Add native audio/image efferent surfaces + projector proof-of-shape

audio-surface.el / image-surface.el: own-core additive-synthesis WAV and
raster-PNG renderers (integer-only DSP, since EL has no floats), rendered
from learned engram signatures via a pluggable surface-profile
abstraction (surface-profile.el). audio-demo.el / image-demo.el are
drivers. NOTE: demo files hardcode absolute paths to this worktree's own
directory — will need a path fixup before landing.

elp/projector/ is a Python package the author's own README marks as
"STAGING/PROOF-OF-SHAPE — not the deliverable", superseded by the native
.el surface-profile work above; kept as a validated architecture proof.
Generated output (elp/faculty/{out,sig}, elp/projector/out,
__pycache__) intentionally excluded.

elp/projector/generate.py

@@ -0,0 +1,81 @@
+"""generate.py — drive the projector: one geometry region -> many surfaces.
+
+Proves the thesis with REAL output: builds ONE surface-neutral DocumentIR from
+Neuron's OWN self-geometry (read-only against the live soul via the proven
+faculty), then EMITS it to Markdown, docx, and MIDI — the same plan/realize/
+cohere, three surfaces. Writes the files + the faithfulness audit to ./out/.
+"""
+from __future__ import annotations
+
+import json
+import os
+import sys
+
+_HERE = os.path.dirname(os.path.abspath(__file__))
+sys.path.insert(0, _HERE)
+
+import pipeline  # noqa: E402
+import provenance  # noqa: E402
+from geometry import load_self_region  # noqa: E402
+
+OUT = os.path.join(_HERE, "out")
+
+
+def _emit_all(doc, stem):
+    """Emit one IR to every text/audio surface + audit + provenance."""
+    for surface in ("markdown", "docx", "midi"):
+        data = pipeline.emit(doc, surface)
+        proj = pipeline.get_projector(surface)
+        path = os.path.join(OUT, f"{stem}.{proj.ext}")
+        with open(path, "wb") as f:
+            f.write(data)
+        print(f"  emitted {surface:9s} -> {os.path.basename(path)} ({len(data)} bytes)")
+    a = provenance.audit(doc)
+    with open(os.path.join(OUT, f"{stem}.audit.json"), "w") as f:
+        json.dump(a, f, indent=2)
+    with open(os.path.join(OUT, f"{stem}.provenance.md"), "w") as f:
+        f.write(provenance.trace_table(doc))
+    print("  audit:", {k: a[k] for k in ("claims", "ungrounded_claims",
+          "negations_preserved", "distinct_source_nodes", "faithful")})
+    return a
+
+
+def main():
+    os.makedirs(OUT, exist_ok=True)
+    print("surfaces registered:", pipeline.available_surfaces())
+
+    # ---- Document 1: Neuron's self-description (marquee) ------------------- #
+    print("\n[1] Neuron self-description")
+    region = load_self_region(max_nodes=9)
+    print("   self region:", region)
+    doc1 = pipeline.build_ir(
+        None, region=region,
+        title="Neuron: A Self-Description from Its Own Geometry",
+        subtitle="Projected efferently from the engram — every claim traces a node.",
+        format_spec={"genre": "self-description", "register": "expository"},
+        max_sections=5, conf_floor=0.6)
+    print(f"   IR: {len(doc1.sections)} sections, {doc1.claim_count()} claims, "
+          f"ungrounded={doc1.ungrounded_count()}")
+    _emit_all(doc1, "neuron-self")
+
+    # ---- Document 2: a coherent, clean whitepaper-style section ------------ #
+    print("\n[2] Whitepaper-style section (coherent clean region)")
+    doc2, _ = pipeline.project(
+        ["chronoception", "time", "awareness", "engram", "temporal"],
+        surface="markdown",
+        title="Temporal Awareness in the Engram",
+        subtitle="A section projected from the geometry of chronoception.",
+        format_spec={"genre": "whitepaper-section", "register": "technical"},
+        max_sections=4)
+    print(f"   IR: {len(doc2.sections)} sections, {doc2.claim_count()} claims, "
+          f"ungrounded={doc2.ungrounded_count()}")
+    _emit_all(doc2, "engram-temporal")
+
+    # echo both markdowns so they are visible in the run log
+    for stem, doc in (("neuron-self", doc1), ("engram-temporal", doc2)):
+        print(f"\n===== GENERATED MARKDOWN — {stem} =====\n")
+        print(pipeline.emit(doc, "markdown").decode())
+
+
+if __name__ == "__main__":
+    main()