Push audio mastering workflow

1 files changed, 117 insertions, 2 deletions

diff --git a/2022/organizers-notebook.md b/2022/organizers-notebook.md
index 3ff1dfe3..88c13656 100644
--- a/2022/organizers-notebook.md
+++ b/2022/organizers-notebook.md
@@ -2213,6 +2213,121 @@ before the conference!
 Sacha Chua
 
 
+### Mastering the prerec’s audio-track
+
+Mastering is the process of preparing an audio-track for a purpose.  For
+us, the purpose is quite simple: maximize the intelligibility of the
+speaker and minimize the noise.
+
+We can get great results with Audacity for the vast majority of
+audio-tracks.  Sometimes, however, some audio-tracks have intractable
+noise-profile that require the use of model-based denoising filters that
+can applied with ffmpeg.
+
+We’ll start with the average Audacity workflow, and we’ll move on to the
+model-based filters after.
+
+
+#### Audacity workflow
+
+When we process a prerec, we extract the audio of the original upload
+and add it to the backstage.  You should be able to find it under the
+name –original.$audio\_format.  If it’s not there, it’s easy to extract
+the audio from the original video, but we’d prefer if you warned
+core-organizers about it because it’s not normal.
+
+We’ve simplified the process down to these steps:
+
+1.  Open the audio file in Audacity.
+    
+    You might want to increase the size of the waveform by pulling on the
+    bottom of the bottom of the track.
+
+<audacity-demo-resize.webm>
+
+1.  Find a moment of quiet in the video, and select it.
+    
+    We ask our speakers to include 5 seconds of quiet at the beginning or
+    end of their prerecs, but even if they don’t, it’s relatively.
+
+2.  Effects → Noise Reduction → Get Noise Profile
+
+3.  Select → All
+
+4.  Effects → Noise Reduction → OK
+    
+    You can select a spoken portion of the track before applying the
+    effect and preview it to test your settings.  The default are usually
+    enough (Noise reduction (dB): 12, Sensitivity: 6.00, Frequency smoothing
+    (bands): 3).
+
+5.  Tools → Apply Macro → Alpha
+    
+    Before you can apply the Alpha macro, you need to save its content to
+    disk and import it via Tools → Macro Manager → Import.
+
+    Reverb:Delay="20" DryGain="5" HfDamping="99" Reverberance="15" RoomSize="70" StereoWidth="25" ToneHigh="0" ToneLow="100" WetGain="-13" WetOnly="0"
+    Amplify:Ratio="1"
+    FilterCurve:f0="79.621641" f1="101.02321" FilterLength="8191" InterpolateLin="0" InterpolationMethod="B-spline" v0="5.9148936" v1="0.042552948"
+    Normalize:ApplyGain="1" PeakLevel="-3" RemoveDcOffset="1" StereoIndependent="1"
+    Compressor:AttackTime="0.1" NoiseFloor="-50" Normalize="1" Ratio="2" ReleaseTime="1" Threshold="-30" UsePeak="0"
+
+1.  Save the file to disk with libopus (.opus format)
+    Use the following settings:
+
+> Bit Rate: 64 kbps
+> VBR Mode: On
+> Compression: 10
+> Application: Audio
+> Frame Duration: 20 ms
+> Cutoff: Disabled
+
+![img](audacity-export-settings.png)
+
+
+#### Model-based denoising filter
+
+If you can’t manage to get a good result with Audacity, chances are it’s
+because there’s too much noise in the video, even after profile-based
+denoising.  This usually happens when the noise-pattern of an
+audio-track evolves over the video, or if has an aperiodic quality.  For
+those, we’re going to need a bigger boat.
+
+Model-based denoising means using an AI-generated model to remove the
+audio frequencies that are usually associated to noise and preserve
+those that aren’t.  A different context (e.g. noisy room with statics,
+noisy room with people chatting, etc.) means a different model; for us,
+this means a model that minimizes background noise and maximizes clear
+voices (the speakers’).
+
+This is the model we’ve been using:
+Source: [rnnoise-models](https://github.com/GregorR/rnnoise-models), Model: [marathon-prescription](https://raw.githubusercontent.com/GregorR/rnnoise-models/master/marathon-prescription-2018-08-29/mp.rnnn)
+
+You should always apply the filter on the original’s audio, as opposed
+to an Audacity-processed audio.  This is to ensure that we have the most
+information about the signal, which means we can have gather the most
+information about the noise-profile.
+
+Following is the ffmpeg incantation to use to apply the filter-model.
+Make sure to modify the `DENOISER` variable and adapt input/output.
+
+    DENOISER="/path/to/audio-denoiser-model-mp.rnnn"
+    input="original.opus"
+    output="denoised.opus"
+    ffmpeg -i "$input" -af "$DENOISER" "$output"
+
+There’s no need to customize the libopus export information; the default
+is more than enough for human-speech.
+
+When you’re done with this step, you can then process the outputted
+audio-track with Audacity, skipping the denoising steps (1 to 5).
+
+
+#### Questions?
+
+If you’ve got any question on the process, you canget in touch with me (zaeph)!
+
+
 <a id="when-captioned"></a>
 
 ## When a talk is captioned
@@ -2717,7 +2832,7 @@ Probably focus on grabbing the audio first and seeing what&rsquo;s worth keeping
 
 Make a table of the form
 
-<table id="org4850a3d" border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">
+<table id="orgdacd95f" border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">
 
 
 <colgroup>
@@ -3685,7 +3800,7 @@ Where:
     Nice if there&rsquo;s an Ansible playbook
     
     sachac&rsquo;s notes:
-    <file:///home/sacha/code/docker/emacsconf-publish/>
+    <file:///home/zaeph/code/docker/emacsconf-publish/>
 -   probably good to set it up on front
 
 It&rsquo;s now on front.