#Song Identification using Numpy, Scipy, Audiolab

This implementation attempts to match a piece of music and find the most appropriate match in the database. It has applications as a song identifier or as a song suggester.

The matching of the input piece of music against the database is done by creating spectrograms and identifying key points in the spectrogram. In this case, peaks of power. A spectrogram is a graph with the time on the X-axis, frequency on the Y-axis and power on the Z-axis. Fast fourier transform is used to calculate the power and plot the spectrogram.

The DB (MongoDB) contains pre-processed songs with their respective hashed peak points of spectrograms. The input song's spectrogram is plotted, peak points are hashed and is matched against the database.

This implementation was inspired by Avery Li-Chun Wang's paper