Update tutorial notebook: fix stale API, add new audio methods

- thresh= -> threshold= in motiongrams examples
- cols= -> columns= in grid examples (matches renamed parameter)
- Add MFCC, tempo/beat-tracking, beat statistics, and chromagram
  sections to the audio analysis chapter
- Refresh the audio methods overview list

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: alexarje

musicalgestures/MusicalGesturesToolbox.ipynb

@@ -1968,7 +1968,7 @@
     }
    ],
    "source": [
-    "video_grid = video.grid(height=300, rows=1, cols=9)\n",
+    "video_grid = video.grid(height=300, rows=1, columns=9)\n",
     "video_grid.show(mode='notebook')"
    ]
   },
@@ -2023,7 +2023,7 @@
     }
    ],
    "source": [
-    "video_grid = video.grid(height=300, rows=3, cols=3)\n",
+    "video_grid = video.grid(height=300, rows=3, columns=3)\n",
     "video_grid.show(mode='notebook')"
    ]
   },
@@ -2344,7 +2344,7 @@
     }
    ],
    "source": [
-    "video_grid = history.grid(height=300, rows=3, cols=3)\n",
+    "video_grid = history.grid(height=300, rows=3, columns=3)\n",
     "video_grid.show(mode='notebook')"
    ]
   },
@@ -3265,7 +3265,7 @@
     }
    ],
    "source": [
-    "video.motiongrams(thresh=0.0)[0].show(mode='notebook')"
+    "video.motiongrams(threshold=0.0)[0].show(mode='notebook')"
    ]
   },
   {
@@ -3328,7 +3328,7 @@
     }
    ],
    "source": [
-    "video.motiongrams(thresh=0.1)[0].show(mode='notebook')"
+    "video.motiongrams(threshold=0.1)[0].show(mode='notebook')"
    ]
   },
   {
@@ -3391,7 +3391,7 @@
     }
    ],
    "source": [
-    "video.motiongrams(thresh=0.9)[0].show(mode='notebook')"
+    "video.motiongrams(threshold=0.9)[0].show(mode='notebook')"
    ]
   },
   {
@@ -4297,8 +4297,14 @@
    "source": [
     "- `waveform()`: Renders a figure showing the waveform of the video/audio file.\n",
     "- `spectrogram()`: Renders a figure showing the mel-scaled spectrogram of the video/audio file.\n",
-    "- `descriptors()`: Renders a figure of plots showing spectral/loudness descriptors, including RMS energy, spectral flatness, centroid, bandwidth, rolloff of the video/audio file.\n",
-    "- `tempogram()`: Renders a figure with a plots of onset strength and tempogram of the video/audio file."
+    "- `tempogram()`: Renders a figure with plots of onset strength and tempogram of the video/audio file.\n",
+    "- `descriptors()`: Renders a figure of spectral/loudness descriptors (RMS energy, flatness, centroid, bandwidth, rolloff).\n",
+    "- `mfcc()`: Renders a figure of the Mel-frequency cepstral coefficients (timbre features).\n",
+    "- `tempo()`: Estimates tempo and beat positions, returning beat times, inter-beat intervals and beat regularity.\n",
+    "- `beat_statistics()`: Renders circular statistics of beat-timing consistency.\n",
+    "- `chromagram()`: Renders a figure mapping audio energy onto the 12 pitch classes over time.\n",
+    "- `hpss()`: Separates and plots the harmonic and percussive components of the audio.\n",
+    "- `ssm()`: Computes a self-similarity matrix on audio (or video) features.\n"
    ]
   },
   {
@@ -4580,6 +4586,86 @@
     "audio.descriptors()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### MFCCs\n",
+    "\n",
+    "[Mel-frequency cepstral coefficients](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum) (MFCCs) compactly describe the spectral envelope (timbre) of a sound over time and are widely used as features for audio classification and similarity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "audio = mg.MgAudio(pianist)\n",
+    "audio.mfcc()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tempo and beat tracking\n",
+    "\n",
+    "`tempo()` estimates the tempo and beat positions and renders the waveform with beat markers. Numeric results are available in the returned figure's `.data` dictionary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "audio = mg.MgAudio(pianist)\n",
+    "beats = audio.tempo()\n",
+    "\n",
+    "print(f\"Tempo: {beats.data['tempo']:.1f} BPM\")\n",
+    "print(f\"Number of beats: {len(beats.data['beat_times'])}\")\n",
+    "print(f\"Beat regularity: {beats.data['beat_regularity']:.1%}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Beat statistics\n",
+    "\n",
+    "`beat_statistics()` fits an ideal isochronous grid to the detected beats and visualises how each beat deviates from it — a polar phase histogram plus a millisecond-deviation time series. This reveals whether a performer rushes, drags, or keeps steady time (requires at least four beats)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "audio = mg.MgAudio(pianist)\n",
+    "audio.beat_statistics()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Chromagrams\n",
+    "\n",
+    "A [chromagram](https://en.wikipedia.org/wiki/Chroma_feature) maps audio energy onto the 12 pitch classes (C, C#, D, …, B) over time, which is useful for analysing harmony and chord progressions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "audio = mg.MgAudio(pianist)\n",
+    "audio.chromagram()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -5543,4 +5629,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}