from pydub import AudioSegment from scipy import signal from operator import itemgetter import pyaudio import numpy as np import utils import os import sys import warnings import operator import matplotlib.mlab as mlab import matplotlib.pyplot as plt

FORMAT = pyaudio.paInt16 ''' Number of audio channels in the recording ''' CHANNELS = 2 ''' Original sample rate of the recordings ''' SAMPLE_RATE = 44100 ''' Sampling rate (after downsampling) ''' FS = 8000 ''' Factor by which the original signal will be downsampled ''' DECIMATION_FACTOR = SAMPLE_RATE/FS ''' Size of the FFT window, affects frequency granularity (we saw this in class!) ''' WINDOW_SIZE = 1024 ''' Degree to which a fingerprint can be paired with its neighbors -- higher will cause more fingerprints, but potentially better accuracy. ''' FAN_VALUE = 15 ''' Ratio by which each window overlaps the previous and next window -- higher will cause more fingerprints, but higher granularity of offset matching ''' OVERLAP_RATIO = 0.5 ''' Maximum time interval during which second peak must occur after the first peak ''' MAX_TIME = 150 path = os.getcwd() warnings.filterwarnings("ignore", message="divide by zero encountered in log10") warnings.filterwarnings("ignore", category=DeprecationWarning)

# Database with key=songname, value=[channel1, channel2] SongDb = {} #Goes through mp3s folder and adds each song to database for filename in os.listdir(path + "/mp3s/"): audiofile = AudioSegment.from_file(path + "/mp3s/" + filename) data = np.fromstring(audiofile._data, np.int16) channels = [] for chn in range(audiofile.channels): channels.append(data[chn::audiofile.channels]) SongDb[filename[:-3]] = channels print("Added to song database: " + str(filename[:-4]))

def Preprocess(channels): channel1 = channels[0] channel2 = channels[1] channelmean = ((channel1 + channel2)/2 - np.mean(channel1 + channel2)) resampled = signal.decimate(channelmean, int(DECIMATION_FACTOR)) return resampled

# Database with key=songname, value=processed signal ProcessedDb = {} #Processes each song and adds it to ProcessedDb #Prints table of number of samples in for each song print('{0:65}{1:22}{2:20}\n'.format('Song Name', 'Original #Samples', 'Processed #Samples')) for song, sig in SongDb.items(): processed = Preprocess(sig) ProcessedDb[song] = processed original_duration = len(sig[0]) processed_duration = len(processed) print('{0:50}{1:32d}{2:20d}'.format(song, original_duration, processed_duration))

def getSpectrogram(signal): # define scaling factor scale = 10 # take the spectrogram spectrogram, freq, t = mlab.specgram(signal, NFFT=WINDOW_SIZE, Fs=FS, noverlap=WINDOW_SIZE*OVERLAP_RATIO) # log compression for i in range(len(spectrogram)): spectrogram[i] = scale * np.log10(spectrogram[i]) # set infinite values to 0 spectrogram[np.nonzero(np.abs(spectrogram) == np.inf)] = 0 return spectrogram

''' TODO ''' # Database with key=songname, value=spectrogram Spectrograms = {} # Gets the spectrogram for each song and adds it to the Spectrograms database for song in ProcessedDb: Spectrograms[song] = getSpectrogram(ProcessedDb[song]) # Plots each spectrogram for song in Spectrograms: plt.figure() plt.title(song) plt.imshow(Spectrograms[song]) plt.show

''' TODO ''' # Database with key=songname, value=array of local peaks Peaks = {} # iterate through songs for song in Spectrograms: # Gets the local peaks for each song and adds it to the Peaks database frq, time, frqtimeArray = utils.get_2D_peaks(Spectrograms[song]) Peaks[song] = list(frqtimeArray) # Plots the peaks over the original spectrogram plt.figure() plt.title(song) plt.imshow(Spectrograms[song]) plt.plot(time, frq, '.', markersize=1) plt.show

''' TODO ''' def getPairs(peaks): # convert array to list (needed since we use len) peaks = list(peaks) # initialize variables to hold results and check the number of identified peaks for an index peakPairs = [] counter = np.zeros(len(peaks)) # loop through all of the peaks for i in range(len(peaks)): for j in range(len(peaks)): # check for various parameters for the two peaks counterCheckI = counter[i] < FAN_VALUE counterCheckJ = counter[j] < FAN_VALUE if (counterCheckI and counterCheckJ and (i != j)): # check to see if the time difference between the peaks is below the threshold timeDifference = np.abs(peaks[i][1] - peaks[j][1]) if (timeDifference <= MAX_TIME): # for a match, store the frequencies of the peaks and the time difference between them peakPairs.append((peaks[i][0], peaks[j][0], timeDifference)) return peakPairs

''' TODO ''' # Database with key=fingerprint (f1, f2, tdelta), value=songname LookUpTable = {} # Get fingerprints for each song stores them in the LookUpTable database for song in Peaks: # get the pairs from a given song pairs = getPairs(Peaks[song]) # store the fingerprint for pair in pairs: LookUpTable[tuple(pair)] = song # Prints a sample of the LookUpTable entries LookUpTableItems = list(LookUpTable.items()) print(str(LookUpTableItems[:][:5]))

# Database with key=songname, value=[channel1, channel2] for a snippet of the song TestDb = {} # Goes through test_mp3s folder and adds a snippet of each song to database for filename in os.listdir("./test_mp3s/"): audiofile = AudioSegment.from_file("./test_mp3s/" + filename) data = np.fromstring(audiofile._data, np.int16)[SAMPLE_RATE*60:SAMPLE_RATE*75] channels = [] for chn in range(audiofile.channels): channels.append(data[chn::audiofile.channels]) TestDb[filename] = channels print("Added to test database. : " + str(filename))

# Goes through test snippets and runs same fingerprinting process # Prints out the number of matches for each song and confidence of prediction for test in TestDb.keys(): print('\033[1mTesting: ' + test + '\033[0m \n') Matches = {} for song in SongDb.keys(): Matches[song] = 0 channels = TestDb[test] preprocessed = Preprocess(channels) spectrogram = getSpectrogram(preprocessed) _, _, peaks = utils.get_2D_peaks(spectrogram) pairs = getPairs(peaks) for p in pairs: match = LookUpTable.get(p, None) if match: Matches[match] += 1 prediction, count = max(Matches.items(), key=itemgetter(1)) for k,v in Matches.items(): if k == prediction: print('\033[1m{0:50} ==> {1:10d} \033[0m'.format(k, v)) else: print('{0:50} ==> {1:10d}'.format(k, v)) confidence = str(float(count)/sum(Matches.values())*100)[:5] + "%" prediction = max(Matches.items(), key=itemgetter(1)) print(f'\nPrediction: {prediction[0]}') print('\033[1m{0:10}: {1:10}\033[0m\n-----------------------------------------------------------------------\n\n'.format('Confidence', confidence))