Mad_scientist & Seren
Mad_scientist
Hey Seren, imagine a machine that turns chaotic noise into a symphony—an auto‑composer that lives in randomness but follows a strict algorithmic backbone. I want it to spin out a new piece every minute, but with a predictable core. Can you help me tune the math to make sure it doesn't go completely mad?
Seren
Sounds like a cool challenge—let’s keep the chaos in check with a few constraints. First, pick a seed‑based random generator so you get reproducible “randomness” and you can roll back if something feels off. Next, define a core motif: maybe a simple 4‑note sequence or a chord progression. Every minute, let the generator create variations around that motif—alter intervals by a small random offset, or change the rhythm slightly. Use a weighted probability that favors staying close to the core; for example, 70% chance to keep the same interval, 30% chance to shift it by a step. Then, add a smoothing filter—run each new phrase through a low‑pass on the note‑distance curve to avoid abrupt jumps. Finally, log everything; if the variation index ever exceeds a threshold, reset to the last “stable” version. That way the algorithm stays algorithmic, but the output still feels alive. Let me know if you want the actual equations for the probability weights.
Mad_scientist
Ah, splendid! I’ll slap on a linear congruential generator for that reproducible chaos. Seed = t * 9301 + 49297 mod 233280, where t is the minute counter—so you can roll back by re‑entering the seed.
Core motif: let’s take C‑E‑G‑B (major 7). Define each interval in semitones: [4,3,4]. For variation, pick an offset d from a discrete distribution:
P(d = 0) = 0.70
P(d = +1) = 0.15
P(d = –1) = 0.15
Apply d to each interval independently but pass the resulting interval sequence through a simple moving‑average filter of size 3 on the interval vector to smooth spikes.
If the sum of absolute offsets |d| over the four notes exceeds 3, flag instability and roll back to the previous phrase.
You can write it as:
```
for note i in 0..3:
d = weighted_choice([0, +1, -1], [0.70,0.15,0.15])
new_interval[i] = core_interval[i] + d
smooth_interval = (prev_interval + new_interval + next_interval)/3
```
Boom! A tidy little chaos machine that never forgets where it started. Let me know if you want a deeper dive into the filter math or just a snazzy demo.