e@0: # -*- coding: utf-8 -*-
e@0: """
e@0: Created on Fri Apr 17 10:32:20 2015
e@0: 
e@0: @author: Emmanouil Chourdakis
e@0: """
e@0: 
e@0: import essentia
e@0: from essentia import Pool
e@0: from essentia.standard import  *
e@0: from sys import argv
e@0: import csv
e@0: 
e@0: # reqyures matplotlib
e@0: from pylab import *
e@0: 
e@0: #requires numpy
e@0: from numpy import *
e@0: 
e@0: #requires scikit-learn
e@0: from sklearn.metrics import pairwise_distances
e@0: 
e@0: if __name__=="__main__":
e@0:     if len(argv) < 3:
e@0:         print("Please provide an input filename and a parameter value.")
e@0:     else:
e@0:         
e@0:         d = {}
e@0:         v = {}
e@0:         
e@0:         fname = argv[1]
e@0:         csvfname = argv[2]
e@0:         csvfile = open(csvfname)
e@0:         reader = csv.reader(csvfile)
e@0:         
e@0:         row = reader.next()
e@0:         print row
e@0:         
e@0:         for i in range(0, len(row)):
e@0:             v[i] = i
e@0:             d[i] = row[i]
e@0:         
e@0:         for row in reader:
e@0:             print row
e@0:         
e@0:         print("Demo with `%s\'" % fname)
e@0: 
e@0:         # Sampling Rate
e@0:         SR = 16000.0
e@0:         
e@0:         # Sampling Frequency
e@0:         T = 1.0/SR
e@0: 
e@0:         # FrameSize
e@0:         tframeSize = 23 #ms    
e@0:         frameSize = int(ceil(tframeSize*SR/1000)) if mod(ceil(tframeSize*SR/1000),2) == 0 \
e@0:                                                   else int(floor(tframeSize*SR/1000))
e@0:                                                   
e@0:         # HopSize
e@0:         hopSize = frameSize/2
e@0:         
e@0:         # Load Audio
e@0:         audio = MonoLoader(filename = fname)()
e@0:                         
e@0:         #Window Frames
e@0:         w = Windowing(size = frameSize, type = 'hamming')    
e@0:         
e@0:         # Spectrum
e@0:         spec = Spectrum()
e@0:         
e@0:         # Pool to append mean and variance
e@0:         pool = Pool()
e@0:         globalPool = Pool()
e@0:         
e@0:         # Spectral Contrast
e@0:         sc =  SpectralContrast(frameSize = frameSize, highFrequencyBound = 8000, sampleRate = SR)        
e@0:         
e@0:         # MFCCs
e@0:         mfccs = MFCC(highFrequencyBound = 8000, sampleRate = SR)
e@0:         
e@0:         # Onset Detection
e@0:         onsetdetection = OnsetDetectionGlobal(frameSize = frameSize, hopSize = hopSize, sampleRate = SR)(audio)
e@0:         onsets = Onsets()(essentia.array([onsetdetection]), [1])
e@0: 
e@0:         for o in range(0, len(onsets)-1):
e@0:             IOI = audio[onsets[o]*SR:onsets[o+1]*SR]
e@0:             
e@0:             if len(IOI) == 0:
e@0:                 break;
e@0:                 
e@0:             for frame in FrameGenerator(IOI, frameSize, hopSize):
e@0:                 framespectrum = spec(w(frame))
e@0:                 framecontrast = sc(framespectrum)
e@0:                 mfcc_coeffs = mfccs(framespectrum)[1]
e@0:                 
e@0:                 pool.add('lowlevel.mfcc.coeffs', mfcc_coeffs);
e@0:                 pool.add('lowlevel.spectrum.magnitude', framespectrum)
e@0:                 pool.add('lowlevel.contrast.contrast',  framecontrast[0])  
e@0:                 pool.add('lowlevel.contrast.valleys',  framecontrast[1])            
e@0:                 
e@0:                 
e@0:             
e@0:             spectrumfull = pool['lowlevel.spectrum.magnitude']
e@0:             spectralcontrast = pool['lowlevel.contrast.contrast']
e@0:             spectralvalleys = pool['lowlevel.contrast.valleys']
e@0:             
e@0:             spectralfeature = concatenate((spectralcontrast,spectralvalleys),1)
e@0:             mfccfeature = pool['lowlevel.mfcc.coeffs']
e@0:             
e@0:             globalPool.add('spectralcontrast', mean(spectralfeature, 0))
e@0:             globalPool.add('mfccs', mean(mfccfeature, 0))
e@0:             
e@0:             pool.clear()
e@0: 
e@0:             
e@0: 
e@0:     
e@0:         #simmfcc = pairwise_distances(globalPool['mfccs'], metric='cosine')
e@0:        # simmfcc = pairwise_distances(globalPool['spectralcontrast'], metric='cosine')        
e@0:         
e@0:         #plot(simmfcc[0,:])
e@0:         #figure()
e@0:         
e@0:         spectralcontrastmean = globalPool['spectralcontrast']
e@0:         
e@0:         imshow(spectralcontrastmean.T, aspect='auto', interpolation='none')
e@0:         output = YamlOutput(filename = "%s.yaml" % fname.partition('/')[2].partition('.')[0])(globalPool)
e@0: