From 0b72e7558bbcd0593fc742c0a339e11f16d7b640 Mon Sep 17 00:00:00 2001 From: Eric B Date: Wed, 26 Jun 2019 00:03:43 +0200 Subject: [PATCH 1/6] Add room generator. --- CHANGELOG.rst | 1 + .../pyroomacoustics.datasets.distribution.rst | 7 + docs/pyroomacoustics.datasets.room.rst | 10 + docs/pyroomacoustics.datasets.rst | 2 + examples/generate_room_dataset.py | 191 +++++++ examples/input_samples/README.md | 4 +- examples/input_samples/exercise_bike.wav | Bin 0 -> 1960248 bytes examples/input_samples/running_tap.wav | Bin 0 -> 1957100 bytes pyroomacoustics/datasets/__init__.py | 3 + pyroomacoustics/datasets/distribution.py | 140 +++++ pyroomacoustics/datasets/room.py | 478 ++++++++++++++++++ pyroomacoustics/utilities.py | 27 + 12 files changed, 862 insertions(+), 1 deletion(-) create mode 100644 docs/pyroomacoustics.datasets.distribution.rst create mode 100644 docs/pyroomacoustics.datasets.room.rst create mode 100644 examples/generate_room_dataset.py create mode 100644 examples/input_samples/exercise_bike.wav create mode 100644 examples/input_samples/running_tap.wav create mode 100644 pyroomacoustics/datasets/distribution.py create mode 100644 pyroomacoustics/datasets/room.py diff --git a/CHANGELOG.rst b/CHANGELOG.rst index ff1e45f5..7808bf6b 100644 --- a/CHANGELOG.rst +++ b/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 +- Room generator in ``pyroomacoustics.datasets.room`` Changed diff --git a/docs/pyroomacoustics.datasets.distribution.rst b/docs/pyroomacoustics.datasets.distribution.rst new file mode 100644 index 00000000..7aa2611b --- /dev/null +++ b/docs/pyroomacoustics.datasets.distribution.rst @@ -0,0 +1,7 @@ +Distribution Utilities +============================================ + +.. automodule:: pyroomacoustics.datasets.distribution + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/pyroomacoustics.datasets.room.rst b/docs/pyroomacoustics.datasets.room.rst new file mode 100644 index 00000000..68b489da --- /dev/null +++ b/docs/pyroomacoustics.datasets.room.rst @@ -0,0 +1,10 @@ +Room Generation Utilities +==================================== + +See ``examples/generate_room_dataset.py`` for an example on generating +a dataset of randomly sampled room. + +.. automodule:: pyroomacoustics.datasets.room + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/pyroomacoustics.datasets.rst b/docs/pyroomacoustics.datasets.rst index 6c463630..24993a8a 100644 --- a/docs/pyroomacoustics.datasets.rst +++ b/docs/pyroomacoustics.datasets.rst @@ -25,4 +25,6 @@ Tools and Helpers pyroomacoustics.datasets.base pyroomacoustics.datasets.utils + pyroomacoustics.datasets.distribution + pyroomacoustics.datasets.room diff --git a/examples/generate_room_dataset.py b/examples/generate_room_dataset.py new file mode 100644 index 00000000..6b8536b4 --- /dev/null +++ b/examples/generate_room_dataset.py @@ -0,0 +1,191 @@ +import numpy as np +import json +import click +import os +from glob import glob +from pprint import pprint +import random +import soundfile as sf + +from pyroomacoustics.utilities import rms, sample_audio +from pyroomacoustics.datasets.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 +``` + +""" + +example_noise_files = [ + 'examples/input_samples/doing_the_dishes.wav', + 'examples/input_samples/exercise_bike.wav', + 'examples/input_samples/running_tap.wav', +] + + +@click.group() +def main(): + pass + + +@main.command('make_dataset') +@click.option('--n_rooms', type=int, default=50) +def make_dataset(n_rooms): + """ + + Generate a dataset of room impulse responses. A new folder will be created + with the name `pra_room_dataset_` with the following structure: + + ``` + pra_room_dataset_/ + room_metadata.json + data/ + room_.npz + room_.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. + """ + + room_generator = ShoeBoxRoomGenerator() + room_generator.create_dataset(n_rooms) + + +@main.command('apply_rir') +@click.option('--room_dataset', type=str, default=None) +@click.option('--target_speech', type=str, + default='examples/input_samples/cmu_arctic_us_aew_a0001.wav') +@click.option('--noise_dir', type=str, default=None) +@click.option('--snr_db', type=float, default=5.) +@click.option('--output_file', type=str, default='simulated_output.wav') +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__': + main() diff --git a/examples/input_samples/README.md b/examples/input_samples/README.md index d84e7ea4..30dd21ee 100644 --- a/examples/input_samples/README.md +++ b/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. diff --git a/examples/input_samples/exercise_bike.wav b/examples/input_samples/exercise_bike.wav new file mode 100644 index 0000000000000000000000000000000000000000..6ed588cc955b492de7b14bf86af234dc23725757 GIT binary patch literal 1960248 zcmeFZ1-n+&_WnP{oa@<}?hZ)-X{5W67EroVK|)Fpq!EymlrANtkwzq>8ziN>m6F)c znsbcbeg3cD{{p_}T<5wD3Y+IyYpyZw@wxA@8r7;%qwwR*)URo#D%bJXc82=q! z0sm(Rk)dSNseQlpyB=l6Kls1@{NEM$zbo*6SK$Ay!2h2s5Rj#K6rW=8n`7ycw)A6l zx%QVQJkFlhK>wk-G)y|tkNTDl&>#9lbNR#itET5QT2vdF<@A$Y)jT>-hVe&y$B_BO z9xxwq33@RpQ$UuFWhv=%Gu2^eIduF42|R#QX_n!8>NSy zCCqj 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