🧠 How Feedback Fine-Tunes Neuroscience-Based Audio

1️⃣ The Core Concept

You’re combining two systems:

AI-generated music (from ElevenLabs or other models) — gives the aesthetic and emotional tone.

Neuroscience modulation layer (binaural/isochronic frequencies, amplitude envelopes, etc.) — gives the physiological effect.

User feedback (or biometric input) acts as the reward signal in a loop that tunes the parameters of the second system.

2️⃣ The Parameters You Can Fine-Tune

Each session generates a track with specific entrainment parameters, for example:

Parameter	Description	Example Range
diffHz	Difference between tones (controls brainwave entrainment frequency)	4–20 Hz
mixDb	Volume level of the entrainment layer	−24 dB – −12 dB
mode	Type of signal (binaural / isochronic / amplitude-modulated)	categorical
fadeSeconds	Length of fade-in/out to avoid abruptness	2 – 10 s
carrierFreq	Base frequency (e.g., 200 Hz – 440 Hz)	150 – 500 Hz
sessionLength	Duration (in seconds)	60 – 180 s

3️⃣ Feedback Sources

You can treat feedback as explicit or implicit signals:

Type	Source	Example Metric	Signal Meaning
Explicit	User rating (“felt relaxed”, “too stimulating”)	1–5 stars, or slider	Direct reinforcement signal
Implicit (biometric)	HRV, HR, EEG alpha/theta ratio	HRV↑ → relaxed / focus ↑	Physiological reward
Behavioural	Session completion, repeats, skip rate	Longer sessions = better response	Engagement proxy

All these become input features to your optimisation logic.

4️⃣ The Optimisation Loop (Bayesian / Reinforcement style)

You can think of each session as an experiment:

System generates a sound with parameters (p).

User experiences it and returns feedback (r).

Optimizer updates its belief of which parameters maximise reward (focus, calm, etc.).

Next session picks parameters slightly adjusted toward the predicted optimum.

In pseudocode:

python
# simplified bandit loop
history = []

for session in sessions:
    p = suggest_params(history)
    r = get_feedback_from_user(p)
    history.append((p, r))
    update_model(history)

🧮 Implementation: Bayesian Optimisation

A practical lightweight choice is Gaussian Process / Bayesian optimisation (e.g., skopt, ax-platform, hyperopt).

It models your function:

[

f(p) = \text{user satisfaction or physiological improvement}

]

and searches parameter space efficiently, balancing exploration vs exploitation.

Example loop:

python
from skopt import gp_minimize
from skopt.space import Real, Categorical
from skopt.utils import use_named_args

space  = [Real(4.0, 20.0, name='diffHz'),
          Real(-24.0, -12.0, name='mixDb'),
          Categorical(['binaural','isochronic'], name='mode')]

@use_named_args(space)
def objective(diffHz, mixDb, mode):
    score = run_session_and_collect_feedback(diffHz, mixDb, mode)
    return -score  # maximise reward

res = gp_minimize(objective, space, n_calls=30)
best_params = res.x

Over time, each user’s personal optimiser converges toward their “sweet spot.”

5️⃣ Adaptive Tuning Logic (Simpler MVP version)

In your MVP (first 6 weeks), skip the full GP.

Start with rule-based adaptation:

Condition	Adjustment
User rated “too strong”	↓ mixDb by 2 dB
User rated “too weak”	↑ mixDb by 2 dB
User “felt sleepy” but target=Focus	↑ diffHz by 1–2 Hz
User “felt anxious” but target=Calm	↓ diffHz by 1–2 Hz
HRV ↑ vs baseline	keep params steady
HRV ↓ vs baseline	soften mix, lower diffHz

This gives immediate perceived personalisation with zero heavy modelling.

6️⃣ Feedback Storage Model

Table	Fields	Notes
`sessions`	id, user_id, state, diffHz, mixDb, mode, created_at	Each sound session
`feedback`	session_id, rating, comment, HRV_before, HRV_after, computed_score	Links to optimisation loop
`user_profile`	user_id, preferred_params (JSON), updated_at	Cached personal optimum

7️⃣ How It Feeds Future Personalisation

Once you have hundreds of sessions, you can build per-user or population-level models:

User-specific GP → learns individual neuro-response curve.

Cluster analysis → groups users by response profile (“low-beta responders”, “theta-dominant relaxers”).

AI-generated music conditioning → pick emotional timbre that synergises with the found frequency pattern.

8️⃣ Visual Summary (Flow)

flowchart TD A["User selects state"] --> B["Node.js generates base music via ElevenLabs"] B --> C["Python DSP adds entrainment"] C --> D["User listens"] D --> E["Feedback (rating / HRV / EEG)"] E --> F["Optimizer updates parameter model"] F --> G["Next session personalised diffHz / mixDb / mode"] G --> B

9️⃣ Example in Action

User: chooses “Calm” → system plays 7 Hz theta beat at −20 dB

Feedback: “too sleepy”

Adjustment: next session → 8 Hz @ −18 dB

Effect: moves user’s state closer to alert calmness

Over time, their calm profile converges to 8.3 Hz @ −19 dB (binaural), i.e. their individualised entrainment signature.

🔬 10️⃣ Validation Metrics (for scientific credibility)

Track over sessions:

Mean HRV delta (before vs after)

Self-reported calmness/focus Δ

Retention (voluntary session repeats)

Optimal parameter variance per user (stability)

TL;DR

Feedback is the reinforcement signal that tunes the entrainment parameters (frequency, intensity, mode) for each user, making the system increasingly personalised and effective over time.

Data schema for Feedback Optimiser

Fine Tune Audience by Feedback