Apple Buys Shazam to Boost Apple Music(bloomberg.com) |
Apple Buys Shazam to Boost Apple Music(bloomberg.com) |
Fact that Shazam is 18-years-old made me curious, and found the following on Wikipedia:
>> “Initially, in 2002, the service was launched only in the UK and was known as "2580", as the number was the shortcode that customers dialled from their mobile phone to get music recognised. The phone would automatically hang up after 30 seconds. A result was then sent to the user in the form of a text message containing the song title and artist name.”
There was another service around the same time called Any Question Answered. This was before high quality internet on phones, and you could SMS them reasonably complicated questions and (at first) get good replies. Notable successes were their getting me ownership information for a pub, and telling me which local shops had an iPad in store. Service degraded significantly over time.
It goes to show how the switch from radio (station directed programming) to streaming (user directed programming) has put a huge crimp in music discovery and music promotion.
Music is at my fingertips from a variety of apps. My biggest problem is discovering new artists or songs.
[1] https://www.macrumors.com/2016/04/20/shazam-for-brands-user-...
[2] https://www.billboard.com/articles/business/7526322/shazam-1...
Even if selling data and/or advertising, do they need (say) a 200 people sales team.. ?
http://coding-geek.com/how-shazam-works/ https://news.ycombinator.com/item?id=9870408
My guess is it supplements the data with songs from your Google Music and youtube history.
If the last round had a strong liquidity preference then it wasn't really valued at $1 billion, and those investors might have even come out ahead.
This feels like a natural acquisition to compete w/ some of Googles offering w/ the latest Pixel 2.
The fact Shazam is 18 years old is crazy. Pre-dates "apps" with the "2580" service and was one of the first apps on the iPhone.
I don't want to be anti-apple because I like most of their stuff but iTunes is complete garbage compared to Spotify.
With the stuff they have now they will never catch up.
I suspect it’s due to end soon, and they realised once it’s gone they would just become a feature of music streaming services. Good to get out now while there is still some exclusivity for Apple to milk.
Hoping this doesn't mean there service is degrading because I've really benefited from it over the years.
I used to use it to identify music in shows or soundtracks, but it started just saying "Breaking Bad Episode 4" which while more accurate was less helpful.
Shortly after that everyone else had music discovery natively anyway.
It's also a nice acqui-hire; Spotify already snatched Echo Nest a few years ago, so they get to catch-up with Shazam.
It's the brand, mindshare and music store/service lead gen that's more difficult to replicate. Why get rid of an icon that's already on everyone's phones that could be a funnel to apple music instead of spotify?
Just buy a month of premium ($10/month or $5 if you're a student) and try it.
Personally I think Spotify's recommendations, radio stations, and app (both mobile and desktop) are just more pleasant to use than Apple Music and iTunes.
The business is/was connecting resale opportunities to brands and artists[1] so you'll have a fairly significant sales and marketing effort although typically you will pay sales people for performance so their compensation will track revenue.
But to give you some things to think about, if you have an engineering team of 15 engineers, median salary $120K, and an 'overhead' (office, health plans, insurance, etc of 60%) then that is $200K/engineer/year (or $3M/year or $48M for 16 years [2001 - 2017]) that is just integrating cost per engineer over time using constant engineering. You can put any function in you want for head count (does it grow exponentially? does it grow in chunks? etc) and then add a C-suite team (higher median salary) and an 'overhead' team (IT, marketing, HR, etc) and you can burn through that fairly quickly.
It is a useful thing to build models for this stuff as your 'pre-operationally-cash-flow-positive' costs are really the health and future of your company.
A good app looks like it only takes a few engineers to maintain, but in likelihood there's a lot of complexity, even that's outside of the core "platform" software going on.
Salaries for - Chief Twitter Feed Monitor, Chief Assistant of The Twitter Feed monitor. The Special Secretary to The Assistant of Twitter Feed Monitor, etc etc etc
Best outcome for all in that case would be acquisition. (Just ask Flux)
This could just be Apple’s way of acquiring more patents and mind share through Shazam brand.
At the level Apple is at and the hordes of cash they have in bank, it probably makes business sense to buy Shazam just for the patents.
[1] https://www.sec.gov/Archives/edgar/data/1441816/000104746917...
[2] https://www.sec.gov/Archives/edgar/data/1576942/000119312517...
Given that Apple seems to compensate junior engineers with compensation packages north of $300k, it wouldn't be too surprising if they overpaid for Shazam too. Apple also has $74.2B in cash and short-term investments, so paying $0.6B for Shazam doesn't really move the needle.
Good catch. Cheers mate.
http://appleinsider.com/articles/14/09/19/siri-partners-with...
Note: I've never heard of soundhound though, so it might be popular in some places. Shazam is like the name-brand of music recognition though, to the extent of being a verb.
Shazam has an augmented reality based advertising platform.
Then again, it does have 48M fingerprints, so that's only ~3.3KB/fingerprint. Maybe Google has a decent subset in a reasonably-sized package.
I actually find Spotify apps to be far worse than iTunes at least on iOS. And the Apple Watch app for Apple Music is really impressive.
1. Favourites mix - i.e. the music I've played the most
2. Recently played - i.e. the music I've played recently
3. Tuesday's Playlists - the first of any real recommendations so far, but 4 of the 9 album covers it shows in the thumbnails are music I've played recently
4. Heavy Rotation - i.e. music I've played a lot, but not just recently
5. Tuesday's Albums - recommendations based on an artist (Waxahatchee) I've listened to
6. Artist Spotlight Playlists - a selection of playlists, including "Influences" and "Inspired By" playlists by artists that I don't listen to and are really unrelated to most of my collection.
7. New Releases
finally there's the wordy stuff I don't care about, social media posts.
Most of this stuff is not even bad ML (like the "Amazon recommends me vaccuum cleaners because I searched for and bought a vaccuum cleaner" problem) it is just literally showing me what I listened to. I've tried the recommended playlists a handful of times and they don't really show me much new things, they remain pretty unchanged in the weeks or so that I check them.
When you throw in the fact that they periodically delete all of the music I've downloaded, and nuked a chunk of my music collection after I signed up ... I have to say, my experience of Apple Music overall is pretty terrible.
The albums I haven't listened to in a while and might want to listen to again according to the app are those I listen to daily.
New releases are not sorted or filtered by genre, so I guess it is great that some pop or reggae artist has a new album out when I only listen to metal on Spotify?
Etc., etc... in other words - I use Spotify for totally unrelated reasons and switched from Google Play Music, but it has all the same faults, it just works better for some part of the target group, but it is in now way perfect, or even good, with regards to their recommendation engine either.
I hope Spotify fixes their security woes. Either way I have no reason to leave Apple Music now.
https://www.theatlantic.com/magazine/archive/2014/12/the-sha...
[1] https://www.ee.columbia.edu/~dpwe/papers/Wang03-shazam.pdf
(Here is Shazam in a chip from 1988:
https://www.youtube.com/watch?v=kFth9K_IvwA
Now imagine you have a magnitude better signal fidelity and 10e8 times the storage and processing power)
Asserting otherwise is an example of the etymological fallacy: https://en.wikipedia.org/wiki/Etymological_fallacy.
Yes, deep neural networks have proven remarkably useful for machine perception, but you would still need to collect a colossal amount of audio data, fingerprint all of it, build a low-latency processing infrastructure for making inferences, and convince a hundred million people to install your software to feed you copious real-world training data that you can use to improve model performance.
That's actually the easy part. You already have the music. Distorting it by superimposing background noise is really not difficult.
Shazam doesn't actually let you improve the answer, nor report incorrect guess. They are so confident with them, even if it's sometimes completely missed genre and style of music.
Also, it works a lot better than being able to find "slightly distorted" versions. It can catch a song in a noisy room where you can barely make out the song to begin with. Couple months back it found a song when there was a very loud crowd yelling over it. They're also able to determine differences between versions of songs pretty well. Some remixes might sound very close to the original.
Other thing you might be missing is just how fast it is even on a slow mobile connection.
I think describing the reaction as lack-of-appreciation is a bit misleading. Perhaps disbelief might be a better description.
The smart money, though, is on the main chance: you don't understand the purchase, or the problem domain, or both.
In this case I think you are overestimating the progress in NN and search, and underestimating the signal processing. Have you tried this with any significant corpus?
"Whack it through a FFT and do correlation " seems like one of the obvious solution to the toy problem version, but this is exactly the sort of thing that usually falls apart in practice.
Is anyone keeping a running list of products that HN commenters have suggested could be built in an afternoon/weekend?
Ones I've seen so far: Facebook, Twitter, Dropbox, and now Shazaam.
Then we'll talk.
https://www.toptal.com/algorithms/shazam-it-music-processing...
What's not straightforward is recognizing cover songs and the like. But that's not only non-trivial but AFAIK can't be done.
Well, you could translate the music into actual notes (or musical intervals), and use Smith-Waterman (or any more advanced and more recent technique) to find the song with the lowest edit-distance.
https://www.toptal.com/algorithms/shazam-it-music-processing...
I'm not disputing they overpaid. However, long to short, building the technology is the easy part, and just a fraction of the brand / product value.
"What's that song?" is a different signal to buying a song. Especially when "what's that song?" isn't restricted by licensing agreements.
Read this article, then come back: [1]
I think you just confirmed how easy (and cheap) it is to actually generate this data.
Also, even if you have many false positives, you have already narrowed down the search, and this allows you to do more brute-force searching like computing cross-correlations.
I mean, you can easily find thousands of hours of music online. Recording background noise is easy (just go to a random bar where they are not playing music). Now simply add the two signals (you can shift them randomly to generate more data). You can also add some linear filtering if you like (just imagine random settings of an equalizer for starters).
This should give you enough data to build a proof of concept at least.
Illegally grabbing thousands of hours of music to train a commercial model hardly qualifies as fair use. Any company you build upon that would be tainted.
For sustaining:
In addition, you'll need to keep an updated catalog of music to identify new songs against, and most uses of a service like shazam are to find names of songs people aren't familiar with, so that catalog needs to be very fresh.
That means you'll have to grab some sort of feed, and engage in large scale music piracy for commercial gain or have access to a library of songs from many disparate music providers, such as ascap.
Background noise:
there are literally hundreds of different background noise environments you need to train against. Dozens of common microphone configurations. Clipping, variations.
It's very much a problem where a proof of concept is neat but doesn't really get you anywhere.