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Initial Questions

• What is the distribution of all the categories in this data?

  • The .wave files are different lengths, so we can't just get the mean of number of wav files per label.

  • Solution: Create a matplotlib.pyplot with length!

Analyze distribution of categories in data using Pyplot

Prerequisite Knowledge:

• wavefile.read("file.wav") → (sample rate (int), data (numpy array)

  • Used this instead of librose.load() in this case because:

    • Faster in most cases

    • By default, wavefile does not normalize the data (get an accurate sample rate with no manipulation to the data) $SampleRate = \frac{samples}{sec}$

  • data.shape = (num_samples, num_channels)

  • Therefore, length = data.shape[0] / sample_rate

• np.array.shape → (width, height)

df = pd.read_csv('instruments.csv')
df.set_index('fname', inplace=True)

for fname in df.index:
    rate, signal = wavfile.read(f'wavfiles/{fname}')
    df.at[fname, 'length'] = signal.shape[0] / rate

classes = list(np.unique(df.label))

# Get mean of length for each label
class_dist = df.groupby(['label'])['length'].mean()

# Returns figure object and axis which we customize
fig, ax = plt.subplots()
ax.set_title('Class Distribution', y=1.08)
ax.pie(class_dist, labels=class_dist.index, autopct='%1.1f%%',
       shadow=False, startangle=90)
ax.axis('equal')
plt.show()