fix internal links, add sound output to example in MFCC

Author: tombackstrom

Modelling/Non-negative_Matrix_and_Tensor_Factorization.md

@@ -36,7 +36,7 @@ The idea is that $H$ is a fixed matrix corresponding to our model of the
 signal, viz. the source model. It describes typical types features of
 the data. With the weights $W$, we interpolate between the columns
 of $H$. In some sense, this is then a generalization of a codebook (see
-[vector quantization](Vector_quantization_VQ.md)), but such that we
+[vector quantization](content:vq)), but such that we
 interpolate between codevectors. In addition, we require that all
 elements of $W$ and $H$ are non-negative, such that we ensure that $V$
 is also non-negative.

Modelling/Vector_Quantization.ipynb

@@ -3,8 +3,15 @@
   {
    "cell_type": "markdown",
    "id": "db207325-e3dc-4afc-bea7-25bc76dac2b5",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
+    "(content:vq)=\n",
     "# Vector quantization (VQ)\n",
     "\n",
     "\n",

Representations/Melcepstrum.ipynb

@@ -10,7 +10,7 @@ For a comprehensive review of speech coding, see {cite}`oshaughnessy2023coding`.
     2.  [Entropy coding](Transmission/Entropy_coding.md)
     3.  [Perceptual modelling in speech and audio
         coding](Transmission/Perceptual_modelling_in_speech_and_audio_coding.md)
-    4.  [Vector quantization (VQ)](Modelling/Vector_quantization_VQ.md)
+    4.  [Vector quantization (VQ)](content:vq)
     5.  [Linear prediction](Representations/Linear_prediction.ipynb)
 4.  [Code-excited linear prediction
     (CELP)](Transmission/Code-excited_linear_prediction_CELP.md)