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mir-noise

Repository for MIR Final Project

File Structure: / <= All top level code (main, plotting, etc.) goes here mirlib <= All useful code goes here. --FFTParams.py <= Class for passing FFTParameters easily --MatlabData.py <= Methods for handling reading matlab data files [not in use] --audiofile.py <= Methods for handling complex audio file i/o --benchmarking.py <= Methods for benchmarking [not in use] --featurevector.py <= Methods for managing feature vectors --mir_utils.py <= MIR-related methods that don't fit in any other categories --peakdetector.py <= Methods for finding peaks in a function --windowmanager.py <= Windowing methods

mirlib/feature_extraction <= Modules related to feature extraction --lowlevel_spectral.py <= Methods for low-level spectral features --lowlevel_temporal.py <= Methods for low-level temporal features [not in use] --BeatDetector.py <= Tempo Detection [not in use] --OnsetDetector.py <= Onset Detection [not in use] --calcLoudness.py <= Sones calculation --eventDetect.py <= find events from an energy function

mirlib/feature_analysis <= Modules related to feature analysis --nn_classifier.py <= Nearest Neighbor Classifier [not in use] --kmeans.py <= Methods for handling k-means --discrimination.py <= methods for handling classifier discrimination [not in use]

mirlib_test <= Unit testing and normal testing for mirlib

Package Dependencies - You must install these to run this!

Usage: This program can be run in several modes. Use the "-h" on each (or the main program) for more details.

Modes: python main.py getfeatures Runs the feature extraction component, which outputs .npy files storing the feature vectors. python main.py featureselect Runs features selection on the k's provided. Dislpays a plot of J0. python main.py clustering <-w> Run for a single k, and attempt to choose the best centroids for it. the -w parameter causes it to output the resultant classes to an audio file in the results folder.

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