Add room generator.

.appveyor.yml

@@ -36,7 +36,9 @@ init:
 
 install:
   - git submodule update --init --recursive
-  - "set PATH=%MINICONDA%;%MINICONDA%\\Scripts;%PATH%"
+#  - "set PATH=%MINICONDA%;%MINICONDA%\\Scripts;%PATH%"
+  - echo ". C:\Miniconda3-x64/etc/profile.d/conda.sh" >> ~/.bashrc
+  - echo "conda activate" >> ~/.bashrc
   - "python --version"
   - "python -c \"import struct; print(struct.calcsize('P') * 8)\""
 

CHANGELOG.rst

@@ -22,6 +22,7 @@ Added
 - Acoustic properties of different materials in ``pyroomacoustics.materials``
 - Scattering from the wall is handled via ray tracing method, scattering coefficients are provided
   in ``pyroomacoustics.materials.Material`` objects
+- ``random`` sub-package for room generation tools.
 
 
 Changed

docs/index.rst

@@ -20,6 +20,7 @@ Table of contents
   pyroomacoustics.bss
   pyroomacoustics.doa
   pyroomacoustics.denoise
+  pyroomacoustics.random
   pyroomacoustics
 
 Indices and tables

docs/pyroomacoustics.datasets.rst

@@ -25,4 +25,6 @@ Tools and Helpers
 
    pyroomacoustics.datasets.base
    pyroomacoustics.datasets.utils
+   pyroomacoustics.datasets.distribution
+   pyroomacoustics.datasets.room
 

docs/pyroomacoustics.experimental.rst

@@ -1,4 +1,4 @@
-pyroomacoustics.experimental package
+Experimental
 ====================================
 
 Submodules

docs/pyroomacoustics.random.distribution.rst

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

docs/pyroomacoustics.random.room.rst

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

docs/pyroomacoustics.random.rst

@@ -0,0 +1,19 @@
+Random Generation
+==============================
+
+Submodules
+----------
+
+.. toctree::
+
+   pyroomacoustics.random.distribution
+   pyroomacoustics.random.room
+   pyroomacoustics.random.source
+
+Module contents
+---------------
+
+.. automodule:: pyroomacoustics.random
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/pyroomacoustics.random.source.rst

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

docs/pyroomacoustics.rst

@@ -13,6 +13,7 @@ Subpackages
     pyroomacoustics.doa
     pyroomacoustics.experimental
     pyroomacoustics.transform
+    pyroomacoustics.random
 
 Submodules
 ----------

examples/generate_room_dataset.py

@@ -0,0 +1,217 @@
+import numpy as np
+import json
+import os
+from glob import glob
+from pprint import pprint
+import random
+import soundfile as sf
+import argparse
+
+from pyroomacoustics.utilities import rms, sample_audio
+from pyroomacoustics.random.room import ShoeBoxRoomGenerator
+
+
+"""
+
+Example script for:
+
+1) Generating a dataset of random room configuration and saving their 
+corresponding room impulse responses.
+```
+python examples/generate_room_dataset.py make_dataset
+```
+
+2) Randomly selecting a room from the dataset and applying its room impulse
+responses to a randomly selected speech file and (depending on the selected 
+room) some noise sources.
+```
+python examples/generate_room_dataset.py apply_rir \
+    --room_dataset <ROOM_DATASET_PATH>
+```
+
+"""
+
+example_noise_files = [
+    'examples/input_samples/doing_the_dishes.wav',
+    'examples/input_samples/exercise_bike.wav',
+    'examples/input_samples/running_tap.wav',
+]
+
+
+def make_dataset(n_rooms, source_min_dist_mic):
+    """
+
+    Generate a dataset of room impulse responses. A new folder will be created
+    with the name `pra_room_dataset_<TIMESTAMP>` with the following structure:
+
+    ```
+    pra_room_dataset_<TIMESTAMP>/
+        room_metadata.json
+        data/
+            room_<uuid>.npz
+            room_<uuid>.npz
+            ...
+    ```
+
+    where `room_metadata.json` contains metadata about each room configuration
+    in the `data` folder.
+
+    The `apply_rir` functions shows a room can be selected at random in order
+    to simulate a measurement in one of the randomly generated configurations.
+
+
+    Parameters
+    -----------
+    n_rooms : int
+        Number of room configurations to generate.
+    source_min_dist_mic : float
+        Minimum distance between each source and the microphone(s).
+    """
+
+    room_generator = ShoeBoxRoomGenerator(
+        source_min_dist_mic=source_min_dist_mic)
+    room_generator.create_dataset(n_rooms)
+
+
+def apply_rir(room_dataset, target_speech, noise_dir, snr_db, output_file):
+    """
+
+    Randomly selecting a room from the dataset and applying its room impulse
+    responses to a randomly selected speech file and (depending on the selected
+    room) some noise sources.
+
+    Parameters
+    -----------
+    room_dataset : str
+        Path to room dataset from calling `make_dataset`.
+    target_speech : str
+        Path to a target speech WAV file.
+    noise_dir : str
+        Path to a directory with noise WAV files. Default is to apply the room
+        impulse response to WAV file(s) from `examples/input_samples`.
+    snr_db : float
+        Desired signal-to-noise ratio resulting from simulation.
+    output_file : str
+        Path of output WAV file from simulation.
+
+    """
+    if room_dataset is None:
+        raise ValueError('Provide a path to a room dataset. You can compute '
+                         'one with the `make_dataset` command.')
+
+    with open(os.path.join(room_dataset, 'room_metadata.json')) as json_file:
+        room_metadata = json.load(json_file)
+
+    # pick a room at random
+    random_room_key = random.choice(list(room_metadata.keys()))
+    _room_metadata = room_metadata[random_room_key]
+    print('Room metadata')
+    pprint(_room_metadata)
+
+    # load target audio
+    target_data, fs_target = sf.read(target_speech)
+
+    # load impulse responses
+    ir_file = os.path.join(room_dataset, 'data', _room_metadata['file'])
+    ir_data = np.load(ir_file)
+    n_noises = ir_data['n_noise']
+    sample_rate = ir_data['sample_rate']
+    assert sample_rate == fs_target, 'Target sampling rate does not match IR' \
+                                     'sampling rate.'
+
+    # apply target IR
+    target_ir = ir_data['target_ir']
+    n_mics, ir_len = target_ir.shape
+    output_len = ir_len + len(target_data) - 1
+    room_output = np.zeros((n_mics, output_len))
+    for n in range(n_mics):
+        room_output[n] = np.convolve(target_data, target_ir[n])
+
+    # apply noise IR(s) if applicable
+    if n_noises:
+
+        if noise_dir is None:
+            noise_files = example_noise_files
+        else:
+            noise_files = glob(os.path.join(noise_dir, '*.wav'))
+        print('\nNumber of noise files : {}'.format(len(noise_files)))
+
+        _noise_files = np.random.choice(noise_files, size=n_noises,
+                                        replace=False)
+        print('Selected noise file(s) : {}'.format(_noise_files))
+        noise_output = np.zeros_like(room_output)
+        for k, _file in enumerate(_noise_files):
+
+            # load audio
+            noise_data, fs_noise = sf.read(_file)
+            assert fs_noise == sample_rate, 'Noise sampling rate {} does ' \
+                                            'not match IR sampling rate.' \
+                                            ''.format(_file)
+
+            # load impulse response
+            noise_ir = ir_data['noise_ir_{}'.format(k)]
+
+            # sample segment of noise and normalize so each source has
+            # roughly similar amplitude
+            # take a bit more audio than target audio so we are sure to fill
+            # up the end with noise (end of IR is sparse)
+            _noise = sample_audio(noise_data, int(1.1*output_len))
+            _noise /= _noise.max()
+
+            # apply impulse response
+            for n in range(n_mics):
+                noise_output[n] = np.convolve(_noise, noise_ir[n])[:output_len]
+
+        # rescale noise according to specified SNR, add to target signal
+        noise_rms = rms(noise_output[0])
+        signal_rms = rms(room_output[0])
+        noise_fact = signal_rms / noise_rms * 10 ** (-snr_db / 20.)
+        room_output += (noise_output * noise_fact)
+
+    else:
+        print('\nNo noise source in selected room!')
+
+    # write output to file
+    sf.write(output_file, np.squeeze(room_output), sample_rate)
+    print('\nOutput written to : {}'.format(output_file))
+
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser(
+        description='Show how to generate a dataset of random room '
+                    'configurations and how to apply the impulse responses '
+                    'of one of the rooms.')
+    parser.add_argument('-n', '--n_rooms', type=int, default=50,
+                        help='Number of rooms to generate')
+    parser.add_argument('-d', '--source_min_dist_mic', type=float,
+                        default=0.5,
+                        help='Minimum distance between each source and the'
+                             'microphone(s)')
+    parser.add_argument('-a', '--apply_room', type=str,
+                        help='Path to room dataset. If provided, a room will'
+                             'be randomly selected.')
+    parser.add_argument('-t', '--target_speech', type=str,
+                        default='examples/input_samples/cmu_arctic_us_aew_a0001.wav',
+                        help='Path to a target speech WAV file.')
+    parser.add_argument('-v', '--noise_dir', type=str,
+                        help='Path to a directory with noise WAV files. '
+                             'Default is to apply the room impulse response '
+                             'to WAV file(s) from `examples/input_samples`.')
+    parser.add_argument('-s', '--snr_db', type=float,
+                        default=5.,
+                        help='Desired signal-to-noise ratio resulting from '
+                             'simulation.')
+    parser.add_argument('-o', '--output_file', type=str,
+                        default='simulated_output.wav',
+                        help='Path of output WAV file from simulation.')
+    args = parser.parse_args()
+
+    if args.apply_room is not None:
+        apply_rir(room_dataset=args.apply_room,
+                  target_speech=args.target_speech,
+                  noise_dir=args.noise_dir,
+                  snr_db=args.snr_db,
+                  output_file=args.output_file)
+    else:
+        make_dataset(args.n_rooms, args.source_min_dist_mic)

examples/input_samples/README.md

@@ -11,9 +11,11 @@ corpus and are included for testing purposes.
 * cmu_arctic_us_aew_a0003.wav
 * cmu_arctic_us_axb_a0005.wav
 
-The following noise sample was taken from Google's [Speech Commands Dataset](https://research.googleblog.com/2017/08/launching-speech-commands-dataset.html)
+The following noise samples were taken from Google's [Speech Commands Dataset](https://research.googleblog.com/2017/08/launching-speech-commands-dataset.html)
 
 * doing_the_dishes.wav
+* exercise_bike.wav
+* running_tap.wav
 
 The following two samples are from unknown origin and were found online.
 

pyroomacoustics/__init__.py

@@ -104,6 +104,9 @@
 :py:obj:`pyroomacoustics.transform`
     Block frequency domain processing tools
 
+:py:obj:`pyroomacoustics.random`
+    Tools for random generation of rooms
+
 
 Utilities
 ---------
@@ -137,6 +140,7 @@
 from . import datasets
 from . import bss
 from . import denoise
+from . import random
 
 import warnings
 warnings.warn(

pyroomacoustics/datasets/__init__.py

@@ -141,3 +141,4 @@
 from .timit import Word, Sentence, TimitCorpus
 from .cmu_arctic import CMUArcticCorpus, CMUArcticSentence, cmu_arctic_speakers
 from .google_speech_commands import GoogleSpeechCommands, GoogleSample
+

pyroomacoustics/random/__init__.py

@@ -0,0 +1,14 @@
+"""
+Random
+======
+
+This sub-package provides classes and methods in order to randomly generate
+objects rooms of e.g. various shapes, microphone and source placement, and
+reverberation properties.
+
+"""
+
+from pyroomacoustics.random.microphone import *
+from pyroomacoustics.random.room import ShoeBoxRoomGenerator
+from pyroomacoustics.random.distribution import UniformDistribution, \
+    MultiUniformDistribution, DiscreteDistribution, MultiDiscreteDistribution