eda.py

import os
from tqdm import tqdm
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import wavfile
from python_speech_features import mfcc, logfbank
import librosa
import glob
import sklearn
def plot_signals(signals):
    fig, axes = plt.subplots(nrows=2, ncols=5, sharex=False,
                             sharey=True, figsize=(20,5))
    fig.suptitle('Time Series', size=16)
    i = 0
    for x in range(2):
        for y in range(5):
            axes[x,y].set_title(list(signals.keys())[i])
            axes[x,y].plot(list(signals.values())[i])
            axes[x,y].get_xaxis().set_visible(False)
            axes[x,y].get_yaxis().set_visible(False)
            i += 1

def envelope(y,rate,threshold):
	mask=[]
	y=pd.Series(y).apply(np.abs)
	y_mean=y.rolling(window=int(rate/10),min_periods=1,center=True).mean()
	for mean in y_mean:
		if mean>threshold:
			mask.append(True)
		else:
			mask.append(False)
	return mask



def plot_fft(fft):
    fig, axes = plt.subplots(nrows=2, ncols=5, sharex=False,
                             sharey=True, figsize=(20,5))
    fig.suptitle('Fourier Transforms', size=16)
    i = 0
    for x in range(2):
        for y in range(5):
            data = list(fft.values())[i]
            Y, freq = data[0], data[1]
            axes[x,y].set_title(list(fft.keys())[i])
            axes[x,y].plot(freq, Y)
            axes[x,y].get_xaxis().set_visible(False)
            axes[x,y].get_yaxis().set_visible(False)
            i += 1

def plot_fbank(fbank):
    fig, axes = plt.subplots(nrows=2, ncols=5, sharex=False,
                             sharey=True, figsize=(200,50))
    fig.suptitle('Filter Bank Coefficients', size=16)
    i = 0
    for x in range(2):
        for y in range(5):
            axes[x,y].set_title(list(fbank.keys())[i])
            axes[x,y].imshow(list(fbank.values())[i],
                    cmap='hot', interpolation='nearest')
            axes[x,y].get_xaxis().set_visible(False)
            axes[x,y].get_yaxis().set_visible(False)
            i += 1

def plot_mfccs(mfccs):
    fig, axes = plt.subplots(nrows=5, ncols=2, sharex=False,
                             sharey=True, figsize=(200,50))
    fig.suptitle('Mel Frequency Cepstrum Coefficients', size=16)
    i = 0
    for x in range(5):
        for y in range(2):
            axes[x,y].set_title(list(mfccs.keys())[i])
            axes[x,y].imshow(list(mfccs.values())[i],
                    cmap='hot', interpolation='nearest')
            axes[x,y].get_xaxis().set_visible(False)
            axes[x,y].get_yaxis().set_visible(False)
            i += 1

def calc_fft(y,rate):
	n=len(y)
	freq=np.fft.rfftfreq(n,d=1/rate)
	Y=abs(np.fft.rfft(y)/n)
	return (Y,freq)

df= pd.read_csv('instruments.csv')


# classes=list(np.unique(df.label))
# class_dist=df.groupby(['label'])['length'].mean()

# fig,ax=plt.subplots()
# ax.pie(class_dist,labels=class_dist.index,autopct='%1.1f%%',shadow=False,startangle=90)
# ax.axis('equal')
# # plt.show()
# df.reset_index(inplace=True)

# signals={}
# fft={}
# fbank={}
# mfccs={}

# for c in classes:
# 	wav_file=df[df.label==c].iloc[0,0]
# 	signal,rate=librosa.load('FSDKaggle2018.audio_train/'+wav_file,sr=44100)
# 	mask=envelope(signal,rate,0.0005)
# 	signal=signal[mask]
# 	signals[c]=signal
# 	fft[c]=calc_fft(signal,rate)
# 	bank=logfbank(signal[:rate],rate,nfilt=26,nfft=1103).T
# 	fbank[c]=bank
# 	mel=mfcc(signal[:rate],rate,numcep=13,nfilt=26,nfft=1103).T
# 	mfccs[c]=mel
# plot_signals(signals)
# plt.show()
if len(os.listdir('clean'))==0:
	for f in tqdm(df.fname):
		signal,rate=librosa.load('wavfiles/'+f,sr=16000)
		mask=envelope(signal,rate,0.0005)
		wavfile.write(filename='clean/'+f,rate=rate,data=signal[mask])
# plot_fft(fft)
# # plt.show()


# plot_fbank(fbank)
# # plt.show()