Tag Archives: technology

Change in MySwar domain

Earlier this week, some of us observed that myswar.com was inaccessible. We had recently moved to a new hosting provider and our first thought was that it was an issue at their end. Further investigation, however, revealed that we were most likely the target of a court order that had ISPs blocking our website. The court order is backed by Section 169A of the IT Act, 2008. We’re not the first website to have been subjected to this arbitrary and draconian law. Websites like Vimeo, Github and Mouthshut have been subjected to such blocks in the past.

One of the biggest issues of such blocks is that the targets have no idea who initiated the block and why. While we will explore legal options to remove the block, with our limited resources, this is going to be extremely difficult.

To work around this issue in the short term, we have moved our domain to http://myswar.in. This comes at great cost to us in terms of our branding and the goodwill of users who’ve encouraged us through the years. Unfortunately, this appears to be our best option for now. The block has also resulted in our apps not working. We’re working to publish the updated versions of the apps by next week.

As experts have noted, copyright owners are increasingly using court orders to mass block torrents and piracy sites around the time major films are released. Unfortunately, this process unfairly sweeps up legal, smaller websites like ours. In an attempt to address this problem, we will defer publishing of film albums to after this period.

We sincerely regret the inconvenience caused to our users and hope that they’ll stay with us while we figure out a long term solution.

MySwar Mobile Web Version Now Available

IMG_3178

Over the last few weeks, we rolled out mobile web updates to the MySwar website. When you access myswar.in from a mobile device, you’ll see an easy-to-navigate, mobile-friendly version. Almost every feature available available on the desktop website is available in the mobile web version. While the UI is different, the flow is very similar to the flow of the desktop website. The mobile website is intuitive but do take time out the check out the feature-rich, context-sensitive Settings option. Depending on what page you are in, the Settings pop-up provides you options to do various things including starting a playlist, filtering lists, logging in, changing the display language and switching to the destop UI. We hope you enjoy this update.

MySwar App Now Available In Hindi

Updated on Mar 12, 2015: The MySwar Android app is also available in Hindi now.

IMG_2018

A little more than a year ago, we had announced the availability of content on MySwar in Hindi in addition to English. We finally got to roll out an update that makes Hindi content available on the MySwar iOS app and the MySwar Android app as well.

Pretty much all content on the app, artist bios and trivia being exceptions, is now available in Hindi. Just go to Settings -> Language -> Choose “हिन्दी में” and Voila! As in the website, regardless of the Language setting in the app, you can search for songs/albums/artists by typing in either English or Devanagari.

Here’s a quick view of how the language setting works:

IMG_2016IMG_2017IMG_2018IMG_2019IMG_2020IMG_2021

Film Credits On MySwar

Since MySwar launched 2011, we have steadfastly focused on crediting musicians making Hindi film music – music directors, lyricists, singers and when the information was available, arrangers, assistants, instrumentalists and so on. We believe that musician credits is a sadly overlooked aspect of music metadata in India. That is the reason you didn’t see any credits for the film cast and crew all this time. While we continue to hold that belief, we believe we have made a significant contribution in cataloguing comprehensive and accurate musician credits and it’s now time to start adding other film credits as well.

A few weeks ago we started showing credits for the film crew – specifically Director, Producer, Writer (Story, Dialogue, Screenplay), Cast and Studio. So far we have credits for over 1500 films and you should be able to find complete filmographies of the superstars – Dev Anand, Shammi Kapoor, Rajesh Khanna, Amitabh Bachchan – and the major directors – Anurag Kashyap, Dibakar Banerjee, Gulzar, Guru Dutt, Hrishikesh Mukherjee, Manmohan Desai, Nasir Hussain, Shakti Samanta, Subhash Ghai, Vishal Bhardwaj, Vidhu Vinod Chopra and Yash Chopra, among others. This remains a work in progress effort as we continue to add film credits for other films based on priority decided by the film’s significance and the significance of the film’s cast and crew.

This additional information is available on the website in the album page as well as in the app in the additional information screen for albums. On the website, this information is available in Hindi as well English.

Sholay

One of the challenges we faced in this project is reconciling artists with same or similar names. For example, while Nasir Hussain (नासिर हुसैन) is the producer/director behind films like “Teesri Manzil” (1966), “Yaadon Ki Baarat” (1973) and “Qayamat Se Qayamat Tak” (1988); Nazir Hussain (नज़ीर हुसैन) is the actor known for his role in films like “Devdas” (1955), “Kashmir Ki Kali” (1964), “Jewel Thief” (1967) and “Amar Akbar Anthony” (1977). We have tried our best to ensure proper credits by using the primary source where we could – the credits in the film itself – as well as a number of other sources including the venerable Hindi Film Geet Kosh. If, however, you find mistakes, please do let us know and we’ll fix it.

In addition to regular search and display, you can also use Advanced Search to find songs that include actor/producer/director/writer/studio parameters. The results from these searches are indicative since – a) we don’t have all the films covered yet for these new credit attributes, b) the credits are at the film level, not the song level (relevant specially for actors).

I hope you like this new facet of MySwar and enjoy the delicious nuggets of information it offers.

MySwar App Update On iOS – Going Free!

We released a new version of the MySwar app on iOS yesterday. This update makes the app free and displays banner ads. There is an option to make an in-app purchase to remove the ads for a period of 1 year (Upgrade option in Settings).

We realize that some of you have purchased the app only recently and this move may appear unfair. To address this scenario – we ask you to send us an email at admin@myswar.in with your iOS device UDID and we will help you get back to the ad-free version (for one year). If you need help figuring out the UDID, let us know and we’ll help you.

If you still haven’t downloaded the app, there really is no excuse now. Get it from here: https://itunes.apple.com/app/myswar/id622503117?ls=1&mt=8

The Android app was always free and remains free: https://play.google.com/store/apps/details?id=com.mavrix.myswar

iTunes Quietly Launches Music And Films In India (Links Available On MySwar)

Digital India was abuzz yesterday with news of iTunes launching its music and films stores in India. I particularly liked the following posts covering the launch:

http://nh7.in/indiecision/2012/12/04/rip-and-run-itunes-india-store-is-finally-here

http://www.medianama.com/2012/12/223-apple-finally-extends-itunes-store-to-india/

While the buzz is mostly positive:

 

,there were some who were not very impressed, like in this comment thread on Medianama.

I think the iTunes launch is a great step forward for digital music in India and while it will have no impact on hard-core freeloaders, it will have huge appeal for people who want easy access to digital music.

iTunes links were already available on MySwar.in in US, UK, Canada. Starting yesterday, iTunes links are available in India as well. The shopping cart icons at the song level link to iTunes India and the album level to Flipkart. This is just a quick fix and we are working on improving this feature.

Digital Music Landscape III: Consumption

[This is the concluding part of a three-part post on the Digital Music Landscape. You can read the first post and the second post to get up to speed]

Let’s look at the services that exist in the West against the services for Indian music in an attempt to look at how music recommendations serve people’s needs. In the previous posts, we’ve discussed a few approaches for recommendation. Let’s pair that up against the following music consumption models:

  • Downloads: Wherein the service allows you to browse and download songs for purchase. Most services allow downloaded songs to be played in any device/player but certain services provide DRM-restricted songs. Such songs can only be played on certain devices or certain players.
  • On-demand streaming: The user can listen to any music, any time. These services are either free (ad-supported) or based on a subscription plan. Increasingly, the free plans are getting capped to a limited amount of music.
  • Non-Interactive streaming: The service is pre-programmed with content,  allowing users to only skip tracks and provide ratings. The content is either delivered through a recommendation engine based on the users’ taste or curated by experts.

Download services limit the number of songs people can listen to (only purchased songs) while streaming offers potentially unlimited number of songs for listening. On the other hand, downloaded songs can be listened to anytime, anywhere. Whereas, streaming services typically require an internet connection. The line between download and stream services is blurring though, as the download services are providing cloud-based features in addition to song previews; and streaming services are allowing downloads either directly or through other download services.

Music Consumption – Mature Markets

ServiceConsumption modelRecommendation approach based on
DownloadOn-demandNon-interactiveMusical attributesWisdom of the CrowdsExpert curation
iTunes
Amazon
Napster
Emusic
Rhapsody
Last.fm
Grooveshark
Spotify
Pandora
Live365
Thesixtyone
Wearehunted

Music Consumption – India

ServiceConsumption modelRecommendation approach based on
DowloadOn-DemandNon-InteractiveMusical attributesWisdom of the CrowdsExpert curation
Gaana
Saavn
Dhingana
Musicindiaonline
Smashits
Raaga
NH7
Hungama

A more detailed look at the Indian music services show that:

  • There are fewer consumption choices in India.
  • There is very little differentiation between various services.
  • The business model behind some of these services is not evident. All streaming services are free to users. Do they make enough money from ads? What about those that don’t even show ads?
  • Services are in the early stages of building recommendation capabilities. Recommendations from Indian services are either poor or limited (e.g.: NH7 does a pretty good job but serves a niche).
  • A lot of popular Indian music is made for films and has unique factors driving people’s interests – music directors, singers, lyricists, actors on which they are filmed, etc. These factors don’t come into play for non-Indian music.
  • Interest in multiples languages need to be catered to.
  • Services have big holes in their song catalogs because of limitations in their licensing agreements.

Given all these challenges, the quest is still on for a good, Indian music service that is comparable to an iTunes or a Spotify. While we’re not launching a music consumption service (not yet at least!), we at Mavrix keenly watch this space because we’re trying to solve one of the challenges listed above – that of serving good recommendations. We will be launching MySwar in a few days as a first step in this journey.

Digital Music Landscape II: Discovery

[This is the second part of a three-part post covering the digital music landscape. You can read the first post here.]

In the second part , let’s look at the different approaches of music discovery. Regardless of the approach, the end objective is to help people find new music.

Music for music’s sake

The Music Genome Project began listing out the key attributes that define a song with a dedicated team of music analysts who would listen for ‘strains’ or ‘genes’ of a song. This was the basis for Pandora Radio, the pioneer in music discovery. When a user listens to a particular song, Pandora will look at the defining attributes and find the most similar tracks based on these attributes.

A variation of this approach uses computers instead of human experts to get to the song definition. Audio features are extracted using MIR (Music Information Retrieval) techniques. This method may misinterpret songs and attributes by ignoring subjects like lyrical themes, cultural context, moods and situations. The Million Song Dataset from EchoNest is the result of one such MIR exercise. Clio Music is another example of machine-enabled music discovery.

Wisdom of the crowds

Collaborative recommendations are the most basic means for many established platforms to generate insight from the community. Most user driven platforms rely solely on user contributed ratings . The system finds users with similar taste patterns via the recorded metadata, and recommend songs that were appreciated by this group of similar minded users. iTunes has a system called Genius that recommends songs from the iTunes store based on users’ library content and history of song plays, matched against a repository of crowd-ranked data.

The new spin on this method is the social recommendation aspect. This utilizes ratings/recommendations given by close friends within your online social network. The upside here is that users are more likely to trust recommendations provided by people they know. The flip-side is that people in a given network may have very different tastes in music.

Curated playlists

This method would commonly be called ‘non interactive’ as the music played on the website is effectively like a preset radio station. People can browse stations by genre,artists or moods and find a nice blend of familiar with random music. Rhapsody, one of the oldest music services around, offers this feature as an in-house specialty. There can be another model like Live365 where users generate playlists around a much narrower niche and often is better suited to discovering music.

Indie popularity

Indie music is a class of its own. If its never-heard-of artists and never-heard-of bands that you wish to discover, the best tool would be to measure their ‘buzz’ online. Discover sites like Thesixtyone or at  Wearehunted measure fan interactions, listener votes and shares/reposting on social networks to uncover new artists. The Hype Machine is another offbeat portal that has been called the ‘Technorati of music’, since it unites the music and the blogging community with a live index of mp3 blogs, and the content is distilled down to a trend of  the music that people are talking about online.

The aim behind all this innovation can be explained as a need to market the massive potential of musical long tail content. There is an immense value that people find with the experience of  easy access to songs and information. Encouraging people to involve in the music community is the best way to promote it.  When there are no barriers to this involvement, is when people stop dependency on piracy and unlawful means to procure something that doesn’t need aggressive marketing of any sort. It’s all about the discovery.

Unfortunately, while music discovery has made significant progress in the West, it’s still in its nascent stages in India. At Mavrix, we’re just beginning to take baby steps towards enabling discovery of Indian music but there is a lot more work to be done.

In the concluding post, let’s talk about the various music consumption models.

Digital Music Landscape I : Recommenders

[This is the first part of a three-part post that provides a high level overview of the digital music landscape where Mavrix and MySwar fits in.]

Recommender: specific type of information filtering system technique that attempts to recommend information items (movies, music, books, news, images, web pages, etc.) or social elements (e.g. people, events or groups) that are likely to be of interest to the user. – Wikipedia

Recommendation engines work as blend of many algorithms and approaches, to find similarities between what you find interesting , and what you may potentially find interesting. Often people use a Collaborative filtering model, or ‘wisdom of the crowd’  approach to generate lists of  music, movies, news and other items you wouldn’t have come across in the mess of information around.

Recommendation services have evolved over the decades as I’ve tried to outline below

  •  The idea of collaborative filtering was derived, when developing an automatic filtering system for electronic mail called Tapestry, over at  Xerox Palo Alto Research in 1992. They needed to handle the large amounts of email and messages posted to newsgroups. Users were encouraged to annotate documents , and these annotations could be used for further filtering.
  • Grouplens began as a research group in the University of Minnesota where the students made a system to recommend Usenet News. It collected ratings from Usenet readers and used those ratings to predict how much other readers would like an article before they read it. This recommendation engine was one of the first automated collaborative filtering systems in which algorithms were used to automatically form predictions based on historical patterns of ratings. The research project would eventually spin out the Movielens project in 1997 and be featured in a Malcolm Gladwell column.
  • Engineers from the MIT Media labs created a email-based collaborative music recommendation system called RINGO. The community around this project eventually became known as the Helpful Online Music Recommendation Service (HORM). In 1999, it eventually spun out into a company called Firefly which was acquired by Microsoft where it was killed suddenly.
Today technology has advanced into a stage where recommender systems have become ubiquitous.
  • Amazon is well-known for its item to item recommendation system. All recommendations are based on individual behavior. Whether you like to buy something because it is related to something that you purchased before, or because it is popular with other users, you have a list of social recommendations – what other users bought, or personal recommendations-based on your purchase history.
  • Netflix encourages subscribers to rate the movies they’ve viewed, and their CineMatch program recommends titles similar to those well liked — regardless of a film’s popularity at the box office.
  • Google news serves a personalized news feed by assimilating the user’s genuine news interests as validated by click history and influences of local news trends, together with a collaborative filtering method. The result is that you view articles that align to your interests.

Music discovery is the new keyword on the digital block. To put it simply, an event of listening to a song by accident, having it play in your head, get you to like it and have you realize you want to hear it again is simplified to a website/app doing all that work for you. The music recommendation world today is vastly different from the Ringo email system where you rated some songs on an absolute scale and emailed it to the system, which would reply with songs/albums it thought you would like.

Let’s look at some awesome platforms that are driving this new experience in the second part of this post.