diff --git a/python/melbank.py b/python/melbank.py
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+"""This module implements a Mel Filter Bank.
+In other words it is a filter bank with triangular shaped bands
+arnged on the mel frequency scale.
+An example ist shown in the following figure:
+.. plot::
+    from pylab import plt
+    import melbank
+    f1, f2 = 1000, 8000
+    melmat, (melfreq, fftfreq) = melbank.compute_melmat(6, f1, f2, num_fft_bands=4097)
+    fig, ax = plt.subplots(figsize=(8, 3))
+    ax.plot(fftfreq, melmat.T)
+    ax.grid(True)
+    ax.set_ylabel('Weight')
+    ax.set_xlabel('Frequency / Hz')
+    ax.set_xlim((f1, f2))
+    ax2 = ax.twiny()
+    ax2.xaxis.set_ticks_position('top')
+    ax2.set_xlim((f1, f2))
+    ax2.xaxis.set_ticks(melbank.mel_to_hertz(melfreq))
+    ax2.xaxis.set_ticklabels(['{:.0f}'.format(mf) for mf in melfreq])
+    ax2.set_xlabel('Frequency / mel')
+    plt.tight_layout()
+    fig, ax = plt.subplots()
+    ax.matshow(melmat)
+    plt.axis('equal')
+    plt.axis('tight')
+    plt.title('Mel Matrix')
+    plt.tight_layout()
+Functions
+---------
+"""
+
+
+from numpy import abs, append, arange, insert, linspace, log10, round, zeros
+
+
+def hertz_to_mel(freq):
+    """Returns mel-frequency from linear frequency input.
+    Parameter
+    ---------
+    freq : scalar or ndarray
+        Frequency value or array in Hz.
+    Returns
+    -------
+    mel : scalar or ndarray
+        Mel-frequency value or ndarray in Mel
+    """
+    return 2595.0 * log10(1 + (freq/700.0))
+
+
+def mel_to_hertz(mel):
+    """Returns frequency from mel-frequency input.
+    Parameter
+    ---------
+    mel : scalar or ndarray
+        Mel-frequency value or ndarray in Mel
+    Returns
+    -------
+    freq : scalar or ndarray
+        Frequency value or array in Hz.
+    """
+    return 700.0 * (10**(mel/2595.0)) - 700.0
+
+
+def melfrequencies_mel_filterbank(num_bands, freq_min, freq_max, num_fft_bands):
+    """Returns centerfrequencies and band edges for a mel filter bank
+    Parameters
+    ----------
+    num_bands : int
+        Number of mel bands.
+    freq_min : scalar
+        Minimum frequency for the first band.
+    freq_max : scalar
+        Maximum frequency for the last band.
+    num_fft_bands : int
+        Number of fft bands.
+    Returns
+    -------
+    center_frequencies_mel : ndarray
+    lower_edges_mel : ndarray
+    upper_edges_mel : ndarray
+    """
+
+    mel_max = hertz_to_mel(freq_max)
+    mel_min = hertz_to_mel(freq_min)
+    delta_mel = abs(mel_max - mel_min) / (num_bands + 1.0)
+    frequencies_mel = mel_min + delta_mel*arange(0, num_bands+2)
+    lower_edges_mel = frequencies_mel[:-2]
+    upper_edges_mel = frequencies_mel[2:]
+    center_frequencies_mel = frequencies_mel[1:-1]
+
+    return center_frequencies_mel, lower_edges_mel, upper_edges_mel
+
+
+def compute_melmat(num_mel_bands=12, freq_min=64, freq_max=8000,
+                   num_fft_bands=513, sample_rate=16000):
+    """Returns tranformation matrix for mel spectrum.
+    Parameters
+    ----------
+    num_mel_bands : int
+        Number of mel bands. Number of rows in melmat.
+        Default: 24
+    freq_min : scalar
+        Minimum frequency for the first band.
+        Default: 64
+    freq_max : scalar
+        Maximum frequency for the last band.
+        Default: 8000
+    num_fft_bands : int
+        Number of fft-frequenc bands. This ist NFFT/2+1 !
+        number of columns in melmat.
+        Default: 513   (this means NFFT=1024)
+    sample_rate : scalar
+        Sample rate for the signals that will be used.
+        Default: 44100
+    Returns
+    -------
+    melmat : ndarray
+        Transformation matrix for the mel spectrum.
+        Use this with fft spectra of num_fft_bands_bands length
+        and multiply the spectrum with the melmat
+        this will tranform your fft-spectrum
+        to a mel-spectrum.
+    frequencies : tuple (ndarray <num_mel_bands>, ndarray <num_fft_bands>)
+        Center frequencies of the mel bands, center frequencies of fft spectrum.
+    """
+    center_frequencies_mel, lower_edges_mel, upper_edges_mel =  \
+        melfrequencies_mel_filterbank(
+            num_mel_bands,
+            freq_min,
+            freq_max,
+            num_fft_bands
+    )
+
+    len_fft = float(num_fft_bands) / sample_rate
+    center_frequencies_hz = mel_to_hertz(center_frequencies_mel)
+    lower_edges_hz = mel_to_hertz(lower_edges_mel)
+    upper_edges_hz = mel_to_hertz(upper_edges_mel)
+    freqs = linspace(0.0, sample_rate/2.0, num_fft_bands)
+    melmat = zeros((num_mel_bands, num_fft_bands))
+
+    for imelband, (center, lower, upper) in enumerate(zip(
+            center_frequencies_hz, lower_edges_hz, upper_edges_hz)):
+
+        left_slope = (freqs >= lower)  == (freqs <= center)
+        melmat[imelband, left_slope] = (
+            (freqs[left_slope] - lower) / (center - lower)
+        )
+
+        right_slope = (freqs >= center) == (freqs <= upper)
+        melmat[imelband, right_slope] = (
+            (upper - freqs[right_slope]) / (upper - center)
+        )
+
+    return melmat, (center_frequencies_mel, freqs)
+
+# from pylab import plt
+
+# f1, f2 = 10, 12000
+# melmat, (melfreq, fftfreq) = compute_melmat(6, f1, f2, num_fft_bands=4097)
+
+# plt.plot(fftfreq, melmat.T)
+# # plt.show()
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