --> 00:21:04,320
Speaker 1: but you know that costs money, right, and then you

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Speaker 1: don't and you have to carry around an extra pace

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00:21:07,000 --> 00:21:09,200
Speaker 1: of gear and you have to be someplace where there's

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Speaker 1: WiFi for you to be able to actually indeed, you

375
00:21:12,320 --> 00:21:16,280
Speaker 1: know that could be problematic that's a lot of qualifiers

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00:21:16,320 --> 00:21:18,600
Speaker 1: for a single application. So maybe I'll invest in a

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00:21:18,640 --> 00:21:22,040
Speaker 1: smartphone eventually. Yeah, that would um, that would be my recommendation.

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00:21:22,200 --> 00:21:25,280
Speaker 1: You know, there's a there's this awesome new phone called

379
00:21:25,280 --> 00:21:29,479
Speaker 1: the Motorola Droid. Have you heard about it? I do

380
00:21:29,560 --> 00:21:33,679
Speaker 1: think I have. It's kind of hard not to, um

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00:21:33,720 --> 00:21:36,439
Speaker 1: so at any rate. Yeah, that's that's interesting stuff. Uh,

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00:21:36,960 --> 00:21:40,120
Speaker 1: I mean, I think these these services are really cool ideas,

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Speaker 1: especially for people who happen to be out and about

384
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Speaker 1: a lot and they hear a lot of you know,

385
00:21:44,800 --> 00:21:47,919
Speaker 1: encountering a lot of new music. Um. It's it's a

386
00:21:48,000 --> 00:21:52,320
Speaker 1: really interesting way to to to educate yourself about stuff

387
00:21:52,359 --> 00:21:54,600
Speaker 1: that you like that you know, you you encounter, but

388
00:21:54,640 --> 00:21:59,080
Speaker 1: you don't necessarily you're not really familiar with it. Um.

389
00:21:59,119 --> 00:22:00,760
Speaker 1: I mean, there are a lot of other options to Like,

390
00:22:00,800 --> 00:22:03,480
Speaker 1: there are a lot of like HD radio stations which

391
00:22:03,480 --> 00:22:06,280
Speaker 1: will identify the song that you are listening to. And

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00:22:06,320 --> 00:22:09,720
Speaker 1: there's some HD radio applications where you can even get

393
00:22:09,760 --> 00:22:12,880
Speaker 1: access to buying the song off of something like Amazon

394
00:22:12,960 --> 00:22:16,960
Speaker 1: or iTunes. So uh yeah. Of course satellite radio also

395
00:22:17,200 --> 00:22:20,440
Speaker 1: identify as those and um, you know there are other

396
00:22:21,280 --> 00:22:23,919
Speaker 1: and this really isn't all that especially new. You know,

397
00:22:24,040 --> 00:22:27,440
Speaker 1: Grace Note alias c d dB, you know, has been

398
00:22:27,480 --> 00:22:32,480
Speaker 1: identifying songs mathematically, uh, you know, based on identification numbers,

399
00:22:32,480 --> 00:22:34,159
Speaker 1: and at least, you know, it's a little bit different

400
00:22:34,160 --> 00:22:37,760
Speaker 1: from the way that Shazam and Medomi do. And then

401
00:22:37,800 --> 00:22:40,520
Speaker 1: there's my buddy John, who seems to know every song

402
00:22:40,600 --> 00:22:43,159
Speaker 1: ever recorded. So I'll be like, hey, John, what's that

403
00:22:43,240 --> 00:22:46,200
Speaker 1: song that goes has He'll be like, oh, that's such

404
00:22:46,240 --> 00:22:48,760
Speaker 1: and such by the so and so's and so. Yeah.

405
00:22:48,800 --> 00:22:51,960
Speaker 1: If you if you don't have access to Shazam or Medomi,

406
00:22:52,119 --> 00:22:54,959
Speaker 1: you should totally call my buddy John because he probably

407
00:22:54,960 --> 00:22:58,600
Speaker 1: knows a song. Yeah, you know. And I bet that somewhere, uh,

408
00:22:58,760 --> 00:23:01,760
Speaker 1: in our brains there is a mathematical algorithm going on

409
00:23:01,960 --> 00:23:05,960
Speaker 1: to converts those bits of information you have to be

410
00:23:06,000 --> 00:23:08,520
Speaker 1: able to recognize something. Well, yeah, you think about the

411
00:23:08,520 --> 00:23:12,040
Speaker 1: brain is pretty remarkable because you can recognize a song

412
00:23:12,160 --> 00:23:14,359
Speaker 1: even if someone is mangling it. Yeah, you know, it

413
00:23:14,400 --> 00:23:17,480
Speaker 1: doesn't you may not sound anything like the original song,

414
00:23:17,600 --> 00:23:20,879
Speaker 1: but because we're able to recognize that, we can say, hey,

415
00:23:21,160 --> 00:23:23,520
Speaker 1: that song is as you know, such and such like,

416
00:23:24,119 --> 00:23:26,359
Speaker 1: or you can always do my favorite thing, which is Hey,

417
00:23:26,400 --> 00:23:28,680
Speaker 1: who sings that song? Oh that would be the Beatles. Yeah,

418
00:23:28,840 --> 00:23:33,199
Speaker 1: let them do it if nice. Um no, I'm a

419
00:23:33,320 --> 00:23:37,720
Speaker 1: I'm a kind guy. But yeah, I mean, you know,

420
00:23:37,760 --> 00:23:42,960
Speaker 1: they say that kids who play a musical instrument are

421
00:23:43,160 --> 00:23:45,119
Speaker 1: a little better at math. You know, it's sort of

422
00:23:45,160 --> 00:23:48,040
Speaker 1: there's a connection between the two. So I don't know,

423
00:23:48,080 --> 00:23:52,200
Speaker 1: maybe there's a thing. I'm I'm a pretty mean hand

424
00:23:52,200 --> 00:23:56,480
Speaker 1: with the slide whistle on and not no, not with

425
00:23:56,520 --> 00:24:01,199
Speaker 1: the slide rule, lack at ish sh mac and you do.

426
00:24:01,560 --> 00:24:03,800
Speaker 1: Oh there you go. See those are the kinds of

427
00:24:03,880 --> 00:24:06,560
Speaker 1: lame jokes that we bring. That's what we bring to

428
00:24:06,600 --> 00:24:08,240
Speaker 1: the table that you just aren't going to get from

429
00:24:08,240 --> 00:24:10,080
Speaker 1: stuff from the B sides. Yeah, you will get some

430
00:24:10,280 --> 00:24:13,359
Speaker 1: very valuable information about music from stuff on the B

431
00:24:13,440 --> 00:24:18,160
Speaker 1: sides and instruments, you won't get really horrible jokes. So

432
00:24:18,960 --> 00:24:22,040
Speaker 1: thank them for that. Yeah, yeah, definitely, Um so yeah

433
00:24:22,080 --> 00:24:24,239
Speaker 1: they If you want to learn more about it, I

434
00:24:24,280 --> 00:24:27,000
Speaker 1: do recommend listening to that podcast. It's you know, you'll

435
00:24:27,040 --> 00:24:28,679
Speaker 1: have to dig back in the archives a little bit

436
00:24:28,720 --> 00:24:32,680
Speaker 1: to fink it's from February. Yeah, it's been quite a bit,

437
00:24:32,720 --> 00:24:35,840
Speaker 1: but shorter podcasts and yeah, that was a group that

438
00:24:35,920 --> 00:24:40,000
Speaker 1: was back when we would we would or ten minute podcasts. Yeah,

439
00:24:40,280 --> 00:24:42,520
Speaker 1: but um, yeah, if you want to learn more about it,

440
00:24:42,600 --> 00:24:45,320
Speaker 1: I would recommend listening to that podcast, and if you

441
00:24:45,359 --> 00:24:47,720
Speaker 1: haven't already, you should subscribe to it because it's a

442
00:24:47,760 --> 00:24:51,920
Speaker 1: good show. Yeah, definitely. So they also covered some some

443
00:24:52,040 --> 00:24:56,399
Speaker 1: great classical music, um topics that I had never really

444
00:24:56,560 --> 00:24:59,000
Speaker 1: explored before. So I learned quite a bit from that show,

445
00:24:59,320 --> 00:25:03,879
Speaker 1: like classical, like in all the way back to the sixties. Yeah,

446
00:25:04,160 --> 00:25:10,040
Speaker 1: the seventeen sixties. Snap, all right, so let's let's let's

447
00:25:10,040 --> 00:25:12,840
Speaker 1: find this. Let's find this down a little bit. Let's

448
00:25:12,880 --> 00:25:15,560
Speaker 1: take this down on notch. Let's let's end with a

449
00:25:15,560 --> 00:25:22,720
Speaker 1: little listener mail is listener mail comes from my good

450
00:25:22,720 --> 00:25:25,960
Speaker 1: friend Ry Adney. She wrote to me and she said,

451
00:25:26,240 --> 00:25:30,840
Speaker 1: I'm hearing your Touch Screens podcast now and the HTC

452
00:25:31,040 --> 00:25:34,800
Speaker 1: Hero for Sprints supports multi touch. I've been crushing on

453
00:25:34,840 --> 00:25:37,159
Speaker 1: this phone for a while, but can't bring myself to

454
00:25:37,280 --> 00:25:41,520
Speaker 1: ditch my Palm Centro just yet. So yeah, we were

455
00:25:41,520 --> 00:25:43,399
Speaker 1: talking about, you know, the multi touch and how Apple

456
00:25:43,480 --> 00:25:48,399
Speaker 1: had had patented the multi touch technology. Um well as

457
00:25:48,440 --> 00:25:52,600
Speaker 1: long as as Google and HTC are creating a multi

458
00:25:52,640 --> 00:25:56,639
Speaker 1: touch system that doesn't infringe on that patent. They're fine

459
00:25:57,080 --> 00:25:59,400
Speaker 1: if they found a different way of doing it otherwise

460
00:25:59,400 --> 00:26:03,760
Speaker 1: they could might they may see a letter from Apple's lawyers.

461
00:26:03,880 --> 00:26:06,119
Speaker 1: It's not that Apple is known to be litigitus or anything,

462
00:26:06,880 --> 00:26:11,520
Speaker 1: not at all. And on that happy note, let's wind up, guys,

463
00:26:11,600 --> 00:26:14,760
Speaker 1: thank you for listening. Remember we have tech Stuff Live.

464
00:26:14,840 --> 00:26:18,160
Speaker 1: It's a show that's live every Tuesday, one pm Eastern.

465
00:26:18,200 --> 00:26:20,280
Speaker 1: You can find that at the house stuff works dot

466
00:26:20,280 --> 00:26:22,600
Speaker 1: com blogs. Let's just go to the house stuff works

467
00:26:22,640 --> 00:26:25,640
Speaker 1: dot com home page. Look on the right side. You'll

468
00:26:25,640 --> 00:26:29,120
Speaker 1: find links to the blogs there and you'll just look

469
00:26:29,160 --> 00:26:31,679
Speaker 1: for the one that says watch tech stuff Live or

470
00:26:31,720 --> 00:26:35,200
Speaker 1: some variation thereof, because that's where you can see our show.

471
00:26:35,720 --> 00:26:37,920
Speaker 1: And uh, we're starting to get some really good feedback

472
00:26:37,960 --> 00:26:40,560
Speaker 1: on it, but we need more more viewers. So I

473
00:26:40,920 --> 00:26:43,960
Speaker 1: know that one pm you might be working or in class,

474
00:26:44,080 --> 00:26:46,320
Speaker 1: you know, just find a quiet corner that you can

475
00:26:46,440 --> 00:26:49,240
Speaker 1: hide in and watch and you'll love it. You'll love

476
00:26:49,280 --> 00:26:51,360
Speaker 1: It's the best use of twenty two minutes of your

477
00:26:51,359 --> 00:26:55,800
Speaker 1: time right there. Yeah. Also, remember that we both contribute

478
00:26:55,840 --> 00:26:58,320
Speaker 1: to the blogs at how softwork dot com. We I

479
00:26:58,359 --> 00:27:02,280
Speaker 1: write articles occasionally for the website uh thus List Senior

480
00:27:02,280 --> 00:27:05,160
Speaker 1: writer title. I'll there you go, so visit how stuff

481
00:27:05,160 --> 00:27:07,159
Speaker 1: Works dot com and Chris and I will talk to

482
00:27:07,160 --> 00:27:12,800
Speaker 1: you again really soon. For more on this and thousands

483
00:27:12,800 --> 00:27:15,720
Speaker 1: of other topics, visit how stuff Works dot com and

484
00:27:15,800 --> 00:27:17,680
Speaker 1: be sure to check out the new tech stuff blog

485
00:27:17,880 --> 00:27:24,840
Speaker 1: now on the House Stuff Works homepage, brought to you

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00:27:24,880 --> 00:27:28,240
Speaker 1: by the reinvented two thousand twelve camera. It's ready, are

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00:27:28,280 --> 00:27:28,440
Speaker 1: you