This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
Medial Olivocochlear (MOC) Efferent Acoustic Reflex and Contralateral OAE Suppression Analyzer based on auditory biophysics.
flowchart TD
A[Baseline OAE in Quiet] --> B[Record Contralateral CAS Suppression]
B --> C[Compute Efferent Delta: Baseline - CAS dB SPL]
C --> D[Compute Linear Voltage Damping %]
D --> E{Suppression Delta Threshold}
E -- >= 1.0 dB --> F[Robust Cochlear Efferent Protection]
E -- < 0.5 dB --> G[Blunted Suppression / Efferent Vulnerability]Zero PIP Dependencies: Implemented strictly with Python Standard Library (math, json, sys, typing).
Acoustic Efferent Modeling: Quantifies outer hair cell electromotility damping under contralateral noise.
Artifact Protection: Checks for acoustic middle-ear stapedial reflex contamination at high sound levels.
Dual Architecture: Native Python import and Model Context Protocol (MCP) JSON-RPC 2.0 stdio server.
Related MCP server: Distortion Product Otoacoustic Emissions (DPOAE) Skill
python example_usage.pyApache-2.0
HLA nomenclature and match checks against a pinned IPD-IMGT/HLA release. No patient identifiers.
Evaluate ML features beyond a baseline and test bounded feature repairs. Invite-only beta.
Pronunciation assessment, phoneme scoring, speaker voice ID, audio transcription, speech synthesis.
Coherence-gated signal engine. Paid verdict settled end-to-end 2026-11-01 (SELF-PROOF, XRPL).