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About this Article
Written by: Bradley Head
Written on: April 1st, 2010
Tags: computer science, lifestyle, physics
Thumbnail by: sibaudio/stock.xchng
About the Author
Bradley is a junior Industrial and Systems Engineering major in the Viterbi School of Engineering. He balances his time as an Air Force ROTC cadet, a brother in Zeta Beta Tau fraternity, an Executive VP on the Interfraternity Council, and a licensed private pilot somewhere in between visiting Tutor Hall for the lunch special and doing his Stochastic Probability Models homework.
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Volume XII Issue II > Letting No Music Go Unrecognized
Modern music recognition software has taken the guesswork out of locating music. With just a tap of a finger, smartphone users everywhere can record, send, and analyze 15 seconds worth of music to receive a response with the track name in only a matter of seconds. By analyzing a song’s unique “audio fingerprint” and reducing the necessary amount of identifying information, the song in question can be matched within a database in increasingly short periods of time, made possible by a powerful algorithm developed by engineers. Now, no territory is unfamiliar for the music connoisseur; any song, anywhere, can now be identified by its correct name and downloaded in a few short steps.

Introduction

Every so often in the engineering world, a product comes along that makes life easier—it takes the “manual” out of manual labor, puts the “automatic” in automatic transmission, or adds the “instant” in instant search. For music lovers, the last decade of engineering developments has transformed the way people listen to and purchase music: mp3 players, peer-to-peer downloading software, online music stores, and music recommendation programs were birthed out of the iPod generation. But something new has arrived that has made searching for tunes ever so harmonious.
No longer do head-bobbing passengers in the back seat of their friend’s car need to wonder what great new song that they’ve never heard before is blasting on the radio; never again will listeners at a bar have to scramble to write down the lyrics on their hands, so they can post it into a search engine later hoping to strike gold; and never again will great music go unrecognized and unsold—because now, there’s an app for that, and it’s called music recognition software.

Deciphering the Music Recognition Dilemma

When developers and entrepreneurs began to tackle the music recognition problem, many told them that their chances for finding a quick fix were slim. Surely, the sheer volume of music on the market and speed with which songs could be compared with one another would require considerable space to house servers and countless hours to search those servers. Yes, one could compare the audio signatures of every song, which had been done for years; but trying to compare the frequency vs. time graph (a scattered map of the sound’s electronic waves over time) of two million files was by no means going to be instantaneous. This matching game was evolving into a puzzle that might call for nearly ten years of research and plenty of exhausted funds in order to produce only an unfinished product. Yet, researchers played stubborn and businessmen refused to say no, and a product was born that exceeded all expectations.

The Audio Fingerprint

All sound files have a unique audio signature, or fingerprint. A graph of their frequency and amplitude versus time displays a complex wave that is exclusive to every song (see Fig. 1). At the time, the most effective way to compare two sound files was to stand them side by side and progressively check each time frame for a match. This worked well enough when the library where the database files were stored was small. However, when a user wanted the name for the song in his or her favorite 1988 B-movie that raked in only 200 downloads last month on iTunes, an issue arose.
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Figure 1: Comparison of two sound files' frequency vs. amplitude graphs.
That is when developers took a radically different approach—they decided to output as much information about a song as possible. In essence, they wanted to rid the audio file of the majority of the very information that had been used before to analyze and compare it. Soon, 3 megabyte files were being whittled down to 64 bytes of information. Hours of searching using older algorithms was soon replaced by a version 10,000 times faster. With a new 2-dimensional approach to music signature analysis, an effective algorithm was devised, and it required 50,000 times less information [1].