Computing a LTAS with the Public API [1]#
As always in the Public API, the first step is to build the project.
An Instrument can be provided to the Project for the WAV data to be converted in pressure units. This will lead the resulting spectra to be expressed in dB SPL (rather than in dB FS):
from pathlib import Path
audio_folder = Path(r"_static/sample_audio/timestamped")
from osekit.public.project import Project
from osekit.core.instrument import Instrument
project = Project(
folder=audio_folder,
strptime_format="%y%m%d_%H%M%S",
instrument=Instrument(end_to_end_db=150.0),
)
project.build()
2026-03-25 15:41:48,116
Building the project...
2026-03-25 15:41:48,117
Analyzing original audio files...
2026-03-25 15:41:48,127
Organizing project folder...
2026-03-25 15:41:48,131
Build done!
The Public API Project is now analyzed and organized:
print(f"{' DATASET ':#^60}")
print(f"{'Begin:':<30}{str(project.origin_dataset.begin):>30}")
print(f"{'End:':<30}{str(project.origin_dataset.end):>30}")
print(f"{'Sample rate:':<30}{str(project.origin_dataset.sample_rate):>30}\n")
print(f"{' ORIGINAL FILES ':#^60}")
import pandas as pd
pd.DataFrame(
[
{
"Name": f.path.name,
"Begin": f.begin,
"End": f.end,
"Sample Rate": f.sample_rate,
}
for f in project.origin_files
],
).set_index("Name")
######################### DATASET ##########################
Begin: 2022-09-25 22:34:50
End: 2022-09-25 22:36:50
Sample rate: 48000
###################### ORIGINAL FILES ######################
| Begin | End | Sample Rate | |
|---|---|---|---|
| Name | |||
| sample_220925_223450.wav | 2022-09-25 22:34:50 | 2022-09-25 22:35:00 | 48000 |
| sample_220925_223500.wav | 2022-09-25 22:35:00 | 2022-09-25 22:35:10 | 48000 |
| sample_220925_223510.wav | 2022-09-25 22:35:10 | 2022-09-25 22:35:20 | 48000 |
| sample_220925_223520.wav | 2022-09-25 22:35:20 | 2022-09-25 22:35:30 | 48000 |
| sample_220925_223530.wav | 2022-09-25 22:35:30 | 2022-09-25 22:35:40 | 48000 |
| sample_220925_223600.wav | 2022-09-25 22:36:00 | 2022-09-25 22:36:10 | 48000 |
| sample_220925_223610.wav | 2022-09-25 22:36:10 | 2022-09-25 22:36:20 | 48000 |
| sample_220925_223620.wav | 2022-09-25 22:36:20 | 2022-09-25 22:36:30 | 48000 |
| sample_220925_223630.wav | 2022-09-25 22:36:30 | 2022-09-25 22:36:40 | 48000 |
| sample_220925_223640.wav | 2022-09-25 22:36:40 | 2022-09-25 22:36:50 | 48000 |
Since we will run a spectral transform, we need to define the FFT parameters:
from scipy.signal import ShortTimeFFT
from scipy.signal.windows import hamming
sample_rate = 24_000
sft = ShortTimeFFT(
win=hamming(1024),
hop=128, # This will be forced to len(win) if we compute a LTAS
fs=sample_rate,
)
To run transforms in the Public API, use the Transform class:
from osekit.utils.audio import Normalization
from osekit.public.transform import Transform, OutputType
transform = Transform(
output_type=OutputType.SPECTROGRAM
| OutputType.SPECTRUM, # we want to export both the spectrogram and the sx spectrum
nb_ltas_time_bins=3000, # This will turn the regular spectrum computation in a LTAS
sample_rate=sample_rate,
normalization=Normalization.DC_REJECT, # Removes the DC component
fft=sft,
v_lim=(0.0, 150.0), # Boundaries of the spectrograms
colormap="viridis", # Default value
name="LTAS",
)
Running the transform will compute the LTAS and save the output files to disk.
project.run(transform=transform)
2026-03-25 15:41:48,182
Creating the audio data...
2026-03-25 15:41:48,189
Running transform...
2026-03-25 15:41:48,189
Computing and writing spectrum matrices and spectrograms...
2026-03-25 15:41:48,996
Transform done!
As for regular spectrum transforms, the output LTAS is stored in a SpectroDataset named after transform.name:
pd.DataFrame(
[
{
"Exported file": list(sd.files)[0].path.name,
"Begin": sd.begin,
"End": sd.end,
"Sample Rate": sd.fft.fs,
}
for sd in project.get_output(transform.name).data
],
).set_index("Exported file")
| Begin | End | Sample Rate | |
|---|---|---|---|
| Exported file | |||
| 2022_09_25_22_34_50_000000.npz | 2022-09-25 22:34:50 | 2022-09-25 22:36:50 | 24000 |
# Reset the project to get all files back to place.
project.reset()