Computing multiple spectrograms with the Public API [1]#
As always in the Public API, the first step is to build the dataset.
An Instrument can be provided to the Dataset 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")
from osekit.public_api.dataset import Dataset
from osekit.core_api.instrument import Instrument
dataset = Dataset(
folder=audio_folder,
strptime_format="%y%m%d_%H%M%S",
instrument=Instrument(end_to_end_db=150.0),
)
dataset.build()
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Building the dataset...
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Analyzing original audio files...
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Organizing dataset folder...
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Build done!
The Public API Dataset is now analyzed and organized:
print(f"{' DATASET ':#^60}")
print(f"{'Begin:':<30}{str(dataset.origin_dataset.begin):>30}")
print(f"{'End:':<30}{str(dataset.origin_dataset.end):>30}")
print(f"{'Sample rate:':<30}{str(dataset.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 dataset.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 analysis, 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, fs=sample_rate)
To run analyses in the Public API, use the Analysis class:
from osekit.public_api.analysis import Analysis, AnalysisType
from pandas import Timestamp, Timedelta
analysis = Analysis(
analysis_type=AnalysisType.MATRIX
| AnalysisType.SPECTROGRAM
| AnalysisType.WELCH, # we want to export these three spectrum outputs
begin=Timestamp("2022-09-25 22:35:15"),
end=Timestamp("2022-09-25 22:36:25"),
data_duration=Timedelta(seconds=5),
sample_rate=sample_rate,
fft=sft,
v_lim=(0.0, 150.0), # Boundaries of the spectrograms
colormap="viridis", # Default value
name="all_spectral_output",
)
The Core API can still be used on top of the Public API.
We will filter out the empty data this way:
# Returns a Core API AudioDataset that matches the analysis
audio_dataset = dataset.get_analysis_audiodataset(analysis=analysis)
# Filter the returned AudioDataset
audio_dataset.data = [ad for ad in audio_dataset.data if not ad.is_empty]
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Creating the audio data...
We can also glance at the spectrogram results with the Core API:
import matplotlib.pyplot as plt
analysis_spectro_dataset = dataset.get_analysis_spectrodataset(
analysis=analysis,
audio_dataset=audio_dataset, # So that the filtered SpectroDataset is returned
)
analysis_spectro_dataset.data[1].plot()
plt.show()
Running the analysis while specifying the filtered audio_dataset will skip the empty AudioData (and thus the empty SpectroData).
dataset.run_analysis(analysis=analysis, audio_dataset=audio_dataset)
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Running analysis...
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Computing and writing spectrum matrices and spectrograms...
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Computing and writing welches...
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Analysis done!
All the new files from the analysis are stored in a SpectroDataset named after analysis.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 dataset.get_dataset(analysis.name).data
],
).set_index("Exported file")
| Begin | End | Sample Rate | |
|---|---|---|---|
| Exported file | |||
| 2022_09_25_22_35_15_000000.npz | 2022-09-25 22:35:15 | 2022-09-25 22:35:20 | 24000 |
| 2022_09_25_22_35_20_000000.npz | 2022-09-25 22:35:20 | 2022-09-25 22:35:25 | 24000 |
| 2022_09_25_22_35_25_000000.npz | 2022-09-25 22:35:25 | 2022-09-25 22:35:30 | 24000 |
| 2022_09_25_22_35_30_000000.npz | 2022-09-25 22:35:30 | 2022-09-25 22:35:35 | 24000 |
| 2022_09_25_22_35_35_000000.npz | 2022-09-25 22:35:35 | 2022-09-25 22:35:40 | 24000 |
| 2022_09_25_22_36_00_000000.npz | 2022-09-25 22:36:00 | 2022-09-25 22:36:05 | 24000 |
| 2022_09_25_22_36_05_000000.npz | 2022-09-25 22:36:05 | 2022-09-25 22:36:10 | 24000 |
| 2022_09_25_22_36_10_000000.npz | 2022-09-25 22:36:10 | 2022-09-25 22:36:15 | 24000 |
| 2022_09_25_22_36_15_000000.npz | 2022-09-25 22:36:15 | 2022-09-25 22:36:20 | 24000 |
| 2022_09_25_22_36_20_000000.npz | 2022-09-25 22:36:20 | 2022-09-25 22:36:25 | 24000 |