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README.md

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+---
+license: mit
+tags:
+  - audio
+  - binaural
+  - hrtf
+  - hrir
+  - spatial-audio
+  - asmr
+language:
+  - en
+  - ja
+---
+
+# Binaural HRIR Spatial Map
+
+Head-Related Impulse Response (HRIR) lookup table for real-time binaural audio rendering.
+
+## Grid Specification
+
+| Parameter | Value |
+|---|---|
+| Azimuth | 0°–355° (Δ5.0°, 72 points) |
+| Distance | 0.10m–2.00m (26 points) |
+| Elevation | 0.0° (fixed) |
+| Total points | 1,872 |
+| IR length | 512 samples (10.7 ms) |
+| Sample rate | 48000 Hz |
+| Dtype | float32 |
+
+### Distance Grid (near-field dense)
+
+```
+0.10, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.20, 0.22, 0.25, 0.28, 0.30, 0.33, 0.36, 0.40, 0.45, 0.50, 0.60, 0.75, 1.00, 1.25, 1.50, 2.00
+```
+
+- 10–20 cm: 1 cm step (11 points) — near-field, high HRTF variation
+- 20–40 cm: 2–3 cm step (7 points)
+- 40 cm–1 m: 5–15 cm step (4 points)
+- 1–2 m: 25–50 cm step (4 points)
+
+## HRTF Model
+
+Rigid sphere model with:
+- Spherical wave expansion (near-field)
+- Ear canal resonance (angle/distance dependent)
+- Pinna diffraction
+- Interaural Time Difference (ITD)
+- Torso reflection
+- Air absorption
+
+## Usage
+
+```python
+import numpy as np
+
+data = np.load("hrir_spatial_map.npz")
+azimuths = data["azimuths"]    # (72,) float32 — [0, 5, 10, ..., 355]
+distances = data["distances"]  # (26,) float32 — [0.10, 0.11, ..., 2.00]
+hrir_L = data["hrir_L"]        # (72, 26, 512) float32
+hrir_R = data["hrir_R"]        # (72, 26, 512) float32
+
+# Look up HRIR for azimuth=270° (right), distance=0.10m
+az_idx = int(270 / 5)   # = 54
+dist_idx = 0             # = 0.10m
+ir_L = hrir_L[az_idx, dist_idx]  # (512,) float32
+ir_R = hrir_R[az_idx, dist_idx]  # (512,) float32
+
+# Binaural rendering via direct convolution
+out_L = np.convolve(mono_audio, ir_L, mode="full")[:len(mono_audio)]
+out_R = np.convolve(mono_audio, ir_R, mode="full")[:len(mono_audio)]
+stereo = np.stack([out_L, out_R], axis=-1)
+```
+
+### Interpolation for arbitrary positions
+
+```python
+def interpolate_hrir(hrir_L, hrir_R, azimuths, distances, az_deg, dist_m):
+    az_step = azimuths[1] - azimuths[0]
+    az_idx_f = (az_deg % 360) / az_step
+    az_i0 = int(az_idx_f) % len(azimuths)
+    az_i1 = (az_i0 + 1) % len(azimuths)
+    az_alpha = az_idx_f - int(az_idx_f)
+
+    dist_m = np.clip(dist_m, distances[0], distances[-1])
+    d_idx = np.searchsorted(distances, dist_m, side="right") - 1
+    d_idx = np.clip(d_idx, 0, len(distances) - 2)
+    d_alpha = (dist_m - distances[d_idx]) / (distances[d_idx + 1] - distances[d_idx])
+
+    L00 = hrir_L[az_i0, d_idx]
+    L01 = hrir_L[az_i0, d_idx + 1]
+    L10 = hrir_L[az_i1, d_idx]
+    L11 = hrir_L[az_i1, d_idx + 1]
+    L = (1 - az_alpha) * ((1 - d_alpha) * L00 + d_alpha * L01) \
+      + az_alpha * ((1 - d_alpha) * L10 + d_alpha * L11)
+
+    R00 = hrir_R[az_i0, d_idx]
+    R01 = hrir_R[az_i0, d_idx + 1]
+    R10 = hrir_R[az_i1, d_idx]
+    R11 = hrir_R[az_i1, d_idx + 1]
+    R = (1 - az_alpha) * ((1 - d_alpha) * R00 + d_alpha * R01) \
+      + az_alpha * ((1 - d_alpha) * R10 + d_alpha * R11)
+
+    return L.astype(np.float32), R.astype(np.float32)
+```
+
+## License
+
+MIT

hrir_spatial_map.npz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bba8a10360110aa80428674da5e10583a0b5f288d9dc1cfd930d66e60b6b3dd1
+size 7147591

metadata.json

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+{
+  "description": "HRIR spatial map for binaural audio rendering",
+  "model": "rigid sphere + ear canal + pinna + torso + air absorption",
+  "grid": {
+    "azimuth_step_deg": 5.0,
+    "azimuth_range": [
+      0.0,
+      355.0
+    ],
+    "n_azimuths": 72,
+    "distances_m": [
+      0.10000000149011612,
+      0.10999999940395355,
+      0.11999999731779099,
+      0.12999999523162842,
+      0.14000000059604645,
+      0.15000000596046448,
+      0.1599999964237213,
+      0.17000000178813934,
+      0.18000000715255737,
+      0.1899999976158142,
+      0.20000000298023224,
+      0.2199999988079071,
+      0.25,
+      0.2800000011920929,
+      0.30000001192092896,
+      0.33000001311302185,
+      0.36000001430511475,
+      0.4000000059604645,
+      0.44999998807907104,
+      0.5,
+      0.6000000238418579,
+      0.75,
+      1.0,
+      1.25,
+      1.5,
+      2.0
+    ],
+    "n_distances": 26,
+    "elevation_deg": 0.0
+  },
+  "audio": {
+    "sample_rate": 48000,
+    "ir_length_samples": 512,
+    "ir_length_ms": 10.67,
+    "dtype": "float32"
+  },
+  "shape": {
+    "hrir_L": [
+      72,
+      26,
+      512
+    ],
+    "hrir_R": [
+      72,
+      26,
+      512
+    ]
+  },
+  "total_points": 1872,
+  "npz_size_bytes": 7147591,
+  "head_params": {
+    "head_radius_m": 0.0875,
+    "ear_canal_length_m": 0.025
+  },
+  "usage": "data = np.load('hrir_spatial_map.npz'); hrir_L = data['hrir_L'][az_idx, dist_idx]; output = np.convolve(mono, hrir_L)"
+}