Author Archives: Fidel

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

Music Consumption – India

ServiceConsumption modelRecommendation approach based on
DowloadOn-DemandNon-InteractiveMusical attributesWisdom of the CrowdsExpert curation

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.

Musicians Are A Lot Like Technology Startups

  1. Life experiences (especially early ones) inspire the musician’s music and the entrepreneur’s business. John Lennon used song-writing as an escape from a troubled childhood. Richard Branson started a school newspaper when he got frustrated by rigid school rules and regulations.
  2. Success for both musicians and startups come after a lot of hard work and learning from experience. The Beatles had played together more than 1200 times before they got noticed in 1964. Bill Gates had done 10,000 hours of programming by the age of 13.
  3. Performing cover songs help bands launch careers but they need to deliver originals to sustain their success. Red Hot Chilli Peppers may have gotten attention through their cover of Stevie Wonder’s “Higher Ground” but it is their unique style of funk infused rock that gained them a large following. Similarly, startups can be inspired by existing companies but need to do something very different to deliver more value and be successful. Facebook may have started off as a Friendster clone but scaled better and innovated to become the premier online social network even as Friendster closed shop.
  4. The musician’s first song and the startup’s first product release is always sketchy. You can’t be perfect the first time.
  5. Both music and technology products are a result of teamwork. The band doesn’t take off till the right set of musicians come together. Indian Ocean started off with two people and saw several shake-ups till it got to the line-up that delivered success. Apple‘s early success was a result of teamwork – Steve Jobs’ marketing skills and Steve Wozniak’s engineering prowess.
  6. Most musicians start with free gigs and startups with free products/services.
  7. Musicians and startups make it big by persisting and continuing to do what they love and believe in. Susan Boyle made it big with Britain’s Got Talent, at age 48. Tim Westergren went through years of struggle before Pandora become the much-loved music service that it is today.

5 Factors To Consider While Buying A Guitar

It could be the funky groove in the song. Or the catchy riff in the chorus. Could definitely be the powerful solo building the crescendo.

For what ever reasons we have, we can no longer deny ourselves the passion.The decision makes itself.

To finally get yourself that  guitar. Or that  keyboard, the drums or that blues harmonica. It’s an awesome feeling, expecting to give life to your favorite songs with your own hands. To feel what it must feel like, to know and play the notes,chords and produce sweet, sweet music.

But the journey must start somewhere, like it did for a friend who asked me to accompany him to purchase a new acoustic guitar. We walked into an impressive music store in Bangalore to look for a companion for his lonely fingers. Music stores today stock an intimidating lineup of instruments and accessories ranging across all levels of musicianship and are increasingly digital and sophisticated compared to instruments of yore.

We looked at some good brands and he quickly decided it was up to me to find the right guitar for him. I list here some of the key factors I considered while picking his guitar:

  1. Guitar Body: The popular  three types of guitar body construction are Classic, Dreadnought and Jumbo. Technical specifications to distinguish them across various brands are beyond the scope of this post. But simply put, they indicate the size of the guitar body. This directly impacts the tonal qualities of the guitar. Larger bodied guitar have a better bass response as the resonance cavity is larger. Smaller bodies are usually more “trebley” in nature.If you like playing solos, then a guitar with a cutaway allowing access to the higher range of frets will interest you. Purists claim the missing chunk of wood will adversely affect the tone. The counter argument is that the upper bout is structural in function while it’s the lower bout that is acoustic.
  2. Wood: Materials used in constructing  the guitar body/neck affect the timbre or quality of sound. The tonal signature  of  an acoustic guitar is heavily dependent on the wood (density, strength). As opposed to commercial wood, quartersawn logs are used. The top surface of the guitar which is called the soundboard traditionally has  spruce/cedar wood ground to 1.5 to 2 mm and fixed in place with tubular bracing. The vibration of the soundboard transmitted via the saddle resonates in the cavity. For the back and sides, hard woods like mahogany, walnut and oak with their distinctive grain and color make for a good guitar. The fingerboard usually has ebony/rosewood glued over the neck. Nowadays though, it’s not uncommon to use alternate material like graphite or carbon fibre for the back, sides and neck.
  3. Action: Upon holding your first chord on the guitar, your left hand starts becoming aware of the kind of effort required to fret notes. This may be rendered more difficult if the ‘string action’ is set high. This simply means the space between the fretboard and the strings parallel above it. The thumb rule is that the string height at the 12th fret should be around 3mm – 5mm. Further tweaking is subject to personal choice. What I did notice at the store was that the saddles are much higher than they should be. I would suggest removing it and  filing down the opposite side to set it right. Medium gauge strings are the best for acoustic guitars allowing for greater  sustain and a crisper tone.
  4. Craftmanship: It’s difficult to discuss this since all guitar brands nowadays manufacture in China. There is still something to be said about the way guitars need to ‘feel’  the minute you hold one.  In my opinion, this is the single most important factor deciding a guitar’s quality.
  5. Electronics: Almost all guitar brands come with an onboard pickup for a premium. The electronics will allow your acoustic  guitar to be plugged in via a 1/4″ jack sending a signal to a mixer for a live show as opposed to mixing it live. Let this be the last of your concerns while picking up a guitar, if your intention is to learn.  The flashy tuner and three-band equalizer may not be the first thing you need to occupy yourself with. Although it’s a direct passport to join a band later!

How do I know so much about guitars? I spent the last ten years researching for the purchase of my first acoustic guitar!

Bangalore’s Live Music Scene Making A Comeback

Bengaluru will not let go of the Rock Capital tag easily! Despite all that transpired in the city potentially spelling doom for music lovers, it appears that there is enough and more support going around.

Here ‘s a link to a blogpost  belonging to a local rising artist, Fidel Dsouza, who he talks about the new kids on the block pitching in to recreate the city’s magic.

Good thing he also works for Mavrix!