Vocal Rider & Automation Writer in Bitwig (Patreon)

To create a vocal rider plugin in Bitwig using the envelope follower for recording modulator to automation lane, follow these steps:

1. Prepare Vocals: Use the raw vocal recording without sidechain processing.
2. Utilize a Separate Track: Bitwig does not allow recording automation data from modulators or within the grid on the same track, so use a second track.
3. Set Up FX Grid:Ensure FX grid is set to mono.
   Add an envelope follower, adjusting settings based on how closely you want to follow the signal (consider switching to RMS).
   Output the envelope follower data as CC data (e.g., CC 30) to a MIDI channel.
   
4. Use Tool Device and CC Modulator:After the FX grid, add a tool device followed by a CC or MIDI modulator using the same controller number to modulate volume.
   
5. Record and Edit Automation:Since direct recording to the same track's automation lane isn't possible, output the FX grid to a separate track and record it.
   After recording, copy the CC data from the second track and paste it into a new MIDI lane in the automation section of the original vocal track.
   This allows manual editing of the automation data for detailed control over the dynamics.
   

Advantages:

• Customization: This method provides flexibility to customize the signal processing and response of the vocal rider effect. You can add processing like signal multiplication, delay, smoothing, frequency filtering, or limiting to tailor the effect to your needs.
• Learning and Adaptation: Building from scratch in Bitwig offers a deeper understanding of how vocal riders work and allows for adaptation to specific needs, unlike fixed-plugin workflows.

Key Notes:

• Use the FX grid and tool devices creatively for signal processing before modulation.
• The necessity of using a second track for recording CC data due to Bitwig's internal node format and MIDI handling.
• After moving the CC data to the original track, you can fine-tune the automation for precise dynamic control, including timing adjustments and scaling.

This approach not only allows for detailed manual edits of automation data but also fosters learning and innovation by encouraging a hands-on, customizable approach to dynamic processing in Bitwig.