Annotated Bibliography

Echo Nest http://the .echonest.com/

The Echo Nest is a music intelligence company that supplies an analytical approach to current music. Echo Nest supplies a platform to its consumers (both big and small) to enhance the way they interact with their music and the way music interacts with them. Echo Nest holds a 34 million song dataset that is available to the public that will be utilized in this project. Several indicators, such as tempo, key signature, duration, song loudness, and danceability are included in this dataset. These indicators will be analyzed throughout this project.

FRED 

2015. Bureau of Economic Analysis, Gross Domestic Product [GDP], retrieved from FRED, Federal Reserve Bank of St. Louis. http://research.stlouisfed.org/fred2/series/GDP/,June 9, 2015.

2015. Bureau of Economic Analysis, Civilian Unemployment Rate [UNRATE], retrieved from FRED, Federal Reserve Bank of St. Louis. http://research.stlouisfed.org/fred2/series/UN RATE/,June 9, 2015.

The FRED offers a vast amount of easily attainable data to the everyday citizen, corporate entity, or academic researcher. FRED offers several longitudinal databases that cover several economic indicators the bank feels is important. For this project, the unemployment and GDP data will be mined from FRED.

How Data Has Drive Music Intelligence

Davis, Gene. “How Big Data Has Driven Music Intelligence (Just in Time for The Grammys!) – Splice Machine.” Splice Machine How Big Data Has Driven Music Intelligence Just in Time for The Grammys Comments. N.p., 08 Feb. 2013. Web. 11 June 2015.

A discussion on how the music industry is changing to incorporate Big Data, namely in the form of predictors. The article discusses and analyzes the way the market for popular music is changing drastically thanks to Big Data. Artistic decisions and business decisions are being advised by some key conclusions that are driven by big data. This article holds the same viewpoint that motivated this project

Million Song Dataset

Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere.
The Million Song Dataset. In Proceedings of the 12th International Society
for Music Information Retrieval Conference (ISMIR 2011), 2011.

A database with 1,000,000 songs with analysis and metadata pulled from various datasets. They were created by the partnership of Echonest and LabROSA and the dataset has useful data to be pulled.

Music Business Plays to Big Data’s Beat

Karp, Hannah. “Music Business Plays to Big Data’s Beat.” WSJ. N.p., n.d. Web. 11 June 2015.

This wall street journal article reexamines the music industry through the lense of Big Data. As the arts become more and more quantified, music industry tycoons are no longer admired for their sharp wit. This is due to the tools and data that is available to them whilst making their contract decisions. While Big Data helps inform record labels, the data helps artists as well. An artist’s staff can run analytics on tweets, follows, and downloads- all giving them valuable information for their employer going forward.

MusiXMatch

Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere.
The Million Song Dataset. In Proceedings of the 12th International Society
for Music Information Retrieval Conference (ISMIR 2011), 2011.

musiXmatch dataset, the official lyrics collection for the Million Song Dataset,
available at: http://labrosa.ee.columbia.edu/millionsong/musixmatch

A database containing millions of songs worth of lyrics that has a partnership with Million Song Dataset. We will be pulling lyric data from this songs and running sentiment analysis on the lyrics that will then be then cross referenced with the Million Song Dataset data. From this a deeper analysis of sentimentality and key will be examined.

The Next Big Sound iTunes Predictor Lab

Czech, Eric. “Predicting ITunes Sales Through the Anatomy of Music.” Making Next Big Sound. Next Big Sound, 18 Sept. 2014. Web. 10 June 2015.

The Next Big Sound is a company that researches and deciphers the next big hit via information available on itunes. In their labs, individually driven project examining music data from Echo Nest, analyzed in R, are presented. From this project, methodologies are drawn to advise decisions for this project.

The Shazam Effect

Thompson, Derek. “The Shazam Effect.” The Atlantic. Atlantic Media Company, 17 Nov. 2014. Web. 11 June 2015.

This article explores the effects of Shazam and other music-based, digital tools that technology and its affect on society is affecting society. A discussion based on these new digitized tools was sparked by shazam and spread to other societal influence in the media age. This article examines how society can affect music and not vice versa.

Shazam It!

Jovanovic, Jovan. “Shazam It! Music Recognition Algorithms, Fingerprinting, and Processing.” Toptal Engineering Blog. N.p., n.d. Web. 11 June 2015.

A technical overview of Shazam was given. Shazam has revolutionized the music industry but also viewing the technical aspect of the company shows how difficult it is to quantify the arts.

World Bank

The World Bank Group. “World Development Indicators.” World Databank. The World Bank, 2015. Web. 10 June 2015.

The World Bank gives a deep, publically available database of economic indicators across the world. This data can be mined easily and accessed on most processing systems. The majority of the economic data will be mined from this site.