Music Search at the Scale of the Web: Tutorial at ISMIR 2009, Oct. 26th, Kobe, Japan

Tutorial AM 1 (10:00-13:00): MIR at the Scale of the Web

by Malcolm Slaney (Yahoo! Research), and Michael Casey (Dartmouth College)

Abstract

In the last couple of years we have received access to music databases with millions of songs. This massive change in the amount of data available to researchers is changing the face of Music Information Retrieval. In many domains, speech-recognition is most notable, people have observed that the best way to improve their algorithm’s performance is to add more data. Starting with hidden-Markov models (HMMs) and support-vector machines, people have applied ever greater amounts of data to their problems and been rewarded with new levels of performance.

What are the algorithms and ideas that are necessary to work with such large databases? How do we define the scope of a problem, and how do we apply modern clusters of processors to these problems? What does it take to collect, manage and deliver solutions with millions of songs and terabytes of data? In this tutorial we will talk about a range of algorithms and tools that make it easy/easier to scale our work to Internet-sized collections of music. The field is just developing so this tutorial will talk about a range of techniques that are in use today. Millions of songs fit into a small number of terabytes, which is just a few hundred dollars of disk space.

This tutorial will give attendees an overview and pointers to the tools that will allow them to scale their work to modern datasets. The tutorial will discuss the theoretical and practical problem with large data, applications where large amounts of data are important to consider, types of algorithms that are practical with such large datasets, and examples of implementation techniques that make these algorithms practical. The tutorial will be illustrated with many real-world examples and results.