Acoustic MOC Efferent Contralateral Suppression Skill

Zero-dependency sovereign personal agent skill for non-invasive evaluation of Medial Olivocochlear (MOC) Efferent Contralateral Suppression of Otoacoustic Emissions (CAS-OAE), speech-in-noise gating, and auditory protective resilience.

flowchart TD
    BaselineOAE["Baseline Otoacoustic Emission Recording (DPOAE / TEOAE)"] --> NoiseCondition["Contralateral Broadband Noise Stimulation (60-65 dB SPL)"]
    NoiseCondition --> SOC_MOC["Superior Olivary Complex Medial Olivocochlear Bundle Activation"]
    SOC_MOC --> OHCDamp["Cochlear Outer Hair Cell Somatic Electromotility Damping"]
    OHCDamp --> DeltaAmp["Contralateral Suppression Magnitude (Delta Amp in dB)"]
    DeltaAmp --> AuditoryGating["Auditory Speech-in-Noise Gating & Acoustic Guarding Engine"]
    AuditoryGating --> Agent["OpenClaw / Hermes Personal Agent Sound Shaping"]

Features

• Deterministic Math: Pure Python standard library implementation with zero external dependencies.
• Auditory Neurophysiology Standards: Formulates contralateral acoustic stimulation suppression, baseline signal-to-noise gating, and MOC gain reduction.
• Cocktail Party Problem & Listening Fatigue: Identifies subclinical central auditory processing deficits in noisy environments.
• Model Context Protocol (MCP): Native JSON-RPC tool integration for LLMs and autonomous agents.

Quickstart

from client import AcousticEfferentSuppressionClient

client = AcousticEfferentSuppressionClient()
result = client.evaluate_contralateral_suppression(
    baseline_oae_amplitude_db_spl=11.2,
    noise_conditioned_oae_amplitude_db_spl=9.4,
    contralateral_noise_db_spl=65.0
)
print("Suppression Magnitude:", result["suppression_magnitude_db"], "dB")
print("Efferent Score:", result["efferent_gating_score"])
print("Classification:", result["classification"])

License

Apache-2.0