"guard_zone_width": 0.5,
},
"output_file": "experiment_speed_11_17_results.json",
}
# Placeholder to hold all the results
sim_results = {"config": config, "room_info": [], "data": []}
if __name__ == "__main__":
np.random.seed(config["seed"])
min_sources = np.min(config["n_sources_list"])
max_sources = np.max(config["n_sources_list"])
audio_files = sampling(config["n_repeat"], min_sources, config["samples_metadata"])
ref_mic = config["separation_params"]["ref_mic"]
for room_id, file_list in enumerate(audio_files):
audio = wav_read_center(file_list)
# Get a random room and simulate
room, rt60 = random_room_builder(audio, max_sources, **config["room_params"])
premix = room.simulate(return_premix=True)
sim_results["room_info"].append(
{
"dim": room.shoebox_dim.tolist(),
"rt60": rt60,
"id": room_id,
"samples": file_list,
def exp1_gen_args(config):
np.random.seed(config["seed"])
# sub-seeds
sub_seeds = []
for r in range(config["repeat"]):
sub_seeds.append(int(np.random.randint(2 ** 32)))
# maximum number of sources and microphones in the simulation
if config["n_targets"] == "determined":
n_sources = np.max(config["n_mics"])
else:
n_sources = np.max(config["n_interferers"]) + np.max(config["n_targets"])
n_mics = np.max(config["n_mics"])
room_file = Path(config["room_cache_file"])
regenerate_rooms = True # assume we need to start over
# now check the content of the cache if it exists
if room_file.exists():
with open(room_file, "r") as f:
room_cache = json.load(f)
# if the content of the config file changes, we should generate again
if not (
room_cache["seed"] != config["seed"]
or room_cache["room_params"] != config["room_params"]
or len(room_cache["rooms"]) != config["repeat"]
):
regenerate_rooms = False
if not regenerate_rooms:
# use the content of the cache
print("Use the rooms in the cache")
rooms = room_cache["rooms"]
rt60s = room_cache["rt60s"]
else:
# generate all the rooms
print("Generate the rooms and measure rt60")
# choose all the files in advance
gen_files_seed = int(np.random.randint(2 ** 32, dtype=np.uint32))
all_wav_files = sampling(
config["repeat"],
n_sources,
config["samples_list"],
gender_balanced=True,
seed=gen_files_seed,
)
# generates all the rooms in advance too
rooms = []
rt60s = []
for i in range(config["repeat"]):
room_params, rt60 = random_room_definition(
n_sources, n_mics, seed=i, **config["room_params"]
)
# add the speech signal files to use
room_params["wav"] = all_wav_files[r]
rt60s.append(rt60)
rooms.append(room_params)
# cache the rooms
with open(room_file, "w") as f:
json.dump(
{
"seed": config["seed"],
"room_params": config["room_params"],
"rooms": rooms,
"rt60s": rt60s,
},
f,
)
print("Done generating the rooms")
# now generates all the other argument combinations
args = []
for sinr in config["sinr"]:
for n_interf in config["n_interferers"]:
for n_mics in config["n_mics"]:
# in the determined case, n_mics == n_targets
if config["n_targets"] == "determined":
n_targets_lst = [n_mics]
else:
n_targets_lst = config["n_targets"]
for n_targets in n_targets_lst:
for dist_ratio in config["dist_crit_ratio"]:
for r in range(config["repeat"]):
# bundle all the room parameters for the simulation
room_params = rooms[r]
args.append(
(
sinr,
n_targets,
n_interf,
n_mics,
dist_ratio,
room_params,
sub_seeds[r],
)
)
return args
)
target_locs = semi_circle_layout(
[4.1, 3.755, 1.1], np.pi / 2, 2.0, n_sources_target, rot=0.743 * np.pi
)
# interferer_locs = grid_layout([3., 5.5], n_sources - n_sources_target, offset=[6.5, 1., 1.7])
interferer_locs = random_layout(
[3.0, 5.5, 1.5], n_sources - n_sources_target, offset=[6.5, 1.0, 0.5],
)
source_locs = np.concatenate((target_locs, interferer_locs), axis=1)
# Prepare the signals
wav_files = sampling(
1,
n_sources,
f"{samples_dir}/metadata.json",
gender_balanced=True,
seed=args.seed,
)[0]
signals = wav_read_center(wav_files, seed=123)
# Create the room itself
room = pra.ShoeBox(room_dim, fs=fs, absorption=absorption, max_order=max_order)
# Place a source of white noise playing for 5 s
for sig, loc in zip(signals, source_locs.T):
room.add_source(loc, signal=sig)
# Place the microphone array
room.add_microphone_array(pra.MicrophoneArray(mic_locs, fs=room.fs))
def exp2_gen_args(config):
# infer a few arguments
room_dim = config["room"]["room_kwargs"]["p"]
mic_array_center = np.array(config["room"]["mic_array_location_m"])
mic_array = mic_array_center[None, :] + np.array(
config["room"]["mic_array_geometry_m"]
)
mic_array = mic_array.T
critical_distance = config["room"]["critical_distance_m"]
# master seed
np.random.seed(config["seed"])
# choose all the files in advance
gen_files_seed = int(np.random.randint(2 ** 32, dtype=np.uint32))
all_wav_files = sampling(
config["repeat"],
np.max(config["n_targets"]) + np.max(config["n_interferers"]),
config["samples_list"],
gender_balanced=True,
seed=gen_files_seed,
)
# sub-seeds
sub_seeds = []
for r in range(config["repeat"]):
sub_seeds.append(int(np.random.randint(2 ** 32)))
# create all distinct interferers locations
interferers_locs = []
for n in range(config["repeat"]):
interferers_locs.append(
random_locations(
np.max(config["n_interferers"]),
room_dim,
mic_array_center,
min_dist=critical_distance,
).tolist()
)
# create all distinct target locations
target_locs = {}
for r in range(config["repeat"]):
# pick rotation
random_rot = np.random.rand() * 2 * np.pi
target_locs[r] = {}
for n in config["n_targets"]:
target_locs[r][n] = {}
for dist_ratio in config["dist_crit_ratio"]:
dist_mic_target = dist_ratio * critical_distance
target_locs[r][n][dist_ratio] = choose_target_locations(
n, mic_array_center, dist_mic_target
).tolist()
args = []
for sinr in config["sinr"]:
for n_targets in config["n_targets"]:
for n_interf in config["n_interferers"]:
for n_mics in config["n_mics"]:
for dist_ratio in config["dist_crit_ratio"]:
for r in range(config["repeat"]):
assert (
n_mics == mic_array.shape[1]
), "n_mics and number of microphones used should match"
# bundle all the room parameters for the simulation
room_params = {
"room_kwargs": config["room"]["room_kwargs"],
"mic_array": mic_array.tolist(),
"sources": np.concatenate(
(
target_locs[r][n_targets][dist_ratio],
interferers_locs[r],
),
axis=1,
).tolist(),
"wav": all_wav_files[r][: n_targets + n_interf],
}
args.append(
(
sinr,
n_targets,
n_interf,
n_mics,
dist_ratio,
room_params,
sub_seeds[r],
)
)
return args
critical_distance = config["room"]["critical_distance_m"]
# all source locations
target_locs = choose_target_locations(n_sources_target, mic_array_center,
dist_ratio * critical_distance)
interferers_locs = random_locations(args.interf,
room_dim,
mic_array_center,
min_dist=critical_distance)
source_locs = np.concatenate((target_locs, interferers_locs), axis=1)
# Prepare the signals
wav_files = sampling(
1,
n_sources,
f"{samples_dir}/metadata.json",
gender_balanced=True,
seed=np.random.randint(2**32),
)[0]
signals = wav_read_center(wav_files, seed=123)
# Create the room itself
room = pra.ShoeBox(**config["room"]["room_kwargs"])
# Place a source of white noise playing for 5 s
for sig, loc in zip(signals, source_locs.T):
room.add_source(loc, signal=sig)
# Place the microphone array
room.add_microphone_array(pra.MicrophoneArray(mic_locs, fs=room.fs))