Making Next Big Sound

Posts from the team

Front End Functional Testing at Next Big Sound

mbildner mbildner

Sep 26, 2014

Let’s talk about Functional testing. Functional testing is super important and commensurately difficult. Over the last few months, the engineering team here at Next Big Sound has been overhauling our testing system as part of a push to improve our development process. As part of the process we wrote up a high-level explanation of what Functional testing is and how we’ve been designing our new system. We then refined those ideas and wrote this blog post. This amazing blog post.

This post is meant to describe four things:

  1. Why Automated UI Testing is Hard
  2. How our front-end testing system is built
  3. What our front-end testing system is intended to do
  4. What our front-end testing system is NOT meant for, and what it CANNOT do

Testing is a weird thing; Functional testing is especially challenging, and while it’s challenging, it’s really, really important. It’s also kind of cool.

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Predicting iTunes Sales Through the Anatomy of Music

eric-czech eric-czech

Sep 18, 2014

The commercial success of music is dependent on a lot of things, but there are certainly similarities inherent to what “works” for mainstream artists. Top country, rap, rock, and EDM acts all produce content varying in style but with certain commonalities that seem to suggest that there might be “features” of the songs themselves, independent of the artists who create them, that make them so popular.

Electronic music is usually more uptempo with long, dramatic transitions, rock music is usually a little slower with more musical elements/instruments, country music is usually more balladic and twangy, etc. It would be hard to argue that the potential success of new music isn’t based highly on the previous success of the artist producing it, but it’s worth asking if there isn’t some magical formula for the components of the songs that would also be predictive in a similar way.

This analysis attempts to answer this question by comparing iTunes sales data to properties of musical content. The properties used are provided by EchoNest (recently acquired by Spotify) and break each song down into certain components like duration, tempo, loudness, etc. These properties will be crossed with sales data to see if any interesting relationships jump out and whether or not they could be exploited.


The Data Set

To run this analysis, a data set was built containing values for the features below as well as average daily iTunes sales data for 32,310 tracks (by 751 artists). Here is a full list of all track features collected and their associated definitions:

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Iterating on Iterations - The Year-Long Evolution of the Way We Work at Next Big Sound

dzwieback dzwieback

Jun 13, 2014

[Originally posted in two parts on popforms.com.]

With epidemically low employee engagement, being highly effective and happy at work is an exception, not the norm. Why are we failing to engage at work? Why are healthy, high performance teams so rare?

At least part of the answer to these troubling questions lies in the fact that most companies are organized in inflexible, hierarchical, command-and-control silos. These organizational structures are arguably ill-suited even to the assembly lines where they originated over a century ago, let alone today’s knowledge workforce. Even more surprisingly, of the many companies that have adopted a modern, iterative approach to product development (known as “Lean” or “Agile”), only a few take the same iterative approach to their organizations.

At Next Big Sound, we are committed to iterating not only on our products but also on the way that we work.

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Zipf’s Law for Facebook Fans: Building Intuition for Big Numbers

adamhajari adamhajari

May 30, 2014

Have you heard of Lydia Loveless? She is an alt-country singer songwriter from Ohio with three full length albums under her belt, and she has played more than 150 shows across the United States.

Loveless is more popular than 90% of all musicians on Facebook. With more than 7,000 page likes, she is 16 times more popular than the typical* artist. When viewed from this perspective, 7,000 seems like a large number. But when you consider that the average number of page likes across all artists on Facebook is 22,000 – over 3 times greater than the value for the Loveless page, things are less clear.

This seeming incongruity is due to the highly skewed distribution of Facebook page likes amongst musicians. On Facebook, as with most things on the internet, a very large percentage of engagement belongs to a very small percentage of artists. One of the original observers of this “Law of Unfairness” was Vilfredo Pareto, an early 20th century Italian economist who, when looking at the distribution of land ownership in Italy, noticed that 80% of land was owned by 20% of the population. The Pareto Distribution (also known as the Power Law Distribution) still applies to the distribution of wealth today, as well as to many other naturally occurring and man-made phenomena.

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Data Architecture @ NBS

eric-czech eric-czech

May 13, 2014

Tracking online activity is hardly a new idea, but doing it for the entire music industry isn’t easy. Half a billion music video streams, track downloads, and artist page likes occur each day and measuring all of this activity across platforms such as Spotify, iTunes, YouTube, Facebook, and more, poses some interesting scalability challenges.

Our data growth rate has been close to exponential and early adoption of distributed systems has been crucial in keeping up. With over 100 sources tracked coming from both public and proprietary providers, dealing with the heterogenous nature of this data has required some novel solutions that go beyond the features that come for free with modern distributed databases.

We’ve also transitioned between full cloud providers (Slicehost), hybrid providers (Rackspace), and colocation (Zcolo) all while running with a small engineering staff using nothing but open source systems. There has been no shortage of lessons learned in building Next Big Sound, and following are some highlights on what we did and how we did it.

Stats

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Predicting Next Year’s Breakout Artists

victorlovesdata victorlovesdata

Nov 27, 2013

At Next Big Sound, we have always been fascinated by the power of data to predict tomorrow’s music stars. Recently we developed an algorithm that creates a list of the emerging artists who are most likely to break out this year. Over time we tweaked this formula enhancing its forecasting ability, to the point that we’ve been able to patent its powers of prediction. This article describes how to pinpoint breakout artists 500 times better than random chance, up to a year in advance.

For instance, in June 2012, both Kendrick Lamar and A$AP Rocky had released acclaimed mixtapes but no studio albums or hit Billboard songs. We predicted both to explode based on their social media data in June 2012. Their debut studio albums opened at #1 and #2 months later on the Billboard 200, with Lamar now counting a total of nine singles that have hit the Hot 100 and A$AP Rocky a total of three. 

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Introducing the Tech Blog, Making Next Big Sound

victorlovesdata victorlovesdata

Nov 26, 2013

We are starting this blog to share some of these techniques that we use to understand how people discover and engage with music. Over the last four years, we have collected data on hundreds of thousands of artists worldwide, drawing from fan interactions on the radio, YouTube, Twitter, iTunes, concerts and more. In the tech blog we will delve more deeply into topics such as:

  • how we accurately predict breakout artists a year in advance
  • defining and detailing the spread of DevOps culture
  • how to use a music robot to DJ for your office
  • technical challenges we’ve overcome as a Big Data company
  • what exactly Granger causality is and why it matters
  • and more!

Enjoy a behind-the-scenes look at Making Next Big Sound!  

davidhoffman davidhoffman

Nov 17, 2013

Reppin’ the NBS Mousepad. 

thecool thecool

Nov 04, 2013

We all want to change the world