Hi everyone!

I've been making youtube videos about electronic music for the last couple months, but this is my first tutorial. Let me know what you think and if there are other topics you'd like me to cover!

https://youtu.be/C3a55XjLptM

Hope you enjoy

My fellow music programmers. Recently I found myself interested in physical modelling synthesis and noticed that there aren't that many software synths around that do that, especially on Linux.

I'm a software dev by trade and I've done some basic DSP at university (physics degree), but I'm basically a noob at audio programming. Some cursory googling yielded the odd paper or book chapter in a general DSP course, but nothing that seemed to go into very much depth or breadth regarding PM. So maybe you can help me find a learning path.

I'm looking for something that covers both the theory of PM synthesis and ideally as many practical examples as possible. Math heavy is fine and doesn't need to be focused on programming per se, though I wouldn't mind it. I'm not married to any particular programming language. (Though I'm kinda interested in Faust, as it seems it lets me create something that makes sound fairly quickly without worrying about the nitty gritty of I/O and the like.)

Is there any focused resource along those lines or will I have to go the path of a general DSP course and then find scraps of physical modelling advice here and there?

I love learning new and less common synthesis types, but for some reason I've never gotten fired up by physical modelling synthesis. It gets 1% the airtime of, say, subtractive or FM synthesis, and seems to me to be all about "realistically" producing real-life sounds rather than being experimental and pushing into novel sonic territory.

So, with that said, if anyone out there is a big advocate of PM, what is so great about it?

(BTW this is not me trashing PM as I've never used a PM synth. Really, I'm just looking for reasons to bother getting into it.)

Admittedly I have almost no experience with it, having only ever used two physical modelling synths: Collision (Ableton) and Laplace (iOS). But one of the reasons I can't get motivated to try anything else is that everything I create is so lackluster and similar sounding. No matter how I shape and balance the noise and click, or how I set the resonator, it just ends up sounding super meh.

So this post is just a nebulous request for physical modelling tips and tricks. Does anyone have any for creative compelling patches? Alternatively, what techniques really helped you up your physical modelling game?

I was wondering if anyone knew of some interesting and weird physical-modelling resources outside of the usual strings, membranes, tubes, formants, etc. I've been enamored with the xoxos plugins like Oscine Tract, Synesect, Fauna as well as all of AAS's work (Corpus, Chromaphone, etc), the resonance section of Madrona Labs' Kaivo, etc. Have you seen any interesting papers, programs, or plugins that have went in an unusual direction with this idea?

hey r/sounddesign community,

I am starting to work in Max MSP to create more immersive sound design for games or videos.

Can someone recommend any good literature for physical modeling synthesis?

I haven't found anything useful yet or nearly as advanced as I want to. I only find a few youtube videos on the topic.

Thanks in advance!

Hi everyone. I recently got into music production as a hobby, and have really been interested by sound design.

In the past couple of days, I spent a lot of time doing preliminary research on sound design and physical modeling synthesis: patching tools (like Pure Data) and language-environments (like SuperCollider), Karplus Strong, Andy Farnell, etc (and even slip-stick friction!).

My question is: what level of math proficiency would I need to be able to work in, let's say, something like SuperCollider to physically model an instrument? According to Andy Farnell (page 3 in his book excerpt), trigonometry seems to be a requirement. What about calculus?

As a graphic design grad, the farthest I've gone in math is 12th grade calculus, which I've done pretty well in. I know it will take me a while to get to where I want in sound design, but because it's a hobby, I've got time :)

this string quartet recreates the sound of water using principles from physical modeling synthesis. it loops and in each passing cycle it sounds more like water, while melodies emerge.

liquid liquid

Hey everyone. As I'm fairly new to sound design and physical modeling, I'll have huge gaps in knowledge, so please bear with me. I've been reading about modal synthesis, or the spectral modeling of an instrument by replicating its modes of vibration.

Are there any acoustic/audio recording databases that can be used for reference when making a modal model of, for example, a grand piano? If not, what are the ethics of using sample libraries (either free or paid) for reference only, for spectral analysis/reconstruction for an open source physical model?

Edit: For anyone who's interested, I found a great open source CC0 (Creative Commons) sample library, that includes several instruments, grand pianos as well (like Steinway B)! It's called Versilian Community Sample Library, or VCSL. I think it's a perfect resource for studying sound and recreating with physical models. Here's the link: https://vis.versilstudios.com/vcsl.html

VCSL is also on GitHub: https://github.com/sgossner/VCSL

Huge NNs have revolutionized speech synthesis, but AFAICT the understanding of speech has not improved much as a result. In the 90's and 00's there were some papers on learning physical models of the vocal tract from audio recordings. It is plausible that such models could be improved greatly with today's computing power, ML pipelines and larger/better datasets. The benefits of such an approach are many:

• disentangle the effects of vocal folds, lip and tounge positions, and motor control
• TTS models could be greatly reduced in size (because motor control is low dimensional)
• better automated tools for speech pathologists
• overall better understanding of speech production

There are a few papers using this approach to produce single vowels, but I haven't found any full TTS systems with publicly available example outputs.

FULL 63 PAGE PDF - The Lancet Commission on Diabetes: using data to transform diabetes care and patient lives

Dr David Ludwig on Twitter https://twitter.com/davidludwigmd/status/1328346729284964352

https://preview.redd.it/jw7w278s7mz51.png?width=355&format=png&auto=webp&s=852f3aee1767e1be681a7e348b08b7abed5442c4

https://preview.redd.it/b8vhyw3r7mz51.png?width=450&format=png&auto=webp&s=67b35e63373b627578a49e25e0fe42fc37ce8fa9

https://www.sciencedirect.com/science/article/abs/pii/S0140673620323746?via%3Dihub

The Lancet CommissionsThe Lancet Commission on diabetes: using data to transform diabetes care and patient lives

Executive summary

2020 will go down in history as the year when the global community was awakened to the fragility of human health and the interdependence of the ecosystem, economy, and humanity. Amid the COVID-19 pandemic, the vulnerability of people with diabetes during a public health emergency became evident by their at least 2 times increased risk of severe disease or death, especially in individuals with poorly controlled diabetes, comorbidities, or both. The disease burden caused by COVID-19, exacerbated by chronic conditions like diabetes, has inflicted a heavy toll on health-care systems and the global economy.

In this Lancet Commission on diabetes, which embodies 4 years of extensive work on data curation, synthesis, and modelling, we urge policy makers, payers, and planners to collectively change the ecosystem, build capacity, and improve the clinical practice environment. Such actions will enable practitioners to systematically collect data during routine practice and to use these data effectively to diagnose early, stratify risks, define needs, improve care, evaluate solutions, and drive changes at patient, system, and policy levels to prevent and control diabetes and other non-communicable diseases. Emerging evidence regarding the possible damaging effects of severe acute respiratory syndrome coronavirus 2 on pancreatic islets implies the potential worsening of the COVID-19 pandemic and the diabetes epidemic, adding to the urgency of these collective actions.

Prevention, early detection, prompt diagnosis,

The AES San Francisco Section is ses to cover the history of physical modeling with lots of sound examples and discuss why it is a big deal again in this free, online meeting.

https://aessf.org/event/a-history-of-the-technology-of-physical-modeling-synthesis-for-musical-instruments/

Royal Society of Chemistry, Cambridge 2020. 363 pp., hardcover, € 199.00.—ISBN 978‐1‐78801‐370‐3

https://ift.tt/39Se6it

Let's talk about physical modeling synthesis!

From using short delays to get tuned feedback (Karplus Strong) to physical modeling effects (reverbs, amp simulations, things like Corpus in Ableton Live) to the ACB of current Roland gear (that supposedly model the circuits) to drum synthesis (Machinedrum, Xoxoxs' VSTs, etc) to full blown physical modeling synths (wikipedia link, I'm feeling lazy here).

I have been dodging this one long enough, it is finally time to make a paper summary for Guided Diffusion!

GANs have dominated the conversation around image generation for the past couple of years. Now though, a new king might have arrived - diffusion models. Using several tactical upgrades the team at OpenAI managed to create a guided diffusion model that outperforms state-of-the-art GANs on unstructured datasets such as ImageNet at up to 512x512 resolution. Among these improvements is the ability to explicitly control the tradeoff between diversity and fidelity of generated samples with gradients from a pretrained classifier. This ability to guide the diffusion process with an auxiliary model is also why diffusion models have skyrocketed in popularity in the generative art community, particularly for CLIP-guided diffusion.

Does this sound too good to be true? You are not wrong, there are some caveats to this approach, which is why it is vital to grasp the intuition for how it works!

Full summary: https://t.me/casual_gan/228

Blog post: https://www.casualganpapers.com/guided_diffusion_langevin_dynamics_classifier_guidance/Guided-Diffusion-explained.html

Guided Diffusion - SOTA generative art model for CLIP

arxiv / code

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Hi everyone. I recently got into music production as a hobby, and have really been interested by sound design.

In the past couple of days, I spent a lot of time doing preliminary research on sound design and physical modeling synthesis: patching tools (like Pure Data) and language-environments (like SuperCollider), Karplus Strong, Andy Farnell, etc (and even slip-stick friction!).

My question is: what level of math proficiency would I need to be able to work in, let's say, something like SuperCollider to physically model an instrument? According to Andy Farnell (page 3 in his book excerpt), trigonometry seems to be a requirement. What about calculus?

As a graphic design grad, the farthest I've gone in math is 12th grade calculus, which I've done pretty well in. I know it will take me a while to get to where I want in sound design, but because it's a hobby, I've got time :)

Hey everyone. As I'm fairly new to sound design and physical modeling, I'll have huge gaps in knowledge, so please bear with me. I've been reading about modal synthesis, or the spectral modeling of an instrument by replicating its modes of vibration.

Are there any acoustic/audio recording databases that can be used for reference when making a modal model of, for example, a grand piano? If not, what are the ethics of using sample libraries (either free or paid) for reference only, for spectral analysis/reconstruction for an open source physical model?

Edit: For anyone who's interested, I found a great open source CC0 (Creative Commons) sample library, that includes several instruments, grand pianos as well (like Steinway B)! It's called Versilian Community Sample Library, or VCSL. I think it's a perfect resource for studying sound and recreating with physical models. Here's the link: https://vis.versilstudios.com/vcsl.html

VCSL is also on GitHub: https://github.com/sgossner/VCSL

Royal Society of Chemistry, Cambridge 2020. 363 pp., hardcover, € 199.00.—ISBN 978‐1‐78801‐370‐3

https://ift.tt/39Se6it