#======= ADJUSTABLE PARAMETERS ================================================

num_total_folds = 10

whereAreTheFiles = "UrbanSound8K/audio/" #directory that contains the FOLDERS "fold1", "fold2" ... "fold10"

where2SaveIt = "" # "" to save it in the same location as the code, otherwise "path/to/folder/"

silence_threshold = -70.0 #the value that Karol uses

name_verite = ""

#==============================================================================

import numpy as np

import librosa

#import matplotlib.pyplot as plt

import os

import scipy.io

from parse import parse

FRAMES_PER_SEGMENT = 41 # 41 frames ~= 950 ms segment length @ 22050 Hz

TIME_WINDOW_SIZE = 512 * FRAMES_PER_SEGMENT # 23 ms per frame @ 22050 Hz #gives the 950ms from Piczak

STEP_SIZE = 512 * FRAMES_PER_SEGMENT // 2

sampl_freq_Hz = 22050

num_classes = 10

img_height = 60

img_width = 41

fft_window_len = 1024

N_vec = np.zeros(num_total_folds, np.float64)

N2_vec = np.zeros(num_total_folds, np.float64)

tooShortList_mat = np.zeros((num_total_folds, num_classes), np.float64)

classPriorsRaw_mat = np.zeros((num_total_folds, num_classes), np.float64)

classPriorsBeforeWindowing_mat = np.zeros((num_total_folds, num_classes), np.float64)

classPriorsAfterWindowing_mat = np.zeros((num_total_folds, num_classes), np.float64)

for fold_num in np.arange(1,num_total_folds+1):

print("")

print("-----------------------------------------------------------")

print("------------- FOLD %d -------------------------------------" %fold_num)

print("-----------------------------------------------------------")

direc = whereAreTheFiles + "fold" + str(fold_num)

audiofiles = os.listdir(direc)

audiofiles.remove(".DS_Store")

too_quiet_ctr = 0

N =len(audiofiles)

N_vec[fold_num-1] = N

clip_len = np.zeros(N, np.float64) #get the lengths of the respective audio sequences

observations_wav = np.zeros((0, TIME_WINDOW_SIZE), np.float64)

observations_spcgm = np.zeros((0, img_height, img_width), np.float64)

labels = np.zeros(0, int)

num_too_short = 0 #number of audioclips that are too short to give even one observation

classPriorsRaw = np.zeros(num_classes, np.float64)

classPriorsBeforeWindowing = np.zeros(num_classes, np.float64)

classPriorsAfterWindowing = np.zeros(num_classes, np.float64)

for n, au in enumerate(audiofiles):

tmp = parse("{}-{}-{}-{}.wav", au)

label_for_file = int(tmp[1])

classPriorsRaw[label_for_file] += 1

print("processing soundfile {0} of {1}, label {3}, named {2}".format(n, N, au, label_for_file))

audioclip, _ = librosa.load(direc + "/" + au, sr=sampl_freq_Hz)

clip_len[n] = len(audioclip)

classPriorsBeforeWindowing[label_for_file] += 1

normalization_factor = 1.0 / np.max(np.abs(audioclip)) #how Karol does it

audioclip *= normalization_factor #how Karol does it

s = 0

while True:

window_wav = audioclip[(s * STEP_SIZE):(s * STEP_SIZE + TIME_WINDOW_SIZE)] #how Karol does it

s+=1

if len(window_wav) < TIME_WINDOW_SIZE: break

window_spcgm = librosa.feature.melspectrogram(window_wav, hop_length=512, n_fft=fft_window_len, sr=sampl_freq_Hz, n_mels=img_height) #how Karol does it

window_spcgm = window_spcgm[:, :img_width] #for some reason the window_spcgm returned by window_spcgm has a width of 42 so we have to trim it to 41

window_spcgm = librosa.logamplitude(window_spcgm) #how Karol does it

if np.mean(window_spcgm) <= silence_threshold: #That's what Karol said

too_quiet_ctr += 1

else:

observations_spcgm = np.vstack((observations_spcgm, [window_spcgm]))

observations_wav = np.vstack((observations_wav, window_wav))

labels = np.hstack((labels, label_for_file)) #*np.ones(1, int)

classPriorsAfterWindowing[label_for_file] += 1

tooShortList = classPriorsRaw - classPriorsBeforeWindowing

tooShortList_mat[fold_num-1] = tooShortList

classPriorsRaw /= N

classPriorsRaw_mat[fold_num-1] = classPriorsRaw

classPriorsBeforeWindowing /= N - np.sum(tooShortList)

classPriorsBeforeWindowing_mat[fold_num-1] = classPriorsBeforeWindowing

N2 = np.shape(observations_wav)[0]

N2_vec[fold_num-1] = N2

classPriorsAfterWindowing /= N2

classPriorsAfterWindowing_mat[fold_num-1] = classPriorsAfterWindowing

print("augmented from {0} to {1} observations.".format(N, N2) )

print("{0} observations (after windowing) were silent and therefore discarded.".format(too_quiet_ctr))

name = where2SaveIt + "fold%d"%fold_num

scipy.io.savemat(name + name_verite + "_spcgm.mat", dict(ob_spcgm = observations_spcgm) )

scipy.io.savemat(name + name_verite + "_wav.mat", dict(ob_wav = observations_wav) )

scipy.io.savemat(name + name_verite + "_labels.mat", dict(lb = labels) )

scipy.io.savemat(name + name_verite + "_stats.mat",

dict(clipLen = clip_len, N=N, N2=N2, tooShortList = tooShortList, classPriorsRaw =classPriorsRaw,

classPriorsBeforeWindowing = classPriorsBeforeWindowing,

classPriorsAfterWindowing = classPriorsAfterWindowing) )

print("observations and labels were saved in")

print(name + name_verite + "_spcgm.mat")

print(name + name_verite + "_wav.mat")

print(name + name_verite + "_labels.mat")

scipy.io.savemat(where2SaveIt + "allfolds" + name_verite + "_stats.mat",

dict(N=N_vec, N2=N2_vec, tooShortList = tooShortList_mat, classPriorsRaw =classPriorsRaw_mat,

classPriorsBeforeWindowing = classPriorsBeforeWindowing_mat,

classPriorsAfterWindowing = classPriorsAfterWindowing_mat) )