Let ChatGPT help you program sound decoders

[signalmaintainer]

I've been configuring the sound decoders for my two Southern SD-9s this week. One is equipped with a LokSound V5 Nano; the other has a Zimo MS540E24.

But rather than rely on lots of tedious trial and error, I've told ChatGPT what I want -- silky smooth low-speed performance -- and it provides all the information, including a speed table, which CVs to adjust (some of which I had no idea of what they did, such as CVs 402/403 in the Zimo), and suggestions for tweaking the locos' performance, either with minor adjustments to the speed table or BEMF values.

The results are beyond impressive for me. I had been quite content for years with how these locomotives had performed using Digitrax DN163A0 silent decoders. Now, they perform better and sound like the real things.

It gets better. I told ChatGPT the decoders had EMD 567B sound projects loaded into them, and that the decoders were installed in the newer Atlas SD-9 chassis. It tailored the programming for the locomotive and sound. Why does that matter? Because ChatGPT also said my Atlas GP38s with LokSound Nanos running an EMD 645E sound file will need a slightly different configuration for peak perfornance, both to sound and run as a GP38 would. That's impressive, too!

I'd encourage you to use ChatGPT if you're frustrated with or overwhelmed with decoder programming. Tell it specifically what you have and what you want -- that's key; the more information it has, the better -- then let it do the heavy lifting for you. I'd wager it could help immensely with speed matching, too.

[Chris333]

Supposidly programing is the one thing AI is good at.

[nickelplate759]

Supposidly programing is the one thing AI is good at.

Especially if you don't particularly value robustness or security...

[cgw]

curious,   can you post your chat thread with chat gtp?   

[signalmaintainer]

curious,   can you post your chat thread with chat gtp?

It's far too long, only because I kept querying it about tweaking a small group specific speed table CVs. I made my own adjustments to sound CVs, such as the bell. Here's the summary:


The ESU LokSound 5 Nano decoder in an N scale Atlas SD-9 can deliver excellent smooth low-speed performance with proper configuration, leveraging its advanced Back-EMF load compensation and the 28-step speed table. The key is to enable the speed table, run the auto-calibration for Back-EMF, and use a custom exponential speed curve that emphasizes gradual increases at low steps for fine control and realistic diesel ramp-up. This prevents stalling or jerking in N scale's lightweight mechanisms. For the Atlas SD-9, which models an EMD locomotive with a 567 16-cylinder non-turbo diesel engine, integrating sound tuning enhances realism by syncing prime mover notching, horn, bell, and other effects with the low-speed operation. Atlas Gold series locos often come pre-loaded with ESU's EMD 567 sound project (e.g., project 58925 or similar for Nano), but you can adjust via CVs or load custom files using the ESU LokProgrammer.

### Step 1: Basic Setup and Enabling the Speed Table
- Set your DCC system to 28 speed step mode for programming (you can switch back to 128 steps for operation afterward).
- Enable the 28-step speed table by setting bit 4 in CV29 to 1 (add 16 to the current value).
  - For a long (4-digit) address: CV29 = 62 (typical for DCC with RailCom and 128 steps enabled).
  - For a short (2-digit) address: CV29 = 30.
- Set acceleration (CV3) and deceleration (CV4) to low values initially for testing (e.g., CV3=10, CV4=8), then increase for realistic momentum (defaults are CV3=28, CV4=21).
- Disable momentum temporarily (CV3=1, CV4=1) while setting the speed table to make adjustments easier.

### Step 2: Back-EMF Auto-Calibration for Smooth Motor Control
The LokSound 5 Nano's 6th-generation Back-EMF is crucial for creep speeds in N scale. Run the auto-tune procedure first:
- Place the locomotive on a straight, level track (no load or cars attached).
- Set CV54 = 0.
- Activate F1 (the loco will briefly run at high speed forward, measure the motor, and stop automatically).
- This tunes CV51 (K slow cutoff), CV52 (K slow gain), CV53 (reference voltage), CV54 (K proportional), and CV55 (I integral) to your specific motor/gearbox.
- Defaults before tuning: CV54=50, CV55=100, CV53=130, CV56=255 (full Back-EMF influence at speed step 1).
- Post-tuning adjustments for N scale smooth low-speed:
  - If jerky at speed step 1: Reduce CV54 by 5-10 increments (e.g., from 50 to 40) until smooth.
  - If lurching on stop: Reduce CV55 by 5-10 (e.g., from 100 to 80-90).
  - Ensure CV56=255 for maximum low-speed Back-EMF influence.
  - For finer low-speed sampling: Set CV116=50-100 (slow speed period, default 50), CV118=10-20 (slow speed gap, default 15).
  - Confirm load control is enabled: CV49 bit 0 = 1.
- Test at speed step 1; the loco should crawl smoothly without buzzing or stalling. Remove any motor capacitors if present, as they interfere with Back-EMF.

### Step 3: 28-Step Speed Table (CV67 to CV94)
For the LokSound 5 Nano DCC, the table follows NMRA standards: CV67 (step 1) to CV94 (step 28) set the motor PWM values (0-255) directly. An exponential curve (gamma ~1.3) works best for diesels like the SD-9, providing slow starts with gradual acceleration for smooth low-speed crawling and realistic prototyping.

Suggested values (starts at ~1-2 smph at step 1, ramps to ~60-70 smph at step 28; adjust based on testing—reduce early values if too fast, or CV94 if max speed exceeds prototype ~65 mph):
- CV67: 2
- CV68: 3
- CV69: 4
- CV70: 5
- CV71: 7
- CV72: 9
- CV73: 11
- CV74: 13
- CV75: 16
- CV76: 19
- CV77: 22
- CV78: 25
- CV79: 29
- CV80: 33
- CV81: 37
- CV82: 42
- CV83: 47
- CV84: 52
- CV85: 58
- CV86: 64
- CV87: 71
- CV88: 78
- CV89: 85
- CV90: 93
- CV91: 101
- CV92: 110
- CV93: 120
- CV94: 255

### Step 4: Sound Tuning for Realistic Diesel Performance
The LokSound 5 Nano supports full-sound projects for the EMD 567 engine in the SD-9, with automatic notching tied to speed steps and load for seamless integration with low-speed crawling. Use indexed CV programming (set CV31=16 first for extended CVs) to adjust volumes and behaviors. Master volume is CV63=128 (default; range 0-192—try 100-140 for N scale to avoid distortion in small speakers). For individual slots (assuming standard EMD diesel mapping: Slot 1=Prime Mover, Slot 2=Horn, Slot 3=Bell):

- **Prime Mover (Drive Sound) Tuning for Low-Speed Sync:**
  - Volume: Set CV32=1, then CV257=100-128 (default 128; lower for subtler idling during creeps).
  - Minimum drive sound: CV123=1 (ensures faint idle at speed step 0-1).
  - Notch transitions: Automatic based on speed; for smoother low-speed ramp-up, adjust notch-up thresholds if needed (e.g., via LokProgrammer for sound flow edits). Default notching starts low and ramps realistically with the exponential speed table.
  - Auto-notching enable: CV49 bit 2=1 (default on).
  - Idle timeout: CV11=0 (no timeout) for continuous low-speed sound.

- **Horn Tuning:**
  - Volume: Set CV32=1, then CV265=80-110 (default ~128; adjust for balance with prime mover).
  - Selection: For SD-9 realism, use Nathan K3L or similar (if not default, change via CV163 or full project reload—e.g., CV163=1 for K3L; consult ESU sound file details).

- **Bell Tuning:**
  - Volume: Set CV32=1, then CV273=60-90 (default ~128; softer for N scale).
  - Rate: CV182=128 (default; slower for older diesels like SD-9).

- **Other Sounds:**
  - Brake squeal: Volume via CV32=1, CV281=50-70 (slot 4 typically).
  - Compressor/Air: Slot 5-6; adjust similarly if overpowering at low speeds.
  - Function mapping: Ensure F8=mute (default), F1=bell, F2=horn for standard ops. Remap via CVs 246-416 if needed.

For best results, use the ESU LokProgrammer to load or tweak the full EMD 567 non-turbo sound file (available on ESU's site) and visualize notch points aligning with your speed table's low steps.

### Step 5: Final Testing and Tweaks
- Program in 28-step mode, then test in your preferred mode (128 steps interpolates smoothly).
- Adjust momentum: Increase CV3 (acceleration) to 40-60 and CV4 (deceleration) to 30-50 for diesel realism, ensuring sound notches match acceleration.
- If max speed is too high: Lower CV94 (e.g., to 200).
- For gearbox backlash (common in N scale): Set CV111=10-20 to add a brief high-voltage pulse after direction changes.
- PWM frequency (CV9): Default ~40 kHz is fine for the Atlas SD-9's motor; increase to 50 if buzzing occurs.
- Sound-motor sync test: At speed step 1, prime mover should idle softly with smooth crawl; notch up gradually as speed increases without abrupt jumps.

This setup should give buttery-smooth crawling at speed step 1 (~1 smph) without hesitation, with immersive EMD 567 sounds ramping realistically, ideal for switching on an N scale layout.

[kiwi_bnsf]

Firstly I should state that if ChatGPT (or any other AI) encourages people to experiment further with more complicated DCC decoder settings, then that is overall a good thing — especially if it results in improved performance.

Also, I'm not anti-AI, and I use it daily in my work and for model railroading, but currently it has some major pitfalls.

The key thing to be aware of with the current state of LLM-based AI tools, is they are all essentially engage with what I would call "teenage behaviour". They are absolutely brimming with knowledge based on the enormous volume of text that they have been trained on and their access to web APIs. However, they have no skill or judgement when it comes to offering up this information in response to a prompt. So they will confidently tell you information that is completely incorrect for a given context.

Obviously you can get more-and-more specific in refining your prompts to help with context, but ultimately all the current LLMs including ChatGPT and Google Gemini will routinely spout information that is complete bullshit, but is written in a syntactically advanced way that makes it seem correct.

This is dangerous, because it is very difficult for a novice user of any tool (software coding, DCC programming, health care diagnoses, cooking or otherwise) to spot when the AI is talking BS.

Why does this matter? Well with model trains and DCC specifically, probably the worst you could do would be to fry a decoder or motor, so it's probably not a big deal.

I would just encourage you to take any recommendations and do a quick search on TheRailwire or a more general Google to see if the info stacks up. Even Google Gemini hallucinates completely inaccurate info that is contradicted by briefly researched results from a Google search and better sources.

Reading through the ChatGPT summary above, it is overall quite useful. But I can't help but spot some info that I would consider dubious at best:

- advice to remove capacitors is inaccurate for pretty much any N scale loco in the last 20 years (most stock PC boards have inductors protecting the motor, and these should not be messed with).

- there is no mention of the standard ESU notch speed CVs that can be edited without a LokProgrammer or any need for "editing the sound flow" or "visualising notch points" — both of which are hallucinated statements

- an SD9 would likely not use a modern K3L Canadian tuned horn unless it was a modernised Canadian loco

(and that's just from a quick glance — I haven't checked it all)


This probably all comes across as a bit negative or pedantic, but in summary I'm trying to say: don't believe anything that AI LLMs say is actually correct. First go and research this a bit more on the wider internet. In the case of BEMF tuning, there are some truly excellent guides out there that cover the K / I tuning for various decoders, and different motor types.

It's very helpful to get a broader understanding of what these do so that you can apply them to all manner of different DCC installs. If you have a specific decoder that you want to learn more about, join the relevant Groups.IO and you will find a massive amount of helpful info (and people).

If you haven't done so already, I would definitely spend some time playing with JMRI Decoder Pro to simplify loco programming via ops mode programming. You can also download the ESU LokProgrammer software for free, and then use it to make complex changes to many CVs that can then be exported to JMRI and then applied to a loco even without the LokProgrammer hardware. Both tools guide you through setting many CVs with a user interface with some contextual help.


Anyway, I'm glad you have found the joy that is excellent motor control and BEMF that is now available with modern decoders from ESU, Zimo, and others.

Cheers

[signalmaintainer]

Firstly I should state that if ChatGPT (or any other AI) encourages people to experiment further with more complicated DCC decoder settings, then that is overall a good thing — especially if it results in improved performance.

Also, I'm not anti-AI, and I use it daily in my work and for model railroading, but currently it has some major pitfalls.

The key thing to be aware of with the current state of LLM-based AI tools, is they are all essentially engage with what I would call "teenage behaviour". They are absolutely brimming with knowledge based on the enormous volume of text that they have been trained on and their access to web APIs. However, they have no skill or judgement when it comes to offering up this information in response to a prompt. So they will confidently tell you information that is completely incorrect for a given context.

Obviously you can get more-and-more specific in refining your prompts to help with context, but ultimately all the current LLMs including ChatGPT and Google Gemini will routinely spout information that is complete bullshit, but is written in a syntactically advanced way that makes it seem correct.

This is dangerous, because it is very difficult for a novice user of any tool (software coding, DCC programming, health care diagnoses, cooking or otherwise) to spot when the AI is talking BS.

Why does this matter? Well with model trains and DCC specifically, probably the worst you could do would be to fry a decoder or motor, so it's probably not a big deal.

I would just encourage you to take any recommendations and do a quick search on TheRailwire or a more general Google to see if the info stacks up. Even Google Gemini hallucinates completely inaccurate info that is contradicted by briefly researched results from a Google search and better sources.

Reading through the ChatGPT summary above, it is overall quite useful. But I can't help but spot some info that I would consider dubious at best:

- advice to remove capacitors is inaccurate for pretty much any N scale loco in the last 20 years (most stock PC boards have inductors protecting the motor, and these should not be messed with).

- there is no mention of the standard ESU notch speed CVs that can be edited without a LokProgrammer or any need for "editing the sound flow" or "visualising notch points" — both of which are hallucinated statements

- an SD9 would likely not use a modern K3L Canadian tuned horn unless it was a modernised Canadian loco

(and that's just from a quick glance — I haven't checked it all)


This probably all comes across as a bit negative or pedantic, but in summary I'm trying to say: don't believe anything that AI LLMs say is actually correct. First go and research this a bit more on the wider internet. In the case of BEMF tuning, there are some truly excellent guides out there that cover the K / I tuning for various decoders, and different motor types.

It's very helpful to get a broader understanding of what these do so that you can apply them to all manner of different DCC installs. If you have a specific decoder that you want to learn more about, join the relevant Groups.IO and you will find a massive amount of helpful info (and people).

If you haven't done so already, I would definitely spend some time playing with JMRI Decoder Pro to simplify loco programming via ops mode programming. You can also download the ESU LokProgrammer software for free, and then use it to make complex changes to many CVs that can then be exported to JMRI and then applied to a loco even without the LokProgrammer hardware. Both tools guide you through setting many CVs with a user interface with some contextual help.


Anyway, I'm glad you have found the joy that is excellent motor control and BEMF that is now available with modern decoders from ESU, Zimo, and others.

Cheers

Thanks. Please note:

1) I didn't opt for Nathan K3L horns. The Southern used M3s; got as close as I could.

2) I used DecoderPro to configure and test everything of what ChatGPT suggested. I also have a LokProgrammer to download sound projects, and sometimes I program with that, as well.

3) I specifically didn't ask ChatGPT for a setup using standard ESU notch speed CVs.

4) ChatGPT did a damn fine job of suggesting CVs to fine tune the BEMF. It had to have drawn the best information from somewhere. Sounds to me like it got it right. It did query me about the motor's performance and suggested adjustments as I fine-tuned the CVs, which I did not include here.

5) ChatGPT and other AI tools scour forums and IO chat groups so that I don't have to, sifting through the chaff.

6) At worse, dialing CV8 = 8 would have rid the decoder of any bogus ChatGPT settings, but ...

7) ...the locomotive runs silky smooth, with great low speed performance, reaching Run 4 at half-thottle (@ 23 smph). Just what I wanted. So does my Zimo-equipped SD-9, but ChatGPT did stumble over setting up Speedlock, and had me set one CV to a value that turned on the headlights when F1 was toggled for the bell. After a bit of head scratching, I found the CV (in the 300s or 400s) and dialed it back to zero -- problem solved.

Is it possible that despite AI's "teenage behavior" (an apt description), it rates as adequate to excellent for many, perhaps even most, model railroading applications? And will only improve as it is used?

[peteski]

- advice to remove capacitors is inaccurate for pretty much any N scale loco in the last 20 years (most stock PC boards have inductors protecting the motor, and these should not be messed with).

That is also not quite accurate.  Actually, older locos manufactured by American companies (unlike  European models which have to adhere to stricter laws of their equivalent of FCC) had no RFI suppressing components at all.  Straight connections from the track to the motor.  But likely when model manufacturing was outsourced to China most American prototype models were equipped with the RFI components at the motor leads. That circuit contains 2 inductors in series with motor leads, and a small value capacitor across (in parallel) with the motor leads.  It is basically a low-pass filter.  Majority of current models with built-in RFI filter do include that capacitor.

But while in the past the values of the components in the RFI filter often interfered with the DCC decoder's motor driver circuitry (the high frequency PWM voltage and BEMF sensing), removing the capacitor, and often the inductors, would remedy the problem.  But nowadays the RFI filter components have different values which do not interfere with the DCC motor driver. Even companies like Bachmann, BLI and ESU, to name a few, include them on the decoder or decoder adapter boards.    No capacitor/inductor removal should be necessary.