

In [1]:

    
%matplotlib inline
import seaborn
import numpy, scipy, IPython.display as ipd, matplotlib.pyplot as plt
import librosa, librosa.display
plt.rcParams['figure.figsize'] = (14, 5)

Pitch Transcription Exercise

Load an audio file.



In [2]:

    
filename = 'audio/simple_piano.wav'
x, sr = librosa.load(filename)

Play the audio file.



In [3]:

    
ipd.Audio(x, rate=sr)









    Out[3]:

Display the CQT of the signal.



In [4]:

    
bins_per_octave = 36
cqt = librosa.cqt(x, sr=sr, n_bins=300, bins_per_octave=bins_per_octave)
log_cqt = librosa.logamplitude(cqt)



In [5]:

    
cqt.shape









    Out[5]:





(300, 166)



In [6]:

    
librosa.display.specshow(log_cqt, sr=sr, x_axis='time', y_axis='cqt_note', 
                         bins_per_octave=bins_per_octave)









    Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x117cf2510>

Goal: Identify the pitch of each note and replace each note with a pure tone of that pitch.

Step 1: Detect Onsets

To accurately detect onsets, it may be helpful to see what the novelty function looks like:



In [7]:

    
hop_length = 100
onset_env = librosa.onset.onset_strength(x, sr=sr, hop_length=hop_length)



In [8]:

    
plt.plot(onset_env)
plt.xlim(0, len(onset_env))









    Out[8]:





(0, 850)

Among the obvious large peaks, there are many smaller peaks. We want to choose parameters which preserve the large peaks while ignoring the small peaks.

Next, we try to detect onsets. For more details, see librosa.onset.onset_detect and librosa.util.peak_pick.



In [9]:

    
onset_samples = librosa.onset.onset_detect(x,
                                           sr=sr, units='samples', 
                                           hop_length=hop_length, 
                                           backtrack=False,
                                           pre_max=20,
                                           post_max=20,
                                           pre_avg=100,
                                           post_avg=100,
                                           delta=0.2,
                                           wait=0)



In [10]:

    
onset_samples









    Out[10]:





array([ 5800, 11300, 22300, 33300, 44300, 55300, 66400])

Let's pad the onsets with the beginning and end of the signal.



In [11]:

    
onset_boundaries = numpy.concatenate([[0], onset_samples, [len(x)]])



In [12]:

    
print onset_boundaries









    



[    0  5800 11300 22300 33300 44300 55300 66400 84928]

Convert the onsets to units of seconds:



In [13]:

    
onset_times = librosa.samples_to_time(onset_boundaries, sr=sr)



In [14]:

    
onset_times









    Out[14]:





array([ 0.        ,  0.26303855,  0.51247166,  1.01133787,  1.51020408,
        2.00907029,  2.50793651,  3.01133787,  3.85160998])

Display the results of the onset detection:



In [15]:

    
librosa.display.waveplot(x, sr=sr)
plt.vlines(onset_times, -1, 1, color='r')









    Out[15]:





<matplotlib.collections.LineCollection at 0x10a3b3d90>

Step 2: Estimate Pitch

Estimate pitch using the autocorrelation method:



In [16]:

    
def estimate_pitch(segment, sr, fmin=50.0, fmax=2000.0):
    
    # Compute autocorrelation of input segment.
    r = librosa.autocorrelate(segment)
    
    # Define lower and upper limits for the autocorrelation argmax.
    i_min = sr/fmax
    i_max = sr/fmin
    r[:int(i_min)] = 0
    r[int(i_max):] = 0
    
    # Find the location of the maximum autocorrelation.
    i = r.argmax()
    f0 = float(sr)/i
    return f0

Step 3: Generate Pure Tone

Create a function to generate a pure tone at the specified frequency:



In [17]:

    
def generate_sine(f0, sr, n_duration):
    n = numpy.arange(n_duration)
    return 0.2*numpy.sin(2*numpy.pi*f0*n/float(sr))

Step 4: Put it together

Create a helper function for use in a list comprehension:



In [18]:

    
def estimate_pitch_and_generate_sine(x, onset_samples, i, sr):
    n0 = onset_samples[i]
    n1 = onset_samples[i+1]
    f0 = estimate_pitch(x[n0:n1], sr)
    return generate_sine(f0, sr, n1-n0)

Use a list comprehension to concatenate the synthesized segments:



In [19]:

    
y = numpy.concatenate([
    estimate_pitch_and_generate_sine(x, onset_boundaries, i, sr=sr)
    for i in range(len(onset_boundaries)-1)
])

Play the synthesized transcription.



In [20]:

    
ipd.Audio(y, rate=sr)









    Out[20]:

Plot the CQT of the synthesized transcription.



In [21]:

    
cqt = librosa.cqt(y, sr=sr)



In [22]:

    
librosa.display.specshow(abs(cqt), sr=sr, x_axis='time', y_axis='cqt_note')









    Out[22]:





<matplotlib.axes._subplots.AxesSubplot at 0x11a5f4d50>

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