I'm curious about your thoughts.
When designing a speaker network, what do you think about using XSim or VituixCAD compared to using AI assistance?
XSim and VituixCAD are excellent for precise simulation, while AI can suggest design directions, ideas, and optimizations.
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Have a look at xmachina, automatic x-over software: https://www.diyaudio.com/community/threads/automatic-crossover-designing-with-xmachina.316283/
So you ask for help but if it's not easy you don't accept it?
I think I understand why you prefer AI!
Edit: sorry for being snarky ... xmachina can eventually be combined with AI and vituixcad.
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Where is AI getting the driver response data to generate the XO?
Perhaps a kit might be a good start for you.
Perhaps a kit might be a good start for you.
No expert on assessing tools, here, but for me it is eye-opening to ask the tool to do a task in a domain about which you are already knowledgeable.
Is that so? Even with XMachina-based designs, the user still needs to listen and fine-tune if necessary, right?
I'm planning to do the same — building the design based on AI suggestions, and then personally fine-tuning it by ear and through testing.
In the end, what’s really different?
Isn't refinement always part of the process, no matter what tools you start with?
Not sure if you can call that "fine tuning", though.
I'd rather call it doing it all over again, based on actual parameters (measurements).
But hey, it will be a great and long journey into speaker building!
Thank you for pointing that out you're right, it might not just be simple fine tuning in the end.
I'm fully aware that once real measurements are available, some major adjustments could be needed.
Still, I'm excited about the process and looking forward to learning as much as possible through this journey!
You have never used XMachina, and don't know how it works.
AI might be able to help you with the box design, but I would still prefer a stand alone tool that I trust for something like that.
"no matter what tools you start with?"
The first tool is, measuring the drivers on the baffle in the box.
^There is no AI substitute for that.
I am not trying to be mean here, the only way is, you measuring your drivers in your box. AI cannot do that.
AI may be better than nothing, especially if the user has not the slightest clue about how loudspeakers work.
I am not familiar with XSim, but VituixCAD is great to process and predict the electroacoustic outcome based on real-world measured data from the loudspeaker drivers. My personal gripe with VituixCAD is that the underlying physics and mathematics are sometimes not documented in full detail, so it can be hard to really understand what the output means (or what it does not mean). Back when I heavily relied on VituixCAD I sometimes had to write my own code to reverse engineer the output of Vituix.
The kind of results inferred here from AI might be better than nothing, but bluntly, it's still junk. Which I appreicate isn't what the OP wants to hear, but the tables above are 90% twaddle, which isn't exactly a good start.
AI models are extremely powerful tools -providing they're used in the right way. For a field as complex as loudspeaker design, that tends to mean what they're actually best adapted to is crunching numbers to very specific questions in order to free up a designer's time so they can concentrate on other areas. But that requires a fair amount [or a lot] of experience on the part of the user. Ironically enough, what they're least suited to is providing useful answers to very basic / general questions / requests from people new to the hobby, because by & large, the questions are so over-generalised the systems simply have no facilities for proper answers and what you end up with is an electronic form of fudged hand-waving.
For example, to the best of my knowledge, at the current state of play, none of the AI systems, even if they suggest something that on the face of it looks sound can actually design a decent speaker from scratch. They don't have measured FR, phase & impedance responses, and can't take generic ones from elsewhere and process them for a relatively accurate simulated value. I've yet to see one come up with an LF alignment that isn't either misleading, or based on some very basic formulas culled off the 'net with no account given for conditions. They can't design a proper filter either, because they have no [under these conditions] responses to work with, and rarely have the programming to work out how to manipulate the responses in a desired way -as a result, when asked basic questions, they typically churn out some variation on a stock electrical circuit assuming static impedances & with no account given to the actual frequency & phase relationships. If you ask it, it may come up with a bit more electronic hand-waving about 'needing to take careful measurements' [QED] or 'experiment' etc.
Which is a slightly abbreviated way of saying: yes, it's better than nothing, but if you really don't know anything about loudspeaker design, want to build something and don't want to spend a lot of time learning about the design processes themselves, the best solution is to build a high quality, proven existing design or kit that suits your requirements from a reputable company or designer. It will save a lot of grief, and give much better results.
AI models are extremely powerful tools -providing they're used in the right way. For a field as complex as loudspeaker design, that tends to mean what they're actually best adapted to is crunching numbers to very specific questions in order to free up a designer's time so they can concentrate on other areas. But that requires a fair amount [or a lot] of experience on the part of the user. Ironically enough, what they're least suited to is providing useful answers to very basic / general questions / requests from people new to the hobby, because by & large, the questions are so over-generalised the systems simply have no facilities for proper answers and what you end up with is an electronic form of fudged hand-waving.
For example, to the best of my knowledge, at the current state of play, none of the AI systems, even if they suggest something that on the face of it looks sound can actually design a decent speaker from scratch. They don't have measured FR, phase & impedance responses, and can't take generic ones from elsewhere and process them for a relatively accurate simulated value. I've yet to see one come up with an LF alignment that isn't either misleading, or based on some very basic formulas culled off the 'net with no account given for conditions. They can't design a proper filter either, because they have no [under these conditions] responses to work with, and rarely have the programming to work out how to manipulate the responses in a desired way -as a result, when asked basic questions, they typically churn out some variation on a stock electrical circuit assuming static impedances & with no account given to the actual frequency & phase relationships. If you ask it, it may come up with a bit more electronic hand-waving about 'needing to take careful measurements' [QED] or 'experiment' etc.
Which is a slightly abbreviated way of saying: yes, it's better than nothing, but if you really don't know anything about loudspeaker design, want to build something and don't want to spend a lot of time learning about the design processes themselves, the best solution is to build a high quality, proven existing design or kit that suits your requirements from a reputable company or designer. It will save a lot of grief, and give much better results.
Here is what AI is good at...This is how Microsoft Copilot summarizes Scott's post #13 above... 🤓
- Overall Assessment: The author argues that while AI-generated results in loudspeaker design might be marginally useful, they're largely unreliable and inadequate—essentially "better than nothing" but still mostly subpar.
- Strengths of AI: AI's real strength lies in performing specific, numerical calculations that can assist experienced designers by automating tedious tasks, allowing them to focus on more complex aspects of design.
- Weaknesses for Beginners: For newcomers, AI tends to offer overly generalized, vague responses ("electronic hand-waving"), lacking the specific data—like precise frequency response, phase, and impedance measurements—needed to design a quality speaker from scratch.
- Design Limitations: The critique underscores that AI systems often rely on basic formulas or generic circuits without accounting for dynamic operating conditions, leading to misleading or incomplete speaker designs, particularly in essential areas such as low-frequency alignment and filter design.
- Practical Advice: Ultimately, if you're new to loudspeaker design and looking for a reliable solution without investing significant time in learning the design process, it's recommended to opt for a tried-and-tested design or kit from a reputable source instead of relying solely on AI-generated designs.