part1 = 'Hello,'
part2 = 'AST world!'
print(...)  # FILL IN: Concatenate the two strings and insert a space

print('\nLet\'s do a headcount!')
for ... in ...:  # FILL IN: Start and end of the loop. Start at 1, end at 10!
  print(i)
print('Great, everyone is here.')

import numpy as np

a = np.array([1, 2])              # vector/"1D-array" with values 1 and 2
b = np.array([3, 4])              # same, values 3 and 4
ab1 = np.vstack((a, b))           # stacks them into a matrix
ab2 = np.array([[1, 2], [3, 4]])  # same as ab1
c = np.ones((2, 3))               # 2D-array with 2 rows and 3 columns, filled with ones.
d = np.log(ab2)                   # natural logarithm

e = [1, 2, 3, 4, 5]               # a list (not numpy)
f = list(np.arange(3, 8))         # [3, 4, 5, 6, 7]
g = np.sum(e)                     # sum of the elements in e
h = np.sum((e, f))                # sum of the elements in both e and f
i = np.sum(ab1, axis=0)           # sum per column of the matrix
ef = set(e).intersection(set(f))  # intersection of e and f

m = e[0]                          # first element of e
n = e[1:3]                        # indices 2 and 3 of e
o = e[-1]                         # last element of e
p = len(e)                        # number of elements in e
q = [1, 2, [3, 4,4,6,8,9,0,], 5]  # nested lists --> length of q?

print('a: {}'.format(a))
print('b: {}'.format(b))
print('ab1:\n {}'.format(ab1))
print('ab2:\n {}'.format(ab2))
print('c:\n {}'.format(c))
print('d:\n {}'.format(d))
print('g: {}'.format(g))
print('h: {}'.format(h))
print('i: {}'.format(i))
print('ef: {}'.format(ef))
print('m: {}, n: {}, o: {}'.format(m, n, o))
print('p: {}, len(q): {}'.format(p, len(q)))

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import IPython.display as ipd


### START CODING HERE ###
amp = 3
freq = 164.81
fs = 22050

t = ...                           # Set up time samples.
pha = 0

sine_wave = ...                   # Formula for sine wave. Use np where applicable.
print(np.round(..., decimals=2))  # FILL IN: Print the first 10 samples of your sine wave.

fig, ax = plt.subplots()
ax.plot(..., ...)                 # FILL IN: Plot the first 500 samples of your sine wave. Takes parameters time and data.
ax.set(xlabel='...', ylabel='...', title='Simple sine wave')  # FILL IN: Axis labels
ax.grid()
plt.show()
### END CODING HERE ###


ipd.Audio(sine_wave, rate=fs)

import os

current_os = os.name
current_path = os.getcwd()                        # get the current working directory
                                                  # 'nt' for windows, 'posix' for linux, among others
print("Current operating system: " + current_os)  # get current operating system
print("Current path: " + current_path)

new_folder = "data"                               # create a new sub directory
abs_path = os.path.join(current_path, new_folder) # concetenate path names
head, tail = os.path.split(abs_path)              # Split absolute path
base_name = os.path.basename(abs_path)
print("Absolute path: " + abs_path)
print("head: " + head)
print("tail: " + tail)
print("basename: " + base_name)

os.mkdir(abs_path)                                # create a new folder

os.chdir(new_folder)                              # enter folder

for i in range(4):                                # create 5 text files in the folder
  filename = "example_" + str(i) + ".txt"
  new_file = open(filename, 'w')
  new_file.close()

os.chdir("..")                                    # change back to parent directory

example_file = os.path.join(abs_path, "example_0.txt")

path_exists =  os.path.exists(abs_path)           # check if path exists
is_file = os.path.isfile(example_file)            # check if path is a file
is_dir = os.path.isdir(abs_path)                  # check if path is a directory

print("\n")
print("Does the path exist?: " + str(path_exists))
print("Is the path a file?: " + str(is_file))
print("Is the path a directory?: " + str(is_dir))

os.remove(example_file)

path_exists =  os.path.exists(abs_path)           # check if path exists
is_file = os.path.isfile(example_file)            # check if path is a file
is_dir = os.path.isdir(abs_path)                  # check if path is a directory

print("\n")
print("Does the path exist?: " + str(path_exists))
print("Is the path a file?: " + str(is_file))
print("Is the path a directory?: " + str(is_dir))

# Create 2 subfolders "speech" and "music" with 3 .txt files inside named
# speech_file_00x.txt or music_file_00x.txt respectivly. (replace '00x' with
# the actual file number)

import os
### Start code here

### End code here

os.chdir('..')

# walk through folder structure and remove content
for root, dirs, files in os.walk(abs_path, topdown=False):
  for name in files:
    print ("Removing: " + os.path.join(root, name))
    os.remove(os.path.join(root, name))
  for name in dirs:
      os.rmdir(os.path.join(root, name))
      print ("Removing: " + os.path.join(root, name))

os.rmdir(abs_path)

path_exists =  os.path.exists(abs_path)           # check if path exists
is_file = os.path.isfile(example_file)            # check if path is a file
is_dir = os.path.isdir(abs_path)                  # check if path is a directory

print("\n")
print("Does the path exist?: " + str(path_exists))
print("Is the path a file?: " + str(is_file))
print("Is the path a directory?: " + str(is_dir))

import librosa
import librosa.display
import numpy as np


### Create an (mag-)STFT from the sine wave
stft_sine = np.abs(librosa.stft(y=sine_wave, n_fft=1024, hop_length=1024, win_length=None, window='hann', center=True, pad_mode='reflect'))
stft_sine_db = librosa.amplitude_to_db(stft_sine, ref=np.max)

fig, ax = plt.subplots()
img = librosa.display.specshow(stft_sine_db, y_axis='log', x_axis='time', ax=ax)
ax.set(title='Power spectrogram')
#plt.colorbar(img, ax=ax, format="%+2.0f dB")
plt.plot()


### Create a chromagram from the STFT. Check the librosa doc!
chroma = ...

fig, ax = plt.subplots()
img = librosa.display.specshow(chroma, y_axis='chroma', x_axis='time', ax=ax)
ax.set(title='Chromagram')
#plt.colorbar(img, ax=ax)
plt.plot()

import librosa
import librosa.display
import matplotlib.pyplot as plt
import numpy as np
import IPython.display as ipd


### Example audio: Load 'Piano1-1.wav' using librosa. It has a sample rate of 44100. Convert it to mono.
piano_data, piano_sr = ...


### Turn into STFT, then db, then plot
piano_stft = ...
piano_stft_db = ...

fig, ax = plt.subplots()
img = librosa.display.specshow(..., y_axis='log', x_axis='time', ax=ax)
ax.set(title='Power spectrogram')
#plt.colorbar(img, ax=ax, format="%+2.0f dB")
plt.plot()


### Turn the STFT into a chromagram, then db, then plot
...
...

fig, ax = plt.subplots()
img = librosa.display.specshow(..., y_axis='chroma', x_axis='time', ax=ax)
ax.set(title='Chromagram')
#plt.colorbar(img, ax=ax)
plt.plot()

import librosa
import librosa.display
import matplotlib.pyplot as plt
import numpy as np


### Create the Mel-spectrogram from the piano data, then convert it from power to dB
piano_mel = ...
piano_mel_dB = ...


fig, ax = plt.subplots()
img = ...  # FILL IN: librosa's specshow()
ax.set(title='Mel-Spectrogram')
#plt.colorbar(img, ax=ax, format="%+2.0f dB")
plt.plot()

(0) Before we get started¶

(1) Hello world¶

(2) Python & numpy basics¶

(3) Sine wave¶

(4) Path operations with os library¶

(5) Short-Time Fourier Transform (STFT) & Chromagram (I): Sine wave¶

(6) Short-Time Fourier Transform (STFT) & Chromagram (II): Piano chords¶

(7) Mel-Spectrogram¶

(Coming up) Building a sound classification system - First steps¶

ESC-50 dataset¶