Add AnechoicRoom class (#224)

.gitignore

@@ -11,3 +11,7 @@ docs/_build
 dist/
 local_examples/
 *.ipynb_checkpoints
+# files added after editable install (pip install -e . )
+pyroomacoustics.egg-info/
+pyroomacoustics/build_rir.c
+pyroomacoustics/**/*.so

CHANGELOG.rst

@@ -14,6 +14,7 @@ adheres to `Semantic Versioning <http://semver.org/spec/v2.0.0.html>`_.
 Added
 ~~~~~
 
+- Added the AnechoicRoom class.
 - Added FastMNMF2 (Fast Multichannel Nonnegative Matrix Factorization 2) to ``bss`` subpackage.
 - Randomized image source method for removing sweeping echoes in shoebox rooms.
 - Adds the ``cart2spher`` method in ``pyroomacoustics.doa.utils`` to convert from cartesian

CONTRIBUTING.rst

@@ -37,7 +37,7 @@ style <https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt>`__
 We recommend the following steps for generating the documentation:
 
 -  Create a separate environment, e.g. with Anaconda, as such:
-   ``conda create -n mkdocs37 python=3.7 sphinx numpydoc mock sphinx_rtd_theme``
+   ``conda create -n mkdocs37 python=3.7 sphinx numpydoc mock sphinx_rtd_theme sphinxcontrib-napoleon tabulate`` 
 -  Switch to the environment: ``source activate mkdocs37``
 -  Navigate to the ``docs`` folder and run: ``./make_apidoc.sh``
 -  The materials database page is generated with the script ``./make_materials_table.py``

docs/make_materials_table.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python
 """
 This helper script creates a documentation page from the materials database
 that is stored in JSON format.
@@ -83,3 +84,4 @@ def print_section(title, data, headers, file):
                 headers=headers,
                 file=f,
             )
+        print("Wrote file", docfile)

docs/pyroomacoustics.doa.normmusic.rst

@@ -0,0 +1,7 @@
+pyroomacoustics.doa.normmusic module
+====================================
+
+.. automodule:: pyroomacoustics.doa.normmusic
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/pyroomacoustics.libroom.rst

@@ -0,0 +1,7 @@
+pyroomacoustics.libroom module
+==============================
+
+.. automodule:: pyroomacoustics.libroom
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/requirements.txt

@@ -2,3 +2,4 @@ sphinx>=1.6.2
 mock>=2.0
 sphinx_rtd_theme
 sphinxcontrib-napoleon
+tabulate

examples/beamforming_delay_and_sum.py

@@ -7,8 +7,8 @@
 import matplotlib.pyplot as plt
 import pyroomacoustics as pra
 
-# Create a 4 by 6 metres shoe box room
-room = pra.ShoeBox([4, 6])
+# Create a 4 by 6 metres anechoic room
+room = pra.AnechoicRoom(dim=2)
 
 # Add a source somewhere in the room
 room.add_source([2.5, 4.5])
@@ -22,5 +22,5 @@
 room.mic_array.rake_delay_and_sum_weights(room.sources[0][:1])
 
 # plot the room and resulting beamformer
-room.plot(freq=[1000, 2000, 4000, 8000], img_order=0)
+room.plot(freq=[1000, 2000, 4000, 8000])
 plt.show()

examples/doa_algorithms.py

@@ -55,6 +55,14 @@
 # Location of original source
 azimuth = 61.0 / 180.0 * np.pi  # 60 degrees
 distance = 3.0  # 3 meters
+dim = 2  # dimensions (2 or 3)
+room_dim = np.r_[10.0, 10.0]
+
+# Use AnechoicRoom or ShoeBox implementation. The results are equivalent because max_order=0 for both.
+# The plots change a little because in one case there are no walls.
+use_anechoic_class = True   
+
+print("============ Using anechoic: {} ==================".format(anechoic))
 
 #######################
 # algorithms parameters
@@ -68,8 +76,10 @@
 sigma2 = 10 ** (-SNR / 10) / (4.0 * np.pi * distance) ** 2
 
 # Create an anechoic room
-room_dim = np.r_[10.0, 10.0]
-aroom = pra.ShoeBox(room_dim, fs=fs, max_order=0, sigma2_awgn=sigma2)
+if use_anechoic_class:
+    aroom = pra.AnechoicRoom(dim, fs=fs, sigma2_awgn=sigma2)
+else:
+    aroom = pra.ShoeBox(room_dim, fs=fs, max_order=0, sigma2_awgn=sigma2)
 
 # add the source
 source_location = room_dim / 2 + distance * np.r_[np.cos(azimuth), np.sin(azimuth)]

examples/doa_anechoic_room.py

@@ -0,0 +1,64 @@
+"""
+This example creates a free-field simulation of direction of arrival estimation.
+"""
+
+import argparse
+
+import numpy as np
+
+import pyroomacoustics as pra
+
+methods = ["MUSIC", "FRIDA", "WAVES", "TOPS", "CSSM", "SRP", "NormMUSIC"]
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser(
+        description="Estimates the direction of arrival of a sound source."
+    )
+    parser.add_argument(
+        "--method",
+        "-m",
+        choices=methods,
+        default=methods[0],
+        help="DOA method to use",
+    )
+    args = parser.parse_args()
+
+    # we use a white noise signal for the source
+    nfft = 256
+    fs = 16000
+    x = np.random.randn((nfft // 2 + 1) * nfft)
+
+    # create anechoic room
+    room = pra.AnechoicRoom(fs=fs)
+
+    # place the source at a 90 degree angle and 5 meters distance from origin
+    azimuth_true = np.pi / 2
+    room.add_source([5 * np.cos(azimuth_true), 5 * np.sin(azimuth_true), 0], signal=x)
+
+    # place the microphone array
+    mic_locs = np.c_[
+        [0.1, 0.1, 0],
+        [-0.1, 0.1, 0],
+        [-0.1, -0.1, 0],
+        [0.1, -0.1, 0],
+    ]
+    room.add_microphone_array(mic_locs)
+
+    # run the simulation
+    room.simulate()
+
+    # create frequency-domain input for DOA algorithms
+    X = pra.transform.stft.analysis(
+        room.mic_array.signals.T, nfft, nfft // 2, win=np.hanning(nfft)
+    )
+    X = np.swapaxes(X, 2, 0)
+
+    # perform DOA estimation
+    doa = pra.doa.algorithms[args.method](mic_locs, fs, nfft)
+    doa.locate_sources(X)
+
+    # evaluate result
+    print("Source is estimated at:", doa.azimuth_recon)
+    print("Real source is at:", azimuth_true)
+    print("Error:", pra.doa.circ_dist(azimuth_true, doa.azimuth_recon))

pyroomacoustics/beamforming.py

@@ -187,7 +187,7 @@ def square_2D_array(center, M, N, phi, d):
         The center of the array
     M: int
         The number of points in the first dimension
-    M: int
+    N: int
         The number of points in the second dimension
     phi: float
         The counterclockwise rotation of the array (from the x-axis)
@@ -806,9 +806,7 @@ def plot_beam_response(self):
         if self.weights is None and self.filters is not None:
             self.weights_from_filters()
         elif self.weights is None and self.filters is None:
-            raise NameError(
-                "Beamforming weights or filters need to be computed" " first."
-            )
+            raise NameError("Beamforming weights or filters need to be computed first.")
 
         phi = np.linspace(-np.pi, np.pi - np.pi / 180, 360)
         freq = self.frequencies

pyroomacoustics/doa/doa.py

@@ -5,7 +5,7 @@
 import math
 import sys
 import warnings
-from abc import ABCMeta, abstractmethod
+from abc import ABCMeta
 
 import numpy as np