diff --git a/python/TimeAlignment.ipynb b/python/TimeAlignment.ipynb new file mode 100644 index 0000000..919df14 --- /dev/null +++ b/python/TimeAlignment.ipynb @@ -0,0 +1,388 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6c1847cf-690a-4f80-87b4-37d101f7a9d7", + "metadata": {}, + "outputs": [], + "source": [ + "import scipy as sp\n", + "import numpy as np\n", + "import pyfar as pf\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "id": "2d15864b-cd89-449a-80e7-48bab97c730a", + "metadata": {}, + "source": [ + "# Define functions" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b655b517-5129-4e86-91f0-1427845df270", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate order (n) and degree (m) indices arrays for sph order N\n", + "def get_nm_arrays(N):\n", + " n = np.array([])\n", + " m = np.array([])\n", + " for nn in range(N+1):\n", + " n = np.append(n, nn * np.ones(2*nn+1))\n", + " m = np.append(m, np.arange(-nn, nn+1))\n", + " return n, m\n", + "\n", + "def get_k_from_f(f):\n", + " return ( ( 2*np.pi * f ) / 343 )\n", + "\n", + "def get_max_sph_order(Q, a=1):\n", + " \"\"\" Computes maximum spherical harmonics order from number of sampling points \n", + " and lambda (a) depending on the grid topology. \n", + " The default for lamda is 1.\"\"\"\n", + " \n", + " return int ( np.sqrt( (( Q - a ) / a ) + 1) - 1 )\n", + "\n", + "def get_hrtf_eq_dataset( Y_sampling, m, n, frequencies, ear_distance=0.165):\n", + " \"\"\" returns eqDataset containing sh coefficients describing \n", + " the sound incidence at the letf/right ear position on the sphere. \n", + " length of sh coefficients is length of input frequencies.\n", + " Y_sampling uses same sampling points as sampling, but with higher sph order.\"\"\" \n", + "\n", + " eq_matrix = []\n", + "\n", + " p = 1 # arbitrary sound pressure level ???\n", + " radius = ear_distance / 2 \n", + " \n", + " i_n = 1j**n\n", + "\n", + " Y_left_ear = sp.special.sph_harm(m, n, -np.pi, 0)\n", + " Y_right_ear = sp.special.sph_harm(m, n, np.pi, 0)\n", + "\n", + " kr = [ get_k_from_f(f) * radius for f in frequencies ]\n", + "\n", + " for Y_ear in [Y_left_ear, Y_right_ear]:\n", + " # rigid sphere transfer function as described in Pörschmann et al. 2019\n", + " eq_matrix.append( [p * 4*np.pi * i_n * sp.special.spherical_jn(n, z) * Y_ear * np.conjugate(Y_sampling) for z in kr])\n", + " \n", + " eq_dataset = np.sum( np.array( eq_matrix ), axis=3) # sum over all sph kernels (part of inverse psh transformation)\n", + " eq_dataset = np.swapaxes( np.swapaxes(eq_dataset, 1, 2), 0, 1)\n", + " \n", + " return eq_dataset\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "991cfeea-2d71-4b7b-9dd8-1497ab2a5736", + "metadata": {}, + "source": [ + "# Load hrirs" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b77ff561-7df7-4b8f-bb35-93402ab24752", + "metadata": {}, + "outputs": [], + "source": [ + "file = os.path.join(\n", + " r'..', 'assets', 'pp2_HRIRs_measured.sofa')\n", + "hrirs, sg, _ = pf.io.read_sofa(file)" + ] + }, + { + "cell_type": "markdown", + "id": "782bf25e-6fc2-41b9-bb1d-e4304d04c5f5", + "metadata": {}, + "source": [ + "# Get spherical harmonics functions for original sampling grid" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "91c087e5-676e-4ed4-b0e1-b0db1c9d1be2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(440, 1296)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sph_order = 35 \n", + "n, m = get_nm_arrays(sph_order)\n", + "Y_sampling = sp.special.sph_harm(m[None, ...], n[None, ...], sg.azimuth[..., None], sg.colatitude[..., None])\n", + "\n", + "Y_sampling.shape # sampling pos, sph kernels\n", + "#radius = 1.47\n", + "#frequencies = hrirs.frequencies\n", + "#Q = np.ma.size(hrirs, axis=0)" + ] + }, + { + "cell_type": "markdown", + "id": "bc461e72-576f-4975-bd2b-463a0cbfbeb3", + "metadata": {}, + "source": [ + "# Get rigid sphere transfer function (equalization dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cd5b334f-e19c-4a9c-a83e-f6544e67e521", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(440, 2, 129)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frequencies = hrirs.frequencies\n", + "\n", + "eq_dataset = get_hrtf_eq_dataset(Y_sampling, m, n, frequencies)\n", + "eq_dataset.shape # left/right ear, frequencies, sampling positions, sph kernels" + ] + }, + { + "cell_type": "markdown", + "id": "d5bb4a1b-687b-4857-80ea-27b5f629e502", + "metadata": {}, + "source": [ + "# Apply equalization" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7300c8ff-d22e-4eea-86f9-4fb39530a964", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/rf/2tvjwq1n48l_gv0rrdfq6j5r0000gp/T/ipykernel_2435/1762649605.py:9: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " hrtf_equalized[i] = np.divide(pos_sparse[i], pos_rsphere[i])\n" + ] + } + ], + "source": [ + "hrirs.freq.shape # sampling positions, left/right ear, frequencies\n", + "number_channels = hrirs.freq.shape[1]\n", + "\n", + "hrtf_equalized = np.zeros((hrirs.freq.shape))\n", + "\n", + "# for every sampling position: divide hrtf with rigid sphere transfer function\n", + "for pos_sparse, pos_rsphere in zip(hrirs.freq, eq_dataset):\n", + " for i in range( number_channels ):\n", + " hrtf_equalized[i] = np.divide(pos_sparse[i], pos_rsphere[i])\n", + "\n", + "hrtf_equalized.shape\n", + "hrtf_equalized = np.swapaxes(hrtf_equalized, 0, 1)" + ] + }, + { + "cell_type": "markdown", + "id": "3820cf93-acb3-42fc-b6ad-45399b8baaf9", + "metadata": {}, + "source": [ + "# Get sh cofficients of equalized hrtf (sh-transform)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bc48b223-1a1b-40f1-9a6e-a3870a341a35", + "metadata": {}, + "outputs": [], + "source": [ + "# get inverse of sh transform kernels\n", + "Y_inv = np.linalg.pinv(Y_sampling)\n", + "\n", + "# SH transform\n", + "f_nm_L = pf.matrix_multiplication((Y_inv, hrtf_equalized[0]))\n", + "f_nm_R = pf.matrix_multiplication((Y_inv, hrtf_equalized[1]))" + ] + }, + { + "cell_type": "markdown", + "id": "7f894f48-9bb3-4798-b91e-203a023bc3c4", + "metadata": {}, + "source": [ + "# Upsample/interpolate equalized hrtf with dense sph grid (inverse sph transform)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a2055ce1-ca7f-4549-8f93-25b992d2c846", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/audio_feature_extraction/lib/python3.11/site-packages/pyfar/samplings/samplings.py:393: PyfarDeprecationWarning: 'This function will be deprecated in pyfar 0.8.0 in favor of spharpy.samplings.hyperinterpolation.'\n", + " warnings.warn((\n", + "/opt/anaconda3/envs/audio_feature_extraction/lib/python3.11/site-packages/pyfar/classes/coordinates.py:166: PyfarDeprecationWarning: 'This function will be deprecated in pyfar 0.8.0 in favor of spharpy.samplings.SamplingSphere.'\n", + " warnings.warn((\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "35\n", + "(1296, 1296)\n", + "(1296, 129)\n" + ] + } + ], + "source": [ + "# Equidistant spherical sampling grid\n", + "grid = pf.samplings.sph_extremal(n_points=None, sh_order=sph_order, radius=1.47)\n", + "print(sph_order)\n", + "\n", + "# Compute Y2 with denser grid\n", + "Y2 = sp.special.sph_harm(\n", + " m[None, ...], n[None, ...],\n", + " grid.azimuth[..., None], grid.colatitude[..., None])\n", + "\n", + "# inverse sh transform\n", + "print(Y2.shape) # positions, sph kernels\n", + "print(f_nm_L.shape)\n", + "hrirs_eq_sh_L = pf.matrix_multiplication((Y2, f_nm_L))\n", + "hrirs_eq_sh_R = pf.matrix_multiplication((Y2, f_nm_R))" + ] + }, + { + "cell_type": "markdown", + "id": "f7984933-9951-46a0-9d55-7dbdfa1c4b97", + "metadata": {}, + "source": [ + "# Reconstruct hrtf by dequalization" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "129225c4-1225-4d06-a59f-1497804f7544", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1296, 129)\n", + "(129, 440)\n" + ] + } + ], + "source": [ + "# multiply upsampled hrtf with rigid sphere transfer function for dequalization\n", + "print(hrirs_eq_sh_L.shape)\n", + "print(np.swapaxes( np.swapaxes( eq_dataset, 0, 1) [0], 0, 1 ).shape)\n", + "hrirs_sh_L = pf.matrix_multiplication((hrirs_eq_sh_L, np.swapaxes( np.swapaxes( eq_dataset, 0, 1) [0], 0, 1 )))\n", + "hrirs_sh_R = pf.matrix_multiplication((hrirs_eq_sh_R, np.swapaxes( np.swapaxes( eq_dataset, 0, 1) [1], 0, 1 )))" + ] + }, + { + "cell_type": "markdown", + "id": "83aa1c99-313d-4c1b-a09d-d0a849308743", + "metadata": {}, + "source": [ + "# Plot specific hrir" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b301a806-d743-4aec-ad54-30afe3cf9284", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/audio_feature_extraction/lib/python3.11/site-packages/pyfar/classes/audio.py:617: UserWarning: Number of samples not given, assuming 2590 samples from 1296 frequency bins.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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