def process_file(wav_file):
sound_handler = SoundHandler(
WAV_FILES_LOCATION,
wav_file["name"],
wav_file["wav_bits"],
wav_file["sample_rate"],
wav_file["n_channels"])
segment_size = int(SEGMENT_DURATION * wav_file["sample_rate"])
feature_generator = FeatureGenerator(
sound_handler, wav_file["timestamp"],
wav_file["sample_rate"], CALIBRATION_FACTOR,
segment_size, WINDOW_SIZE, WINDOW_OVERLAP, NFFT)
results = feature_generator.generate()
# extract sound's id from sound file name
# (sound's name follow convention described in test/resources/README.md)
sound_id = wav_file["name"][:-4]
resultsHandler = ResultsHandler(
sound_id,
RESULTS_DESTINATION,
segment_size,
WINDOW_SIZE,
WINDOW_OVERLAP,
NFFT
)
resultsHandler.write(results)
def process_file(wav_config):
print("Start processing {}".format(wav_config["name"]))
tStart = time.time()
sound_handler = SoundHandler(
WAV_FILES_LOCATION,
wav_config["name"],
wav_config["wav_bits"],
wav_config["sample_rate"],
wav_config["n_channels"])
segment_size = int(SEGMENT_DURATION * wav_config["sample_rate"])
feature_generator = FeatureGenerator(
sound_handler, wav_config["timestamp"],
wav_config["sample_rate"], CALIBRATION_FACTOR,
segment_size, WINDOW_SIZE, WINDOW_OVERLAP, NFFT)
results = feature_generator.generate()
# extract sound's id from sound file name
# (sound's name follow convention described in test/resources/README.md)
sound_id = wav_config["name"][:-4]
resultsHandler = ResultsHandler(
sound_id,
RESULTS_DESTINATION,
segment_size,
WINDOW_SIZE,
WINDOW_OVERLAP,
NFFT
)
resultsHandler.write(results)
duration = time.time() - tStart
print("Finished processing {} in {}".format(wav_config["name"], duration))
return duration
def process_file(task_config):
print("Start processing {}".format(task_config["name"]))
tStart = time()
sound_handler = SoundHandler(task_config["location"], task_config["name"],
task_config["wav_bits"],
task_config["sample_rate"],
task_config["n_channels"])
segment_size = int(task_config["segment_duration"] *
task_config["sample_rate"])
feature_generator = FeatureGenerator(
sound_handler, task_config["timestamp"], task_config["sample_rate"],
task_config["calibration_factor"], segment_size,
task_config["window_size"], task_config["window_overlap"],
task_config["nfft"])
results = feature_generator.generate()
# extract sound's id from sound file name
# (sound's name follow convention described in test/resources/README.md)
sound_id = task_config["name"][:-4]
resultsHandler = ResultsHandler(sound_id,
task_config["results_destination"],
segment_size, task_config["window_size"],
task_config["window_overlap"],
task_config["nfft"])
resultsHandler.write(results)
duration = time() - tStart
print("Finished processing {} in {}".format(task_config["name"], duration))
return duration
WAV_FILES = [{
"name":
file_metadata[0],
"timestamp":
parse(file_metadata[9] + " " + file_metadata[10] + " UTC"),
"sample_rate":
32768.0,
"wav_bits":
16,
"n_channels":
1
} for file_metadata in pd.read_csv(METADATA_FILE_PATH, delimiter=";").values]
for wav_file in WAV_FILES[:N_FILES]:
sound_handler = SoundHandler(WAV_FILES_LOCATION, wav_file["name"],
wav_file["wav_bits"], wav_file["sample_rate"],
wav_file["n_channels"])
segment_size = int(SEGMENT_DURATION * wav_file["sample_rate"])
feature_generator = FeatureGenerator(sound_handler, wav_file["timestamp"],
wav_file["sample_rate"],
CALIBRATION_FACTOR, segment_size,
WINDOW_SIZE, WINDOW_OVERLAP, NFFT)
results = feature_generator.generate()
# extract sound's id from sound file name
# (sound's name follow convention described in test/resources/README.md)
sound_id = wav_file["name"][:-4]