Fixing typos and updating nussl install info

Ethan Manilow · Ethan Manilow · commit fbe83abb2ead · 2021-01-07T10:28:50.000-06:00
diff --git a/book/basics/evaluation.ipynb b/book/basics/evaluation.ipynb
@@ -21,9 +21,9 @@
     "measures struggle because there are many aspects of human perception that are\n",
     "extremely difficult capture by computational means alone. However, compared to\n",
     "subjective measures, they are much faster and cheaper to obtain. On the other\n",
-    "hand, subjective measures are expensive, time-consuming, and subject to\n",
+    "hand, subjective measures are expensive, time-consuming, and subject to the\n",
     "variability of human raters, but they can be more reliable than objective\n",
-    "measures because humans are involved in t\n",
+    "measures because actual human listeners are involved in the evaluation process.\n",
     "\n",
     "\n",
     "Objective measures are, by far, much more popular than subjective measures, but\n",
@@ -256,7 +256,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,
diff --git a/book/basics/representations.md b/book/basics/representations.md
@@ -206,7 +206,7 @@ Six commonly used window types for calculating an STFT.
 
 The window type determines the shape of the short-time window that will segment
 the audio into short segments before applying the DFT. The shape of this window
-can will affect which frequencies get emphasized or attenuated in the DFT. There
+will affect which frequencies get emphasized or attenuated in the DFT. There
 are many types of window functions, in {numref}`window_types`, we show the most
 common ones when calculating an STFT for source separation. For more information
 on other types of windows and their frequency response, please see `scipy.signal`'s
@@ -246,7 +246,7 @@ the frequency resolution and vice versa, as is visible in {numref}`window_lens`.
 alt: The hope length determines the distance (in samples) between adjacent short-time windows.
 name: hop_lens
 ---
-The hope length determines the distance (in samples) between adjacent short-time
+The hop length determines the distance (in samples) between adjacent short-time
 windows. An STFT is computed twice on the same signal; the smaller the hop length
 the more times a particular segment of the audio signal is represented in the
 STFT.
diff --git a/book/first_steps/byo_hpss.ipynb b/book/first_steps/byo_hpss.ipynb
@@ -332,7 +332,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,
diff --git a/book/first_steps/nussl_intro.ipynb b/book/first_steps/nussl_intro.ipynb
@@ -42,11 +42,23 @@
     "\n",
     "\n",
     "\n",
+    "## Installing nussl\n",
+    "\n",
+    "You can install nussl through pip like so:\n",
+    "\n",
+    "- `pip install nussl`\n",
+    "\n",
+    "\n",
+    "In this tutorial, nussl is installed through the `common` utility library. This library was specifically created for the tutorial and will provide functions for us to visualize and interact with audio. It will also install `nussl` and `scaper` into the notebook environments for use on these pages.\n",
+    "\n",
+    "\n",
     "\n",
     "## nussl Resources\n",
     "\n",
-    "- [nussl's Github repository is located here.](https://github.com/nussl/nussl)\n",
-    "- [Documentation for nussl is located here.](https://nussl.github.io/docs/)\n",
+    "This tutorial will guide you through using nussl, but for further details see the following links:\n",
+    "\n",
+    "- [nussl's Github repository](https://github.com/nussl/nussl)\n",
+    "- [nussl's Documentation pages.](https://nussl.github.io/docs/)\n",
     "\n",
     "\n",
     "## nussl Basics\n",
@@ -730,7 +742,7 @@
     "\n",
     "Let’s do something a little more interesting with our `AudioSignal` object. Since `signal1.stft_data` is just a regular numpy array, we can access and manipulate it as such. So let’s implement a low pass filter by creating a new `AudioSignal` object and leaving `signal1` unaltered.\n",
     "\n",
-    "Let’s eliminate all frequencies above about 400 Hz in our signal."
+    "Let’s eliminate all frequencies above about 1000 Hz in our signal."
    ]
   },
   {
@@ -1010,7 +1022,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,
diff --git a/book/first_steps/repetition.ipynb b/book/first_steps/repetition.ipynb
@@ -1076,7 +1076,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,

Original file line number	Diff line number	Diff line change
`@@ -332,7 +332,7 @@`
`332`	`332`	`"name": "python",`
`333`	`333`	`"nbconvert_exporter": "python",`
`334`	`334`	`"pygments_lexer": "ipython3",`
`335`		`- "version": "3.8.5"`
	`335`	`+ "version": "3.7.6"`
`336`	`336`	`}`
`337`	`337`	`},`
`338`	`338`	`"nbformat": 4,`
Original file line number	Diff line number	Diff line change
`@@ -1076,7 +1076,7 @@`
`1076`	`1076`	`"name": "python",`
`1077`	`1077`	`"nbconvert_exporter": "python",`
`1078`	`1078`	`"pygments_lexer": "ipython3",`
`1079`		`- "version": "3.8.5"`
	`1079`	`+ "version": "3.7.6"`
`1080`	`1080`	`}`
`1081`	`1081`	`},`
`1082`	`1082`	`"nbformat": 4,`