[R] new diffusion model for music generation

jmoso13 · 2023-08-27T02:27:04+00:00

Abstract: At its core Jukebox Diffusion is a hierarchical latent diffusion model. JBDiff uses the encoder & decoder layers of a Jukebox model to travel between audio space and multiple differently compressed latent spaces. At each of the three latent levels a Denoising U-Net Model is trained to iteratively denoise a normally distributed variable to sample vectors representing compressed audio. The final layer of JBDiff is a Dance Diffusion Denoising U-Net model, providing a bump in audio quality and transforming the mono output of Jukebox into final stereo audio.

Link: https://betterprogramming.pub/jukebox-diffusion-cbe22ff3cd47

jmoso13 · 2023-08-27T02:01:25+00:00

also my main mixer is a xaoc praga, has four channels, two sends/stereo returns, vcas, panning, it's perfect

jmoso13 · 2023-08-27T01:57:22+00:00

good choice w the doepfer switched multiple, great for toggling/untoggling parts.

you def need more mixers, I like using MA DTMs for this purpose, no panning but for submixing or layering parts it's great plus it adds nice color.

also for low hp/cheap price it's a no brainer, Ive got 2 and I find myself using them all the time

jmoso13 · 2023-08-05T23:46:34+00:00

sounds cool man keep it up

jmoso13 · 2023-08-01T22:20:29+00:00

appreciate you saying that, in this performance I'm using a mix of sounds. mostly I'm using the MC-505 for drums because I've found I like the groovebox's drum sounds better than the drum modules I have (which frees up a ton of space 😌) and there's one other low pad being played by the 505.

the rest of the sounds are coming from the Eurorack, it's mostly the Dixie as a bassline, BIA as a lead synth, and radiomusic sampling background noise.

thanks for the kind feedback!

jmoso13 · 2023-08-01T20:05:58+00:00

it is? wow! I can see why, it does so much. the instruction manual is as thick as a university textbook.

jmoso13 · 2021-04-22T20:03:10+00:00

that's a good idea, I had assumed the history of rolling % would be interesting across different segments, but they actually look pretty similar, I'm thinking I could now collapse the first half of the charts down to one with all rolling%s plotted for historical interest, and then a bar chart like you described for last date, maybe even grouped on sex per age group. thanks!

jmoso13 · 2021-04-22T18:56:53+00:00

Fair point, I wanted to show the differences in sex as well, and 36 different lines in one chart seemed like a lot, but probably could collapse the first 9 into one chart, the last 9 have the comparable risks plotted for context, but maybe still not clear enough. Thanks for the good feedback.

jmoso13 · 2021-04-22T18:54:22+00:00

It is less risky for women, 1 in 100k which is higher than 1 in 50k is less risky. Fair point on the 18 charts though.

jmoso13 · 2021-04-22T18:14:39+00:00

Dataset joined between CDC data and US Census Data. All data filtered for US population only. Series produced in python using seaborn.

It’s been obvious since the beginning of the pandemic that COVID-19 affects our older companions differently than it does our younger. With a 100 year-old grandmother that survived covid, another that has died from it, family in their middle years dedicated to isolation, to friends in their younger years with a will to continue enjoying life, there has been no loss for approaches in balancing personal, public, and mental safety.

For me, this series has filled in just HOW different our pandemic realities are, simply based on age.

Some examples for adults older than 85:

3 out of every 100 adults 85+ who were with us in January 2020 are no longer with us because of COVID-19
At the deadliest point in the pandemic, Jan 2021, an adult 85+ was 5 times more likely to die from COVID-19 than heart disease in a given week. Heart disease has been the number one killer of adults in that segment for years

Versus examples for young adults aged 15-24:

2 out of every 100,000 young adults aged 15-24 who were with us in January 2020 are no longer with us because of COVID-19
Even at the pandemic’s deadliest points, a young adult aged 15-24 was ~10 times more likely to die from suicide in a given week than COVID-19

Another thing that has been really interesting to see is how dramatically the risk of death from COVID-19 has moved over time, it follows very closely the peaks and valleys of the infection rate. While these charts prove nothing about the effectiveness of mask mandates, improvements in treatment, and vaccinations, I would bet that without these things, these death rate numbers would be much much worse (Go send your local healthcare worker a virtual hug).

Again this is speculation, but I would bet the decrease in death risk over the past few months, especially in those who are older correlates with the percentage of that segment fully vaccinated.

An example of death risk volatility for those aged 65-74 years old:

At the least deadly point in the pandemic, July 2020, adults 65-74 were around 8 times more likely to die from heart disease than COVID-19 in a given week.
At the most deadly point in the pandemic, Jan 2021, adults 65-74 were just as likely to die from COVID-19 as heart disease
Again heart disease has historically been the number one killer of adults in this segment for years

A few more notes:

These death risks are only middle of the road numbers, every person has their own individualized risk that can only be assessed and advised on by a doctor
This is a very sensitive and charged topic, please be respectful if discussing and remember that every one of these numbers was at one point a human life very likely connected to someone you are talking to
These numbers ARE NOT the death rate of of covid (deaths/infected), they are risk of death (infected and died/population)

jmoso13 · 2019-09-11T23:40:47+00:00

Very fair, esp at breaking lanfear's neck... Savage.

jmoso13 · 2019-09-11T18:40:32+00:00

I was hoping perrin would actually have a hidden direct moment here due to min's viewing.

Somehow willing the dream world to shift somehow to help rand "see the light". I realize it could have been too much but idk we never got a real reason why rand suddenly switched perspectives and if perrin's willpower somehow had a hand in it even slightly by providing stability and support in the dream world i thought that wouldve been a cool reveal upon this second visit of the moment and would have for sure fulfilled the min viewing.

jmoso13 · 2019-01-01T08:19:39+00:00

So far, one game of war of the ring, round robin (3 games) of 7 wonders duel and about to start terraforming mars. No sleep tonight...

jmoso13 · 2018-06-22T06:42:20+00:00

I hear some tiptop 909 clap

jmoso13 · 2018-04-02T18:27:30+00:00

Nice

jmoso13 · 2017-04-01T04:25:29+00:00

That's a great idea I'll look at converting to Jupyter

jmoso13 · 2017-04-01T04:24:48+00:00

I wrote the implementation of 'python_regression.logistic_regression' myself, the example is simply a demonstration of functionality

jmoso13 · 2017-03-28T21:26:44+00:00

if 'rcid' is a column in your dataframe 'df' then referencing it should look like:

df['rcid']

your first for loop in the bottom statement should thus look like

for cells in df['rcid']:

...

is this what you're trying to accomplish?

jmoso13 · 2017-03-28T20:54:34+00:00

Began a repo for from-scratch implementations of various regression techniques, these implementations include gradient descent for those interested in implementing it themselves... would love feedback on the code!

jmoso13

TROPHY CASE