While the Twitter music app eventually failed, it’s still clear that people use Twitter’s data stream to share and/or discover new #music. Thanks to Twitter cards, a great thing is that you can directly watch a YouTube video, or listen to a SoundCloud clip, right from your feed, without leaving the platform. But what if Twitter could be your own DJ, playing songs on your request?
Since it’s been a few month since I enjoyed my last Music Hack Day – oh, I definitely miss that! – I’ve hacked a proof of concept using the seevl API, combined with the Twitter and the YouTube ones, to make Twitter acts as your own personal DJ.
Hey @seevl, play something cool
The result is a twitter bot, running under our @seevl handle, which accepts a few (controlled) natural-language queries and replies with an appropriate track, embedded in a Tweet via a YouTube card. Here are a few patterns you can use:
Hey @seevl, play something like A
To play something that is similar to A. For instance, tweet “play something like New Order”, and you might get a reply with a Joy Division track in your feed.
Hey @seevl, play something from L
To play something from an artist signed on label L (or, at least, that used to be on this label at some stage)
Hey @seevl, play some G
To play something from a given genre G
Hey @seevl, play A
To simply play a track from A.
By the way, you can replace “Hey” by anything you want, as long as you politely ask your DJ what you want him to spin. Here’s an example, with my tweet just posted (top of the timeline), and a reply from the bot (bottom left).
A little less conversation
As it’s all Twitter-based, not only you can send messages, but you can have a conversation with your virtual DJ. Here’s for instance what I’ve sent first
It’s kind of fun, I have to say, especially due to the instantaneous nature of the conversation – and it even reminds IRC bots!
Unfortunately, it’s likely that the bot will reach the API rate-limit when posting Tweets (and I’m not handling those errors in the current MVP), so you may not have a reply when you interact with it.
Twitter As A Service?
Besides the music-related hack, I also wanted to showcase the growth of intelligent services on the Web – and how a platform like Twitter can be part of it, using “Twitter As A Service” as a layer for an intelligent Web.
The recently-launched “Buy button” is a simple example of how Twitter can be a Siri-like interface to the world. But why not bringing more intelligence into Twitter. What about “Hey @uber, pick me in 10 minutes”, and using the Tweet geolocation plus a Uber-API integration integration to directly pick – and bill – whoever #requested a black car? Or “Please @opentable, I’d love to have sushis tonight”, and get a reply with links to the top-rated places nearby, with in-tweet booking capability (via the previous buy button)? The data is there, the tools and APIs are there, so…
Back in April, I was lucky enough to get a partner invite for Google I/O. Coupled with a stay at the Startup House, a co-working / housing space (ideal when you’re jet-lagged at 4AM and want a proper desk to code a few meters away from your bed) located only one black away from Moscone, I’m very glad I’ve made the trip to my first I/O!
Here are a few highlights, in a conference which clearly confirmed the role of (1) Android as a global OS, and (2) the Knowledge Graph as a hub for everything AI-related, at Google and beyond.
While I’m not (yet) a full-time Android user (let alone a developer), it’s now clear that it goes far beyond a phone-only OS. With the introduction of AndroidWear, AndroidCar, and AndroidTVduring the keynote, the OS is now the core of all hardware-related initiatives at Google.
With common SDKs and API to interact with, wherever the OS is used, this makes the life of developers much easier when building cross-devices products. Relying on a single ecosystem is also of importance when building an engineering team, and I guess it may also be an decision factor for small start-ups when deciding which market to tackle.
Google’s Knowledge Graph – From search to voice controls and app indexing
So far, Google’s Knowledge Graph has been used mostly in search-related projects, including the snippets you can see when searching for entities such as places, people, music and movies on Google. Several sessions-cases showed how it is now used as a central hub for AI-related projects and products.
Using Android TV, you can ask your TV (literally, by talking to your Android watch) to suggest an Oscar-awarded movie from 2000, or who’s casting in X or Y – all answers coming from the Knowledge Graph. In the first case, results can be bought from Google play, another nice piece of integration between the different offerings from the company.
Another interesting case is the use if the Knowledge Graph to connect the dots between previously isolated silos, namely mobile apps. One of the common issue with those apps is their lack of links and outside-world connections, in spite of recent efforts such as Facebook-supported App Links. In the session “The Future of Apps and Search“, a combination of app indexing, JSON-LD and Knowledge Graph was presented to directly link into an app from, e.g., Google’s search results or autocompletion-search in Android, as well as launching actions from search results – e.g. playing a track in Spotify, a use-case announced a few days before I/O – using the new schema.org actions I’ve recently blogged about.
As an early JSON-LD enthusiast, and working on related technologies for almost a decade, you can’t imagine how excited I was when I saw this in something used by million of users! Let’s bet that’s only the beginning, and that new verticals will follow.
Google Cloud and DataFlow – Smarter, faster, easier
Cloud Debugger – making DevOps and back-engineers more efficient when debugging code. You can now add breakpoints, including conditional ones (e.g. user=X) in your live app, without jeopardising its speed, and most important, without having to stop/restart/deploy anything. This means that code can be debugged on production servers with live data, and without patching / tracing multiple boxes, all in the comfort of your browser. A kind of New Relic on steroids, so big thumbs-up here!
Dataflow – aiming to replace MapReduce, with a special focus on stream processing and scalability. A convincing use-case during the keynote was Twitter sentiment analysis, showing not only the simplicity of the interface, but also the orchestration of the services through the API. The service is not open yet, but you can check “Big data, the Cloud Way: Accelerated and simplified” to know more. I’m looking forward to try it on a few stream processing for content discovery!
The Web platform – Polymer, WebRTC and HTML5
Whether you’re accessing if from your desktop, phone, or now, your watch or Glass, there’s only one Web. And far from just websites, it can be used as a platform to build powerful apps, as many session focused on:
WebRTC – building real-time systems in your browser. “Making music mobile with the Web” not only showed how to transform your Macbook into a Marshall JCM2000 with Soundtrap, but also how WebRTC was used for real-time collaborative music creation, with very low latency.
Wearables – It’s all about the UX
Then, a big part of the conference: Glass and smart watches. I often thought that most of the effort to build those was put in the hardware and OS side of things (reducing footprint, optimising battery life, gathering sensor data, etc.).
While some talks clearly focused on this (with some nice hacks such as back-camera for biking in “Innovate with the Glass Platform“, and football-related ones), I was impressed by “Designing for wearables“, which focused on the role of UX to make sure wearables are devices that let you connect, and not interfere with the world as a phone does.
Showing some early prototypes and discussing how and why Glass / wear notifications are so minimalistic, this was an inspiring session for anyone interested in UX and products. A must-watch for developers and entrepreneurs aiming to build appealing user-facing products, whether it’s for wearables or more standard devices.
Google+ – Or how Google missed the spot
I may have missed it from other sessions, but none of the ones I’ve been to mentioned Google+. I was not expecting much about it at I/O since the departure of Vic Gundotra, and Sergey Brin’s statements, as well as a plus-free agenda. Still, that was a big surprise, as it would have been a no-brainer use-case in many talks.
Using dataflow to process streams from your social circles? Not a word about it. Using Glass to see what your friends are posting? Nope. Alerts on your Google TV to binge watch some TV-show together with your friends home 5000km away? Neither.
G+ could have been an awesome social network – or should I say a social platform. Combined with Freebase / Knowledge Graph, linking people to things they like, possibilities would be endless in terms of profiling, discovery and more. Yet, with a poor API, a lack of portability that could have differentiate it from its main competitors from Day 1 (imagine PubSubHubbub / WebSocket as an easy way to integrate G+ into other platforms), I’m sad they’ve missed the spot.
Up to 2015?
Overall, a great conference, in spite of the queue mismatch that forced me to miss about 30min of the keynote, queueing twice around the Moscone, a real shame when you travel 8000km for such an event.
The YouTube V3 API is one of those thing you’ll definitely fall in love with, if you’re into real-world Semantic Web applications, a.k.a “Things, not words”. With its integration with Freebase – the core of Google’s Knowledge Graph -, it’s a concrete and practical showcase of the Web as a distributed database of things and relations, and not only keywords and links between pages.
YouTube Data API v3 with Freebase mappings: the good, the bad, and the ugly
While relatively simple to use, it provides advanced features to let developers built data-driven applications. On the one hand, it allows to search for videos by Freebase entities, as you can try in a recent demo from YouTube themselves. On the other hand, it returns which entities are used/described in a video.
Yet, identifying topics from videos is a difficult task, and if you’re not convinced (and interested in all things Machine Learning related), check the following Google I/O talk from last year.
While the API generally delivers correct information, it sometimes requires a bit of work to automatically uses its results in a music-related context (to be exact, the issues might be in the underlying data, rather then on the API itself):
In some cases, it provides multiple artists – which is often correct, e.g. Blondie and Debby Harry but makes difficult to find who’s the main one, as the API delivers them at the same level (topicIds).
This is something we’ve improved to build our former seevl for YouTube plug-in, and while it’s not available anymore, as we’ve moved away from consumer-facing products to refocus on a B2B, turn-key, music discovery solution, I’ve decided to open source the underlying library to find who’s playing and what (yes, that’s music only) in any YouTube videos.
Introducing youplay – who’s and what’s playing in a YouTube music video
The result is youplay, available on PyPI and github, a MIT-licensed python library that works as an enhancement on top of the YouTube Data API v3 to automatically identify who’s and what’s playing in a music video. It uses different heuristics, data look-up, and more to find the correct artists if multiple ones are returned (unless they’re all playing in the video, like this RHCP + Snoop Dogg version of Scar Tissue), to filter ambiguous ones, or to find the correct artist and track if the API doesn’t deliver anything.
Here’s an example
(artists, tracks) = youplay.extract('0UjsXo9l6I8')
print '%s - %s' %(', '.join([artist.name for artist in artists]), tracks.name)
(artists, tracks) = youplay.extract('c-_vFlDBB8A')
print '%s - %s' %(artists.name, tracks.name)
(env)marvin-7:youplay alex$ python sample.py
Jay-Z, Alicia Keys - Empire State of Mind
Dropkick Murphys - Worker's Song
The tool is also packaged with a command line script returning JSON data for easy integration into non-python apps.
The fun part? All the look-ups (if any) are using the Freebase and YouTube API themselves, such as:
Finding the top-tracks of an artist from Freebase and matching it with the video name if the original API call when it returns only artist names;
Identifying if a song has been recorded by multiple artists;
Looking-up related YouTube videos to identify what’s the common topic between all of them, and guess the current artist of a video with no API-results.
Isn’t it a nice way to bridge the gap?
Even though I hope the API will be useful to other music-tech developers, I also wish that it soon becomes obsolete, as Google’s Knowledge Graph, and other structured-data efforts on the Web, keep growing on the Web in terms of AI, infrastructures and APIs/toolkits – making more and more easier every day to build data-driven applications (if only I had this 10years ago when I started digging into the topic!).
Oh, and I’m attending Google I/O next week, and if you’re working on similar projects, ping me and let’s have a chat!
Once you’ve setup your EchoNest API key as an environemnt variable ECHO_NEST_API_KEY, just run echoplot
marvin-7:~ alex$ echoplot -h
usage: echoplot [-h] [-s START] [-e END] artist title
Plot loudness of a song using the EchoNest API.
artist the song's artist, e.g. 'The Clash'
title the song's title, e.g. 'London Calling'
-h, --help show this help message and exit
-s START, --start START
start analysis at a given time (seconds)
-e END, --end END end analysis at a given time (seconds)
marvin-7:~ alex$ echoplot 'The Clash' 'London Calling'
The Web is not just about static descriptions of entities. It is about taking action on these entities.
Whether they’re online or offline, publishing those actions in a machine-readable format follows TimBL’s “Weaving the Web” vision of the Web as a social machine.
It’s even more relevant when the online and the offline world become one, whether it’s through apps (4square, Uber, etc.) or via sensors and wearable tech (mobile phones, Glass, etc.). A particular aspect I’m interested in is how those actions can help to personalise the Web
The rise of dynamic content and structured data on the Web
Yet, considering the recent advances on structured Web data (schema.org, Google’s Knowledge Graph, Facebook OpenGraph, Twitter cards…), this addition is a timely move. Every one can now publish their actions using a shared vocabulary, meaning that apps and services can consume them openly – pending the correct credentials and privacy settings. And that’s a big move for personalisation.
Personalising content from distributed data
Let’s consider my musical activity. Right now, I can plug my services into Facebook and use the Graph API to retrieve my listening history. Or query APIs such as the Deezer one. Or check my Twitter and Instagram feeds to remember some of the records I’ve put on my turntable. Yet, if all of them would publish actions using the new ListenAction type, I could use a single query engine to get the data from those different endpoints.
Deezer could describe actions using the following JSON-LD, and Spotify with RDFa, but it doesn’t really matter – as both would agree on shared semantics through a single vocabulary.
Ultimately, that means that every service could gather data from different sources to meaningfully extract information about myself, and deliver a personalised experience as soon as I log-in.
You might think that Facebook enables this already with the Graph API. Indeed, but data need to be in Facebook. This is not always the case, either because the seed services haven’t implemented – or removed – the proper connectors, or because you didn’t allow them to share your actions.
In this new configuration, I could decide, for every service I log-in, which sources it can access. Log-in to a music platform? Let’s access to my Deezer and Spotify profiles, where some schema.org Actions can be found. Booking a restaurant? Check my OpenTable ones. From there, those services can quickly build my profile and start personalising my online experience.
In addition, websites could decide to use background knowledge to enrich one’s profile, using vertical databases, e.g. Factual for geolocation data or our recently relaunched seevl API for music meta-data, combining with advanced heuristics such as such as time decay, actions-objects granularity and more to enhance the profiling capabilities (if you’re interested in the topic, check the slides of Fabrizio Orlandi’s Ph.D. viva on the topic) .
This way of personalising content could also have important privacy implications. By selecting which sources a service can access, I implicitly block access to data that is non-relevant or too private for that particular service – as opposed to granting access to all my content.
Going further, we can imagine an privacy-control matrix where I can select not only the sources, but also the action types to be used, keeping my data safe and avoiding freakomendations. I could provide my 4square eating actions (restaurants I’ve checked-in) to a food website, but offer my musical background (concerts I’ve been to) to a music app, keeping both separate.
Of course, websites should be smart enough to know which action they require, doing a source/action pre-selection for me. This could ultimately solve some of the trust issues often discussed when talking about personalisation, as Facebook’s Sam Lessin addressed in his keynote on the future of travel.
As you could see, I’m particularly interested in what’s going to happen with this new schema.org update, both from the publishers and the consumers point of view.
It will also be interesting to see how mappings could emerge between it and the Facebook Graph API, adding another level of interoperability in this quest to make the Web a social space.
Another week-end, another MusicHackDay. This time, I’ve tried to new APIs:
seatwave – that just launched few days and that gives access to a wide range of events, including (obviously) concerts. Search by location, time-frame, venue (including coordinates!), and redirect to seatwave website to get event tickets. Interestingly, they do rev-share if some tickets are bought in one’s app using their API.
SendGrid – cloud-based e-mail services. Sending mails, but also – the most interesting part – receiving ones and parsing them. Simply configure a MX, a callback URL, and parse any incoming e-mail, including header, content and attachements – all in a REST-ful way
So, with those 2 APIs in mind, I’ve build SeatTrip – it’s like seatwave meets tripit. Send your plane ticket by e-mail, and get a listing of events that will happen in the area a few minutes later.
Sending a AerLingus ticket about a trip to London, I got the following e-mail in my inbox a few minutes later. First, a featured artist. I’m using our own seevl data to identify the featured artist using its meta-data, and display her/his biography.
Then, the listing of all concerts for the city at that time.
For each event, it provide additional information from the seatwave API. First, it features a Google Map link to the venue (useful to buy your hotel nearby!).
Also, it links to the seatwave website so that you can directly book your concert – and lists the number of remaining tickets if the show is almost sold-out!
Here’s now the fun part, about how this hack works:
First, once the e-mail is sent to an address mapped to SendGrip API – a PHP script extracts the location and the timeframe of the trip from the e-mail. The extraction if airline-specific, and so far the hack works only on AerLingus ticket (however, an abstraction layer allows to easily create new wrappers – a similar strategy as used in TripIt).
Then, the seatwave API is used to get the list of all events in the area for that period, including all events details.
Once we have the events, seevl is used to identify the featured artist from the list of available concerts.
Finally, the e-mail id rendered in HTML, and send via SendGrid.
It takes around 2 minutes to do the whole processing, check this short video to see it in action. Note that I changed the trip date as that was an old trip ticket for which seatwave didn’t get any data, and that the video has been cut to avoid the delay of receiving the e-mail with the listing.
Also, don’t forget to check this impressive list of 62 hacks – especially Buddhafy (mind-control for Spotify !) and Concerts2021 – the future of live gigs (or not, thanksfully ;-) !
In particular, I’d give my thumbs-up for Tourrent – helping bands to set-up their next tours based on Torrent downloads of their tracks, FlatDrop – Micropayments for Soundcloud tracks, and Badgify – audioscrobbler meets Arduino.
Together with Ian from rd.io and Guillaume from Webdoc, we’ve worked on a new music discovery approach, leveraging real-world data. Various 4-square hacks enabling geolocation-based discovery have been build on previous MHD, so we’ve decided to take another route: take a picture of anything, and play the songs that reference this thing. Whether it’s a bottle, some brown sugar, or a house.
So here’s Mixture, a simple hack / proof-of-concept of the approach, combining APIs from IQ Engines (image recognition – give it a try if you’re looking for something similar, even though queries can be a bit slow), musiXmatch (lyrics identification), rd.io (music streaming) and seevl (artist data). It may still be buggy (we’ll work on it) and some APIs have a daily-rate limit that could block the application, but you should be able to get the overall idea!