diff --git a/docs/examples/plot_hprss.py b/docs/examples/plot_hprss.py
index 13ede1896b..faf2aad869 100644
--- a/docs/examples/plot_hprss.py
+++ b/docs/examples/plot_hprss.py
@@ -30,12 +30,17 @@
 ###############################################
 # Compute the short-time Fourier transform of y
 D = librosa.stft(y)
+D_power = librosa.magphase(D, power=2)[0]
+reference_power = np.max(D_power)
+D_dB = librosa.power_to_db(D_power, ref=rp)
 
 #####################################################
 # Decompose D into harmonic and percussive components
 #
 # :math:`D = D_\text{harmonic} + D_\text{percussive}`
 D_harmonic, D_percussive = librosa.decompose.hpss(D)
+D_harmonic_dB = librosa.power_to_db(librosa.magphase(D_harmonic, power=2)[0], ref=rp)
+D_percussive_dB = librosa.power_to_db(librosa.magphase(D_percussive, power=2)[0], ref=rp)
 
 
 ####################################################################
@@ -47,17 +52,17 @@
 plt.figure(figsize=(12, 8))
 
 plt.subplot(3, 1, 1)
-librosa.display.specshow(librosa.amplitude_to_db(D, ref=rp), y_axis='log')
+librosa.display.specshow(D_dB, y_axis='log')
 plt.colorbar()
 plt.title('Full spectrogram')
 
 plt.subplot(3, 1, 2)
-librosa.display.specshow(librosa.amplitude_to_db(D_harmonic, ref=rp), y_axis='log')
+librosa.display.specshow(D_harmonic_dB, y_axis='log')
 plt.colorbar()
 plt.title('Harmonic spectrogram')
 
 plt.subplot(3, 1, 3)
-librosa.display.specshow(librosa.amplitude_to_db(D_percussive, ref=rp), y_axis='log', x_axis='time')
+librosa.display.specshow(D_percussive_dB, y_axis='log', x_axis='time')
 plt.colorbar()
 plt.title('Percussive spectrogram')
 plt.tight_layout()
@@ -80,9 +85,20 @@
 
 # Let's compute separations for a few different margins and compare the results below
 D_harmonic2, D_percussive2 = librosa.decompose.hpss(D, margin=2)
+D_harmonic2_dB = librosa.power_to_db(librosa.magphase(D_harmonic2, power=2)[0], ref=rp)
+D_percussive2_dB = librosa.power_to_db(librosa.magphase(D_percussive2, power=2)[0], ref=rp)
+
 D_harmonic4, D_percussive4 = librosa.decompose.hpss(D, margin=4)
+D_harmonic4_dB = librosa.power_to_db(librosa.magphase(D_harmonic4, power=2)[0], ref=rp)
+D_percussive4_dB = librosa.power_to_db(librosa.magphase(D_percussive4, power=2)[0], ref=rp)
+
 D_harmonic8, D_percussive8 = librosa.decompose.hpss(D, margin=8)
+D_harmonic8_dB = librosa.power_to_db(librosa.magphase(D_harmonic8, power=2)[0], ref=rp)
+D_percussive8_dB = librosa.power_to_db(librosa.magphase(D_percussive8, power=2)[0], ref=rp)
+
 D_harmonic16, D_percussive16 = librosa.decompose.hpss(D, margin=16)
+D_harmonic16_dB = librosa.power_to_db(librosa.magphase(D_harmonic16, power=2)[0], ref=rp)
+D_percussive16_dB = librosa.power_to_db(librosa.magphase(D_percussive16, power=2)[0], ref=rp)
 
 
 #############################################################################
@@ -91,41 +107,41 @@
 plt.figure(figsize=(10, 10))
 
 plt.subplot(5, 2, 1)
-librosa.display.specshow(librosa.amplitude_to_db(D_harmonic, ref=rp), y_axis='log')
+librosa.display.specshow(D_harmonic_dB, y_axis='log')
 plt.title('Harmonic')
 plt.yticks([])
 plt.ylabel('margin=1')
 
 plt.subplot(5, 2, 2)
-librosa.display.specshow(librosa.amplitude_to_db(D_percussive, ref=rp), y_axis='log')
+librosa.display.specshow(D_percussive_dB, y_axis='log')
 plt.title('Percussive')
 plt.yticks([]), plt.ylabel('')
 
 plt.subplot(5, 2, 3)
-librosa.display.specshow(librosa.amplitude_to_db(D_harmonic2, ref=rp), y_axis='log')
+librosa.display.specshow(D_harmonic2_dB, y_axis='log')
 plt.yticks([])
 plt.ylabel('margin=2')
 
 plt.subplot(5, 2, 4)
-librosa.display.specshow(librosa.amplitude_to_db(D_percussive2, ref=rp), y_axis='log')
+librosa.display.specshow(D_percussive2_dB, y_axis='log')
 plt.yticks([]) ,plt.ylabel('')
 
 plt.subplot(5, 2, 5)
-librosa.display.specshow(librosa.amplitude_to_db(D_harmonic4, ref=rp), y_axis='log')
+librosa.display.specshow(D_harmonic4_dB, y_axis='log')
 plt.yticks([])
 plt.ylabel('margin=4')
 
 plt.subplot(5, 2, 6)
-librosa.display.specshow(librosa.amplitude_to_db(D_percussive4, ref=rp), y_axis='log')
+librosa.display.specshow(D_percussive4_dB, y_axis='log')
 plt.yticks([]), plt.ylabel('')
 
 plt.subplot(5, 2, 7)
-librosa.display.specshow(librosa.amplitude_to_db(D_harmonic8, ref=rp), y_axis='log')
+librosa.display.specshow(D_harmonic8_dB, y_axis='log')
 plt.yticks([])
 plt.ylabel('margin=8')
 
 plt.subplot(5, 2, 8)
-librosa.display.specshow(librosa.amplitude_to_db(D_percussive8, ref=rp), y_axis='log')
+librosa.display.specshow(D_percussive8_dB, y_axis='log')
 plt.yticks([]), plt.ylabel('')
 
 plt.subplot(5, 2, 9)
diff --git a/docs/examples/plot_presets.py b/docs/examples/plot_presets.py
index 488d4d0ffb..e1c9583da2 100644
--- a/docs/examples/plot_presets.py
+++ b/docs/examples/plot_presets.py
@@ -59,29 +59,25 @@
 ######################################################################
 # Now we can load in a file and do some analysis with the new defaults
 filename = 'audio/Karissa_Hobbs_-_09_-_Lets_Go_Fishin.mp3'
-
 y, sr = librosa.load(filename, duration=5, offset=35)
 
-# Generate a Mel spectrogram:
-
+# Generate a mel spectrogram:
 M = librosa.feature.melspectrogram(y=y)
 
 # Of course, you can still override the new default manually, e.g.:
-
-M_highres = librosa.feature.melspectrogram(y=y, hop_length=512)
-
+M_hop512 = librosa.feature.melspectrogram(y=y, hop_length=512)
 
 # And plot the results
 plt.figure(figsize=(6, 6))
 ax = plt.subplot(3, 1, 1)
 
-librosa.display.specshow(librosa.power_to_db(M, ref=np.max),
+librosa.display.specshow(librosa.amplitude_to_db(M, ref=np.max),
                          y_axis='mel', x_axis='time')
 
 plt.title('44100/1024/4096')
 
 plt.subplot(3, 1, 2, sharex=ax, sharey=ax)
-librosa.display.specshow(librosa.power_to_db(M_highres, ref=np.max),
+librosa.display.specshow(librosa.amplitude_to_db(M_hop512, ref=np.max),
                          hop_length=512,
                          y_axis='mel', x_axis='time')
 plt.title('44100/512/4096')
@@ -91,10 +87,10 @@
 librosa['sr'] = 11025
 
 y2, sr2 = librosa.load(filename, duration=5, offset=35)
-M2 = librosa.feature.melspectrogram(y=y2, sr=sr2)
+M_11k = librosa.feature.melspectrogram(y=y2, sr=sr2)
 
 plt.subplot(3, 1, 3, sharex=ax, sharey=ax)
-librosa.display.specshow(librosa.power_to_db(M2, ref=np.max),
+librosa.display.specshow(librosa.amplitude_to_db(M_11k, ref=np.max),
                          y_axis='mel', x_axis='time')
 
 plt.title('11025/1024/4096')
diff --git a/docs/examples/plot_segmentation.py b/docs/examples/plot_segmentation.py
index 9d938eb5f6..198d84fa66 100644
--- a/docs/examples/plot_segmentation.py
+++ b/docs/examples/plot_segmentation.py
@@ -43,13 +43,14 @@
 # Next, we'll compute and plot a log-power CQT
 BINS_PER_OCTAVE = 12 * 3
 N_OCTAVES = 7
-C = librosa.amplitude_to_db(librosa.cqt(y=y, sr=sr,
-                                        bins_per_octave=BINS_PER_OCTAVE,
-                                        n_bins=N_OCTAVES * BINS_PER_OCTAVE),
-                            ref=np.max)
+C = librosa.cqt(y=y, sr=sr,
+                bins_per_octave=BINS_PER_OCTAVE,
+                n_bins=N_OCTAVES * BINS_PER_OCTAVE),
+    ref=np.max)
+C_dB = librosa.power_to_db(librosa.magphase(C, power=2)[0])
 
 plt.figure(figsize=(12, 4))
-librosa.display.specshow(C, y_axis='cqt_hz', sr=sr,
+librosa.display.specshow(C_dB, y_axis='cqt_hz', sr=sr,
                          bins_per_octave=BINS_PER_OCTAVE,
                          x_axis='time')
 plt.tight_layout()
@@ -58,7 +59,7 @@
 ##########################################################
 # To reduce dimensionality, we'll beat-synchronous the CQT
 tempo, beats = librosa.beat.beat_track(y=y, sr=sr, trim=False)
-Csync = librosa.util.sync(C, beats, aggregate=np.median)
+Csync_dB = librosa.util.sync(C_dB, beats, aggregate=np.median)
 
 # For plotting purposes, we'll need the timing of the beats
 # we fix_frames to include non-beat frames 0 and C.shape[1] (final frame)
@@ -68,7 +69,7 @@
                                     sr=sr)
 
 plt.figure(figsize=(12, 4))
-librosa.display.specshow(Csync, bins_per_octave=12*3,
+librosa.display.specshow(Csync_dB, bins_per_octave=12*3,
                          y_axis='cqt_hz', x_axis='time',
                          x_coords=beat_times)
 plt.tight_layout()
@@ -79,7 +80,7 @@
 # (Equation 1)
 # width=3 prevents links within the same bar
 # mode='affinity' here implements S_rep (after Eq. 8)
-R = librosa.segment.recurrence_matrix(Csync, width=3, mode='affinity',
+R = librosa.segment.recurrence_matrix(Csync_dB, width=3, mode='affinity',
                                       sym=True)
 
 # Enhance diagonals with a median filter (Equation 2)
@@ -230,7 +231,7 @@
                                 fmin=librosa.note_to_hz('C1'),
                                 bins_per_octave=BINS_PER_OCTAVE)
 
-librosa.display.specshow(C, y_axis='cqt_hz', sr=sr,
+librosa.display.specshow(C_dB, y_axis='cqt_hz', sr=sr,
                          bins_per_octave=BINS_PER_OCTAVE,
                          x_axis='time')
 ax = plt.gca()
diff --git a/docs/examples/plot_superflux.py b/docs/examples/plot_superflux.py
index 2a1b0292bd..bd2634cd52 100644
--- a/docs/examples/plot_superflux.py
+++ b/docs/examples/plot_superflux.py
@@ -55,8 +55,12 @@
                                    n_mels=n_mels)
 
 
+# Extract magnitude and convert to dB
+S_dB = librosa.amplitude_to_db(librosa.magphase(S)[0], ref=np.max)
+
+
 plt.figure(figsize=(6, 4))
-librosa.display.specshow(librosa.power_to_db(S, ref=np.max),
+librosa.display.specshow(S_dB,
                          y_axis='mel', x_axis='time', sr=sr,
                          hop_length=hop_length, fmin=fmin, fmax=fmax)
 plt.tight_layout()
@@ -72,7 +76,7 @@
 
 #########################################
 # And similarly with the superflux method
-odf_sf = librosa.onset.onset_strength(S=librosa.power_to_db(S, ref=np.max),
+odf_sf = librosa.onset.onset_strength(S=S_dB,
                                       sr=sr,
                                       hop_length=hop_length,
                                       lag=lag, max_size=max_size)
@@ -100,7 +104,7 @@
                                     hop_length=hop_length)
 
 ax = plt.subplot(2, 1, 2)
-librosa.display.specshow(librosa.power_to_db(S, ref=np.max),
+librosa.display.specshow(S_dB,
                          y_axis='mel', x_axis='time', sr=sr,
                          hop_length=hop_length, fmin=fmin, fmax=fmax)
 plt.xlim([0, 5.0])
diff --git a/examples/LibROSA audio effects and playback.ipynb b/examples/LibROSA audio effects and playback.ipynb
index 848fb71467..d8303ca8c3 100644
--- a/examples/LibROSA audio effects and playback.ipynb	
+++ b/examples/LibROSA audio effects and playback.ipynb	
@@ -256,7 +256,8 @@
     "plt.figure(figsize=(12,4))\n",
     "\n",
     "plt.subplot(1,2,1)\n",
-    "librosa.display.specshow(librosa.amplitude_to_db(components, ref=np.max), y_axis='log')\n",
+    "components_db = librosa.amplitude_to_db(np.abs(components), ref=np.max)\n",
+    "librosa.display.specshow(components_db, y_axis='log')\n",
     "plt.xlabel('Component')\n",
     "plt.ylabel('Frequency')\n",
     "plt.title('Components')\n",
diff --git a/examples/LibROSA demo.ipynb b/examples/LibROSA demo.ipynb
index 730d232dd3..d9836850c5 100644
--- a/examples/LibROSA demo.ipynb	
+++ b/examples/LibROSA demo.ipynb	
@@ -108,11 +108,12 @@
     }
    ],
    "source": [
-    "# Let's make and display a mel-scaled power (energy-squared) spectrogram\n",
+    "# Let's make and display a mel-scaled power (squared energy) spectrogram\n",
     "S = librosa.feature.melspectrogram(y, sr=sr, n_mels=128)\n",
+    "S_power = librosa.magphase(S, power=2)[0]\n",
     "\n",
     "# Convert to log scale (dB). We'll use the peak power (max) as reference.\n",
-    "log_S = librosa.power_to_db(S, ref=np.max)\n",
+    "log_S = librosa.amplitude_to_db(S_power, ref=np.max)\n",
     "\n",
     "# Make a new figure\n",
     "plt.figure(figsize=(12,4))\n",
@@ -167,13 +168,15 @@
    ],
    "source": [
     "# What do the spectrograms look like?\n",
-    "# Let's make and display a mel-scaled power (energy-squared) spectrogram\n",
+    "# Let's make and display a mel-scaled power (squared energy) spectrogram\n",
     "S_harmonic   = librosa.feature.melspectrogram(y_harmonic, sr=sr)\n",
     "S_percussive = librosa.feature.melspectrogram(y_percussive, sr=sr)\n",
+    "Sh_power = librosa.magphase(S_harmonic, power=2)[0]\n",
+    "Sp_power = librosa.magphase(S_percussive, power=2)[0]\n",
     "\n",
     "# Convert to log scale (dB). We'll use the peak power as reference.\n",
-    "log_Sh = librosa.power_to_db(S_harmonic, ref=np.max)\n",
-    "log_Sp = librosa.power_to_db(S_percussive, ref=np.max)\n",
+    "log_Sh = librosa.power_to_db(Sh_power, ref=np.max)\n",
+    "log_Sp = librosa.power_to_db(Sp_power, ref=np.max)\n",
     "\n",
     "# Make a new figure\n",
     "plt.figure(figsize=(12,6))\n",
diff --git a/librosa/core/audio.py b/librosa/core/audio.py
index 2aaa29c058..52adf4b286 100644
--- a/librosa/core/audio.py
+++ b/librosa/core/audio.py
@@ -647,8 +647,9 @@ def clicks(times=None, frames=None, sr=22050, hop_length=512,
     >>> import matplotlib.pyplot as plt
     >>> plt.figure()
     >>> S = librosa.feature.melspectrogram(y=y, sr=sr)
+    >>> S_power = librosa.magphase(S, power=2)[0]
     >>> ax = plt.subplot(2,1,2)
-    >>> librosa.display.specshow(librosa.power_to_db(S, ref=np.max),
+    >>> librosa.display.specshow(librosa.power_to_db(S_power, ref=np.max),
     ...                          x_axis='time', y_axis='mel')
     >>> plt.subplot(2,1,1, sharex=ax)
     >>> librosa.display.waveplot(y_beat_times, sr=sr, label='Beat clicks')
diff --git a/librosa/core/constantq.py b/librosa/core/constantq.py
index bd11b4180b..c598ff0f28 100644
--- a/librosa/core/constantq.py
+++ b/librosa/core/constantq.py
@@ -119,7 +119,8 @@ def cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
     >>> import matplotlib.pyplot as plt
     >>> y, sr = librosa.load(librosa.util.example_audio_file())
     >>> C = librosa.cqt(y, sr=sr)
-    >>> librosa.display.specshow(librosa.amplitude_to_db(C, ref=np.max),
+    >>> C_dB = librosa.power_to_db(librosa.magphase(C, power=2)[0], ref=np.max)
+    >>> librosa.display.specshow(C_dB,
     ...                          sr=sr, x_axis='time', y_axis='cqt_note')
     >>> plt.colorbar(format='%+2.0f dB')
     >>> plt.title('Constant-Q power spectrum')
diff --git a/librosa/core/spectrum.py b/librosa/core/spectrum.py
index 31dac103c3..a85c9ff6f5 100644
--- a/librosa/core/spectrum.py
+++ b/librosa/core/spectrum.py
@@ -131,9 +131,8 @@ def stft(y, n_fft=2048, hop_length=None, win_length=None, window='hann',
 
 
     >>> import matplotlib.pyplot as plt
-    >>> librosa.display.specshow(librosa.amplitude_to_db(D,
-    ...                                                  ref=np.max),
-    ...                          y_axis='log', x_axis='time')
+    >>> D_dB = librosa.power_to_db(librosa.magphase(D, power=2)[0], ref=np.max)
+    >>> librosa.display.specshow(D_dB, y_axis='log', x_axis='time')
     >>> plt.title('Power spectrogram')
     >>> plt.colorbar(format='%+2.0f dB')
     >>> plt.tight_layout()
@@ -810,8 +809,8 @@ def power_to_db(S, ref=1.0, amin=1e-10, top_db=80.0):
     Get a power spectrogram from a waveform ``y``
 
     >>> y, sr = librosa.load(librosa.util.example_audio_file())
-    >>> S = np.abs(librosa.stft(y))
-    >>> librosa.power_to_db(S**2)
+    >>> S_power = librosa.magphase(librosa.stft(y), power=2)[0]
+    >>> librosa.power_to_db(S_power)
     array([[-33.293, -27.32 , ..., -33.293, -33.293],
            [-33.293, -25.723, ..., -33.293, -33.293],
            ...,
@@ -820,7 +819,7 @@ def power_to_db(S, ref=1.0, amin=1e-10, top_db=80.0):
 
     Compute dB relative to peak power
 
-    >>> librosa.power_to_db(S**2, ref=np.max)
+    >>> librosa.power_to_db(S_power, ref=np.max)
     array([[-80.   , -74.027, ..., -80.   , -80.   ],
            [-80.   , -72.431, ..., -80.   , -80.   ],
            ...,
@@ -830,7 +829,7 @@ def power_to_db(S, ref=1.0, amin=1e-10, top_db=80.0):
 
     Or compare to median power
 
-    >>> librosa.power_to_db(S**2, ref=np.median)
+    >>> librosa.power_to_db(S_power, ref=np.median)
     array([[-0.189,  5.784, ..., -0.189, -0.189],
            [-0.189,  7.381, ..., -0.189, -0.189],
            ...,
@@ -843,11 +842,11 @@ def power_to_db(S, ref=1.0, amin=1e-10, top_db=80.0):
     >>> import matplotlib.pyplot as plt
     >>> plt.figure()
     >>> plt.subplot(2, 1, 1)
-    >>> librosa.display.specshow(S**2, sr=sr, y_axis='log')
+    >>> librosa.display.specshow(S_power, sr=sr, y_axis='log')
     >>> plt.colorbar()
     >>> plt.title('Power spectrogram')
     >>> plt.subplot(2, 1, 2)
-    >>> librosa.display.specshow(librosa.power_to_db(S**2, ref=np.max),
+    >>> librosa.display.specshow(librosa.power_to_db(S_power, ref=np.max),
     ...                          sr=sr, y_axis='log', x_axis='time')
     >>> plt.colorbar(format='%+2.0f dB')
     >>> plt.title('Log-Power spectrogram')
@@ -861,9 +860,9 @@ def power_to_db(S, ref=1.0, amin=1e-10, top_db=80.0):
         raise ParameterError('amin must be strictly positive')
 
     if np.issubdtype(S.dtype, np.complexfloating):
-        warnings.warn('power_to_db was called on complex input so phase '
+        warnings.warn('power_to_db was called on complex input. Therefore, phase '
                       'information will be discarded. To suppress this warning, '
-                      'call power_to_db(magphase(D, power=2)[0]) instead.')
+                      'call power_to_db(magphase(S, power=2)[0]) instead.')
         magnitude = np.abs(S)
     else:
         magnitude = S
@@ -956,9 +955,9 @@ def amplitude_to_db(S, ref=1.0, amin=1e-5, top_db=80.0):
     S = np.asarray(S)
 
     if np.issubdtype(S.dtype, np.complexfloating):
-        warnings.warn('amplitude_to_db was called on complex input so phase '
+        warnings.warn('amplitude_to_db was called on complex input. Therefore, phase '
                       'information will be discarded. To suppress this warning, '
-                      'call amplitude_to_db(magphase(D)[0]) instead.')
+                      'call amplitude_to_db(np.abs(S)) instead.')
 
     magnitude = np.abs(S)
 
@@ -1039,9 +1038,10 @@ def perceptual_weighting(S, frequencies, **kwargs):
 
     >>> y, sr = librosa.load(librosa.util.example_audio_file())
     >>> CQT = librosa.cqt(y, sr=sr, fmin=librosa.note_to_hz('A1'))
-    >>> freqs = librosa.cqt_frequencies(CQT.shape[0],
+    >>> CQT_power = librosa.magphase(CQT, power=2)[0]
+    >>> freqs = librosa.cqt_frequencies(CQT_power.shape[0],
     ...                                 fmin=librosa.note_to_hz('A1'))
-    >>> perceptual_CQT = librosa.perceptual_weighting(CQT**2,
+    >>> perceptual_CQT = librosa.perceptual_weighting(CQT_power,
     ...                                               freqs,
     ...                                               ref=np.max)
     >>> perceptual_CQT
@@ -1054,7 +1054,7 @@ def perceptual_weighting(S, frequencies, **kwargs):
     >>> import matplotlib.pyplot as plt
     >>> plt.figure()
     >>> plt.subplot(2, 1, 1)
-    >>> librosa.display.specshow(librosa.amplitude_to_db(CQT,
+    >>> librosa.display.specshow(librosa.power_to_db(CQT_power,
     ...                                                  ref=np.max),
     ...                          fmin=librosa.note_to_hz('A1'),
     ...                          y_axis='cqt_hz')
diff --git a/librosa/display.py b/librosa/display.py
index a95fccf0ff..5d4fa7e872 100644
--- a/librosa/display.py
+++ b/librosa/display.py
@@ -580,9 +580,10 @@ def specshow(data, x_coords=None, y_coords=None,
     >>> y, sr = librosa.load(librosa.util.example_audio_file())
     >>> plt.figure(figsize=(12, 8))
 
-    >>> D = librosa.amplitude_to_db(librosa.stft(y), ref=np.max)
+    >>> D = librosa.stft(y)
+    >>> D_dB = librosa.power_to_db(librosa.magphase(D, power=2)[0], ref=np.max)
     >>> plt.subplot(4, 2, 1)
-    >>> librosa.display.specshow(D, y_axis='linear')
+    >>> librosa.display.specshow(D_dB, y_axis='linear')
     >>> plt.colorbar(format='%+2.0f dB')
     >>> plt.title('Linear-frequency power spectrogram')
 
@@ -590,21 +591,22 @@ def specshow(data, x_coords=None, y_coords=None,
     Or on a logarithmic scale
 
     >>> plt.subplot(4, 2, 2)
-    >>> librosa.display.specshow(D, y_axis='log')
+    >>> librosa.display.specshow(D_dB, y_axis='log')
     >>> plt.colorbar(format='%+2.0f dB')
     >>> plt.title('Log-frequency power spectrogram')
 
 
     Or use a CQT scale
 
-    >>> CQT = librosa.amplitude_to_db(librosa.cqt(y, sr=sr), ref=np.max)
+    >>> CQT = librosa.cqt(y, sr=sr)
+    >>> CQT_dB = librosa.power_to_db(librosa.magphase(CQT, power=2)[0], ref=np.max)
     >>> plt.subplot(4, 2, 3)
     >>> librosa.display.specshow(CQT, y_axis='cqt_note')
     >>> plt.colorbar(format='%+2.0f dB')
     >>> plt.title('Constant-Q power spectrogram (note)')
 
     >>> plt.subplot(4, 2, 4)
-    >>> librosa.display.specshow(CQT, y_axis='cqt_hz')
+    >>> librosa.display.specshow(CQT_dB, y_axis='cqt_hz')
     >>> plt.colorbar(format='%+2.0f dB')
     >>> plt.title('Constant-Q power spectrogram (Hz)')
 
@@ -621,7 +623,7 @@ def specshow(data, x_coords=None, y_coords=None,
     Force a grayscale colormap (white -> black)
 
     >>> plt.subplot(4, 2, 6)
-    >>> librosa.display.specshow(D, cmap='gray_r', y_axis='linear')
+    >>> librosa.display.specshow(D_dB, cmap='gray_r', y_axis='linear')
     >>> plt.colorbar(format='%+2.0f dB')
     >>> plt.title('Linear power spectrogram (grayscale)')
 
@@ -629,7 +631,7 @@ def specshow(data, x_coords=None, y_coords=None,
     Draw time markers automatically
 
     >>> plt.subplot(4, 2, 7)
-    >>> librosa.display.specshow(D, x_axis='time', y_axis='log')
+    >>> librosa.display.specshow(D_dB, x_axis='time', y_axis='log')
     >>> plt.colorbar(format='%+2.0f dB')
     >>> plt.title('Log power spectrogram')
 
diff --git a/librosa/feature/spectral.py b/librosa/feature/spectral.py
index f631200c70..30181f7e1a 100644
--- a/librosa/feature/spectral.py
+++ b/librosa/feature/spectral.py
@@ -1467,13 +1467,12 @@ def melspectrogram(y=None, sr=22050, S=None, n_fft=2048, hop_length=512,
 
     Using a pre-computed power spectrogram
 
-    >>> D = np.abs(librosa.stft(y))**2
+    >>> D = librosa.magphase(librosa.stft(y), power=2)[0]
     >>> S = librosa.feature.melspectrogram(S=D)
 
     >>> # Passing through arguments to the Mel filters
     >>> S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128,
     ...                                     fmax=8000)
-
     >>> import matplotlib.pyplot as plt
     >>> plt.figure(figsize=(10, 4))
     >>> librosa.display.specshow(librosa.power_to_db(S,
diff --git a/librosa/onset.py b/librosa/onset.py
index 0c17ee7df6..b59e2912fc 100644
--- a/librosa/onset.py
+++ b/librosa/onset.py
@@ -120,10 +120,10 @@ def onset_detect(y=None, sr=22050, onset_envelope=None, hop_length=512,
 
     >>> import matplotlib.pyplot as plt
     >>> D = librosa.stft(y)
+    >>> D_dB = librosa.power_to_db(librosa.magphase(D, power=2)[0], ref=np.max)
     >>> plt.figure()
     >>> ax1 = plt.subplot(2, 1, 1)
-    >>> librosa.display.specshow(librosa.amplitude_to_db(D, ref=np.max),
-    ...                          x_axis='time', y_axis='log')
+    >>> librosa.display.specshow(D_dB, x_axis='time', y_axis='log')
     >>> plt.title('Power spectrogram')
     >>> plt.subplot(2, 1, 2, sharex=ax1)
     >>> plt.plot(times, o_env, label='Onset strength')
@@ -270,11 +270,11 @@ def onset_strength(y=None, sr=22050, S=None, lag=1, max_size=1,
     >>> y, sr = librosa.load(librosa.util.example_audio_file(),
     ...                      duration=10.0)
     >>> D = librosa.stft(y)
+    >>> D_dB = librosa.power_to_db(librosa.magphase(D, power=2)[0], ref=np.max)
     >>> times = librosa.frames_to_time(np.arange(D.shape[1]))
     >>> plt.figure()
     >>> ax1 = plt.subplot(2, 1, 1)
-    >>> librosa.display.specshow(librosa.amplitude_to_db(D, ref=np.max),
-    ...                          y_axis='log', x_axis='time')
+    >>> librosa.display.specshow(D_dB, y_axis='log', x_axis='time')
     >>> plt.title('Power spectrogram')
 
     Construct a standard onset function
@@ -474,10 +474,10 @@ def onset_strength_multi(y=None, sr=22050, S=None, lag=1, max_size=1,
     >>> y, sr = librosa.load(librosa.util.example_audio_file(),
     ...                      duration=10.0)
     >>> D = librosa.stft(y)
+    >>> D_dB = librosa.power_to_db(librosa.magphase(D)[0], ref=np.max)
     >>> plt.figure()
     >>> plt.subplot(2, 1, 1)
-    >>> librosa.display.specshow(librosa.amplitude_to_db(D, ref=np.max),
-    ...                          y_axis='log')
+    >>> librosa.display.specshow(D_dB, y_axis='log')
     >>> plt.title('Power spectrogram')
 
     Construct a standard onset function over four sub-bands
diff --git a/librosa/util/utils.py b/librosa/util/utils.py
index 3fe9434bde..539f4c5be3 100644
--- a/librosa/util/utils.py
+++ b/librosa/util/utils.py
@@ -898,8 +898,8 @@ def peak_pick(x, pre_max, post_max, pre_avg, post_avg, delta, wait):
     >>> plt.figure()
     >>> ax = plt.subplot(2, 1, 2)
     >>> D = librosa.stft(y)
-    >>> librosa.display.specshow(librosa.amplitude_to_db(D, ref=np.max),
-    ...                          y_axis='log', x_axis='time')
+    >>> D_dB = librosa.power_to_db(librosa.magphase(D, power=2)[0], ref=np.max)
+    >>> librosa.display.specshow(D_dB, y_axis='log', x_axis='time')
     >>> plt.subplot(2, 1, 1, sharex=ax)
     >>> plt.plot(times, onset_env, alpha=0.8, label='Onset strength')
     >>> plt.vlines(times[peaks], 0,
@@ -1305,7 +1305,7 @@ def sync(data, idx, aggregate=None, pad=True, axis=-1):
 
     >>> y, sr = librosa.load(librosa.util.example_audio_file())
     >>> tempo, beats = librosa.beat.beat_track(y=y, sr=sr, trim=False)
-    >>> cqt = librosa.cqt(y=y, sr=sr)
+    >>> cqt = librosa.magphase(librosa.cqt(y=y, sr=sr), power=2)[0]
     >>> beats = librosa.util.fix_frames(beats, x_max=cqt.shape[1])
 
     By default, use mean aggregation
@@ -1331,18 +1331,18 @@ def sync(data, idx, aggregate=None, pad=True, axis=-1):
     >>> subbeat_t = librosa.frames_to_time(sub_beats, sr=sr)
     >>> plt.figure()
     >>> plt.subplot(3, 1, 1)
-    >>> librosa.display.specshow(librosa.amplitude_to_db(cqt,
+    >>> librosa.display.specshow(librosa.power_to_db(cqt,
     ...                                                  ref=np.max),
     ...                          x_axis='time')
     >>> plt.title('CQT power, shape={}'.format(cqt.shape))
     >>> plt.subplot(3, 1, 2)
-    >>> librosa.display.specshow(librosa.amplitude_to_db(cqt_med,
+    >>> librosa.display.specshow(librosa.power_to_db(cqt_med,
     ...                                                  ref=np.max),
     ...                          x_coords=beat_t, x_axis='time')
     >>> plt.title('Beat synchronous CQT power, '
     ...           'shape={}'.format(cqt_med.shape))
     >>> plt.subplot(3, 1, 3)
-    >>> librosa.display.specshow(librosa.amplitude_to_db(cqt_med_sub,
+    >>> librosa.display.specshow(librosa.power_to_db(cqt_med_sub,
     ...                                                  ref=np.max),
     ...                          x_coords=subbeat_t, x_axis='time')
     >>> plt.title('Sub-beat synchronous CQT power, '