In [2]:
%matplotlib inline
from ADTLib import ADT
import numpy, scipy, matplotlib.pyplot as plt, librosa, IPython.display as ipd
import stanford_mir; stanford_mir.init()

/Users/stjoa/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6
  return f(*args, **kwds)
/Users/stjoa/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters

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Drum Transcription using ADTLib

This notebook requires ADTLib. See ADTLib repo for installation instructions. If you experience problems, be sure to install the latest versions of tensorflow and dask.

Load the audio file into an array:

In [3]:
filename = 'audio/classic_rock_beat.mp3'
x, sr = librosa.load(filename)

Listen to the signal:

In [4]:
ipd.Audio(x, rate=sr)
Out[4]:

ADTLib

Use ADTLib to identify the location and types of each onset:

In [5]:
drum_onsets = ADT([filename])[0]

INFO:tensorflow:Restoring parameters from /Users/stjoa/anaconda3/lib/python3.6/site-packages/ADTLib/files/KickADTLibAll
INFO:tensorflow:Restoring parameters from /Users/stjoa/anaconda3/lib/python3.6/site-packages/ADTLib/files/SnareADTLibAll
INFO:tensorflow:Restoring parameters from /Users/stjoa/anaconda3/lib/python3.6/site-packages/ADTLib/files/HihatADTLibAll
In [6]:
drum_onsets
Out[6]:
{'Hihat': array([0.42956916, 0.65015873, 0.85913832, 1.07972789, 1.28870748,
        1.50929705, 1.71827664, 1.93886621, 2.1478458 , 2.36843537,
        2.57741497, 2.78639456, 3.00698413, 3.21596372, 3.42494331,
        3.64553288, 3.86612245, 4.07510204, 4.28408163, 4.5046712 ,
        4.72526077, 4.93424036, 5.15482993, 5.36380952, 5.57278912,
        5.79337868, 6.00235828, 6.21133787, 6.43192744, 6.64090703]),
 'Kick': array([1.49768707, 1.92725624, 2.1478458 , 2.78639456, 3.21596372,
        3.42494331, 4.07510204, 4.93424036, 5.57278912, 5.78176871,
        6.21133787, 6.64090703]),
 'Snare': array([0.85913832, 2.57741497, 4.28408163, 6.43192744])}

ADT also produces the file classic_rock_beat_drumtab.pdf which looks like this:

In [7]:
ipd.Image('img/classic_rock_beat_drumtab.png')
Out[7]:

Listen to onsets

For each type of drum, create a click track from the onsets, and listen to it with the original signal.

Bass/kick drum:

In [8]:
clicks = librosa.clicks(times=drum_onsets['Kick'], sr=sr, length=len(x))
ipd.Audio(x + clicks, rate=sr)
Out[8]:

Snare drum:

In [9]:
clicks = librosa.clicks(times=drum_onsets['Snare'], sr=sr, length=len(x))
ipd.Audio(x + clicks, rate=sr)
Out[9]:

Hi-hat:

In [10]:
clicks = librosa.clicks(times=drum_onsets['Hihat'], sr=sr, length=len(x))
ipd.Audio(x + clicks, rate=sr)
Out[10]:

Visualize spectrum

For each drum type, let's compute an average drum beat from the original signal and visualize the spectrum for that average drum beat.

Create a function that returns a log-amplitude spectrum of an average drum beat for a particular drum type:

In [11]:
def plot_avg_spectrum(x, onset_times):
    
    # Compute average drum beat signal.
    frame_sz = int(0.100*sr)
    def normalize(z): 
        return z/scipy.linalg.norm(z)
    onset_samples = librosa.time_to_samples(onset_times, sr=sr)
    x_avg = numpy.mean([normalize(x[i:i+frame_sz]) for i in onset_samples], axis=0)
    
    # Compute average spectrum.
    X = librosa.spectrum.fft.fft(x_avg)
    Xmag = librosa.amplitude_to_db(abs(X))
    
    # Plot spectrum.
    f = numpy.arange(frame_sz)*sr/frame_sz
    Nd2 = int(frame_sz/2)
    plt.figure(figsize=(14, 5))
    plt.plot(f[:Nd2], Xmag[:Nd2])
    plt.xlim(xmax=f[Nd2])
    plt.ylim([-50, 20])
    plt.xlabel('Frequency (Hertz)')

Plot the spectrum for an average bass drum:

In [12]:
plot_avg_spectrum(x, drum_onsets['Kick'])

Snare drum:

In [13]:
plot_avg_spectrum(x, drum_onsets['Snare'])

Hi-hat:

In [14]:
plot_avg_spectrum(x, drum_onsets['Hihat'])

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