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()

	2026-11-12 08:46:56,490
Building the project...

	2026-11-12 08:46:56,491
Analyzing original audio files...

	2026-11-12 08:46:56,498
Organizing project folder...

	2026-11-12 08:46:56,500
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.STFT_MATRIX,  # we want to export both the spectrogram and the sx STFT matrix
    nb_ltas_time_bins=3000,  # This will turn the regular STFT matrix 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)

	2026-11-12 08:46:56,530
Creating the audio data...

	2026-11-12 08:46:56,533
Running transform...

	2026-11-12 08:46:56,533
Computing and writing STFT matrices and spectrograms...

	2026-11-12 08:46:57,137
Transform done!

As for regular STFT 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()