Audio Files with Librosa

Loading audio files

Librosa is a Python libray created for working with audio data. It's both easy to understand and has an extensive feuture list.
By default Librosa support all the popular audio file extensions, like WAV, OGG, MP3, and FLAC.

import librosa
import librosa.display

filename = librosa.ex('trumpet')
y, sr = librosa.load(filename)

y is a NumPy matrix that contains audio time series. If audio file is mono it will be one-dimensional vector, if audio file is stereo it will be two-dimensional, and so on. sr is a sampling rate of an audio file.

Playing audio in Jupyter notebook

Using the code bellow you will be able to play the audio inside of your Jupyter notebook. The player is very basic, but will be enough for simpler projects.

from IPython.display import Audio

Audio(data=y, rate=sr)

Vizualising audio files

import matplotlib.pyplot as plt

plt.figure(figsize=(12, 4))
librosa.display.waveshow(y, sr=sr)
plt.show()

fig, ax = plt.subplots()

S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128,
                                    fmax=8000)

S_dB = librosa.power_to_db(S, ref=np.max)
img = librosa.display.specshow(S_dB, x_axis='time',
                         y_axis='mel', sr=sr,
                         fmax=8000, ax=ax)
fig.colorbar(img, ax=ax, format='%+2.0f dB')
ax.set(title='Mel-frequency spectrogram')

Working with multiple audio files

from glob import glob

audio_filepaths = []

for filename in glob('./audio/*.wav'): 
    audio_filepaths += [filename]
    
audio_filepaths

padded_audio_files = []

max_allowed_length = 32000

for audio_filepath in audio_filepaths:

    y_voice, sr_voice = librosa.load(audio_filepath)
    
    if len(y_voice) > max_allowed_length:
        raise ValueError("data length cannot exceed padding length.")
    elif len(y_voice) < max_allowed_length:
        embedded_data = np.zeros(max_allowed_length)
        offset = np.random.randint(low = 0, high = max_allowed_length - len(y_voice))
        embedded_data[offset:offset+len(y_voice)] = y_voice
    elif len(y_voice) == max_allowed_length:
        embedded_data = y_voice
        pass
    
    padded_audio_files += [embedded_data]
    
padded_audio_files = np.array(padded_audio_files)

padded_audio_files.shape

Advanced Audio Analysis

S = np.abs(librosa.stft(y))

S.shape

D = librosa.amplitude_to_db(librosa.stft(y), ref=np.max)

D.shape

S = np.abs(librosa.stft(y))
times = librosa.times_like(S)
fig, ax = plt.subplots(nrows=2, sharex=True)
librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),
                         y_axis='log', x_axis='time', ax=ax[0])
ax[0].set(title='Power spectrogram')
ax[0].label_outer()


onset_env = librosa.onset.onset_strength(y=y, sr=sr)
ax[1].plot(times, 2 + onset_env / onset_env.max(), alpha=0.8,
           label='Mean (mel)')

onset_env = librosa.onset.onset_strength(y=y, sr=sr,
                                         aggregate=np.median,
                                         fmax=8000, n_mels=256)
ax[1].plot(times, 1 + onset_env / onset_env.max(), alpha=0.8,
           label='Median (custom mel)')

C = np.abs(librosa.cqt(y=y, sr=sr))
onset_env = librosa.onset.onset_strength(sr=sr, S=librosa.amplitude_to_db(C, ref=np.max))
ax[1].plot(times, onset_env / onset_env.max(), alpha=0.8,
         label='Mean (CQT)')
ax[1].legend()
ax[1].set(ylabel='Normalized strength', yticks=[])

Filters

fig, ax = plt.subplots(nrows=2, figsize=(12, 4), constrained_layout=True)

ax[0].set(title='Normal waveform')

librosa.display.waveshow(y, sr=sr, ax=ax[0])

sos = signal.butter(17, 150, 'hp', fs=1000, output='sos')
filtered = signal.sosfilt(sos, y)

ax[1].set(title='Filtered waveform')

librosa.display.waveshow(filtered, sr=sr, ax=ax[1])