Simulating Shazam: Acoustic Fingerprinting for Music Identification A Comprehensive Study on Developing a Song Recognition System

Abstract

Abstract This report explains the implementation, design and testing of the core functionalities of Shazam. Shazam identifies songs by capturing short audio segments and matches them against a sizeable database. The program is coded in Python and with MySQL for the database. To perform tests, both audio files and a microphone are used to catch the samples of the songs. The results of the project are deemed successful as it can detect songs from a 10 second sample of a song. In conclusion, the project demonstrates a strong foundation to continue developing the project to simulate Shazam.