Added min and max freq attributes to preprocessor protocol

batdetect2/preprocess/__init__.py

@@ -144,12 +144,16 @@ class StandardPreprocessor(PreprocessorProtocol):
     audio_loader: AudioLoader
     spectrogram_builder: SpectrogramBuilder
     default_samplerate: int
+    max_freq: float
+    min_freq: float
 
     def __init__(
         self,
         audio_loader: AudioLoader,
         spectrogram_builder: SpectrogramBuilder,
         default_samplerate: int,
+        max_freq: float,
+        min_freq: float,
     ) -> None:
         """Initialize the StandardPreprocessor.
 
@@ -167,6 +171,8 @@ class StandardPreprocessor(PreprocessorProtocol):
         self.audio_loader = audio_loader
         self.spectrogram_builder = spectrogram_builder
         self.default_samplerate = default_samplerate
+        self.max_freq = max_freq
+        self.min_freq = min_freq
 
     def load_file_audio(
         self,
@@ -429,8 +435,14 @@ def build_preprocessor(
         if config.audio.resample
         else TARGET_SAMPLERATE_HZ
     )
+
+    min_freq = config.spectrogram.frequencies.min_freq
+    max_freq = config.spectrogram.frequencies.max_freq
+
     return StandardPreprocessor(
         audio_loader=build_audio_loader(config.audio),
         spectrogram_builder=build_spectrogram_builder(config.spectrogram),
         default_samplerate=default_samplerate,
+        min_freq=min_freq,
+        max_freq=max_freq,
     )

batdetect2/preprocess/audio.py

@@ -286,7 +286,8 @@ def load_recording_audio(
     """Load and preprocess the entire audio content of a recording using config.
 
     Creates a `soundevent.data.Clip` spanning the full duration of the
-    recording and then delegates the loading and processing to `load_clip_audio`.
+    recording and then delegates the loading and processing to
+    `load_clip_audio`.
 
     Parameters
     ----------
@@ -636,7 +637,11 @@ def resample_audio_fourier(
         If `num` is negative.
     """
     ratio = sr_new / sr_orig
-    return resample(array, int(array.shape[axis] * ratio), axis=axis)  # type: ignore
+    return resample(  # type: ignore
+        array,
+        int(array.shape[axis] * ratio),
+        axis=axis,
+    )
 
 
 def convert_to_xr(
@@ -649,8 +654,8 @@ def convert_to_xr(
     Parameters
     ----------
     wav : np.ndarray
-        The input waveform array. Expected to be 1D or 2D (with the first axis as
-        the channel dimension).
+        The input waveform array. Expected to be 1D or 2D (with the first
+        axis as the channel dimension).
     samplerate : int
         The sample rate in Hz.
     dtype : DTypeLike, default=np.float32
@@ -673,7 +678,8 @@ def convert_to_xr(
 
     if wav.ndim != 1:
         raise ValueError(
-            "Audio must be 1D array or 2D channel where the first axis is the channel dimension"
+            "Audio must be 1D array or 2D channel where the first "
+            "axis is the channel dimension"
         )
 
     if wav.size == 0:

batdetect2/preprocess/spectrogram.py

@@ -21,8 +21,6 @@ The core computation is performed by `compute_spectrogram`.
 
 from typing import Literal, Optional, Union
 
-import librosa
-import librosa.core.spectrum
 import numpy as np
 import xarray as xr
 from numpy.typing import DTypeLike
@@ -147,7 +145,8 @@ class SpectrogramConfig(BaseConfig):
     """Unified configuration for spectrogram generation pipeline.
 
     Aggregates settings for all steps involved in converting a preprocessed
-    audio waveform into a final spectrogram representation suitable for model input.
+    audio waveform into a final spectrogram representation suitable for model
+    input.
 
     Attributes
     ----------
@@ -298,7 +297,8 @@ def compute_spectrogram(
     1. Compute STFT magnitude (`stft`).
     2. Crop frequency axis (`crop_spectrogram_frequencies`).
     3. Apply PCEN if configured (`apply_pcen`).
-    4. Apply final amplitude scaling (dB, power, amplitude) (`scale_spectrogram`).
+    4. Apply final amplitude scaling (dB, power, amplitude)
+       (`scale_spectrogram`).
     5. Apply spectral mean subtraction denoising if enabled.
     6. Resize dimensions if specified (`resize_spectrogram`).
     7. Apply final peak normalization if enabled.
@@ -324,9 +324,6 @@ def compute_spectrogram(
     ------
     ValueError
         If `wav` lacks necessary 'time' coordinates or dimensions.
-    Exception
-        Can re-raise exceptions from underlying libraries (e.g., librosa, numpy)
-        if invalid parameters or data are encountered.
     """
     config = config or SpectrogramConfig()
 
@@ -335,7 +332,6 @@ def compute_spectrogram(
         window_duration=config.stft.window_duration,
         window_overlap=config.stft.window_overlap,
         window_fn=config.stft.window_fn,
-        dtype=dtype,
     )
 
     spec = crop_spectrogram_frequencies(
@@ -410,7 +406,6 @@ def stft(
     window_duration: float,
     window_overlap: float,
     window_fn: str = "hann",
-    dtype: DTypeLike = np.float32,  # type: ignore
 ) -> xr.DataArray:
     """Compute the Short-Time Fourier Transform (STFT) magnitude spectrogram.
 
@@ -425,11 +420,9 @@ def stft(
     window_duration : float
         Duration of the STFT window in seconds.
     window_overlap : float
-        Fractional overlap between consecutive windows [0, 1).
+        Fractional overlap between consecutive windows.
     window_fn : str, default="hann"
         Name of the window function (e.g., "hann", "hamming").
-    dtype : DTypeLike, default=np.float32
-        Target data type for the spectrogram array.
 
     Returns
     -------
@@ -442,55 +435,13 @@ def stft(
     ValueError
         If sample rate cannot be determined from `wave` coordinates.
     """
-    start_time, end_time = arrays.get_dim_range(wave, dim="time")
-    step = arrays.get_dim_step(wave, dim="time")
-    sampling_rate = 1 / step
-
-    nfft = int(window_duration * sampling_rate)
-    noverlap = int(window_overlap * nfft)
-    hop_len = nfft - noverlap
-    hop_duration = hop_len / sampling_rate
-
-    spec, _ = librosa.core.spectrum._spectrogram(
-        y=wave.data.astype(dtype),
-        power=1,
-        n_fft=nfft,
-        hop_length=nfft - noverlap,
-        center=False,
-        window=window_fn,
-    )
-
-    return xr.DataArray(
-        data=spec.astype(dtype),
-        dims=["frequency", "time"],
-        coords={
-            "frequency": arrays.create_frequency_dim_from_array(
-                np.linspace(
-                    0,
-                    sampling_rate / 2,
-                    spec.shape[0],
-                    endpoint=False,
-                    dtype=dtype,
-                ),
-                step=sampling_rate / nfft,
-            ),
-            "time": arrays.create_time_dim_from_array(
-                np.linspace(
-                    start_time,
-                    end_time - (window_duration - hop_duration),
-                    spec.shape[1],
-                    endpoint=False,
-                    dtype=dtype,
-                ),
-                step=hop_duration,
-            ),
-        },
-        attrs={
-            **wave.attrs,
-            "original_samplerate": sampling_rate,
-            "nfft": nfft,
-            "noverlap": noverlap,
-        },
+    return audio.compute_spectrogram(
+        wave,
+        window_size=window_duration,
+        hop_size=(1 - window_overlap) * window_duration,
+        window_type=window_fn,
+        scale="amplitude",
+        sort_dims=False,
     )
 
 
@@ -592,8 +543,10 @@ def apply_pcen(
       verified against the specific `soundevent.audio.pcen` implementation
       details.
     """
-    samplerate = spec.attrs["original_samplerate"]
-    hop_length = spec.attrs["nfft"] - spec.attrs["noverlap"]
+    samplerate = spec.attrs["samplerate"]
+    hop_size = spec.attrs["hop_size"]
+
+    hop_length = int(hop_size * samplerate)
     t_frames = time_constant * samplerate / (float(hop_length) * 10)
     smoothing_constant = (np.sqrt(1 + 4 * t_frames**2) - 1) / (2 * t_frames**2)
     return audio.pcen(

batdetect2/preprocess/types.py

@@ -168,6 +168,10 @@ class PreprocessorProtocol(Protocol):
     loading or spectrogram computation from a waveform.
     """
 
+    max_freq: float
+
+    min_freq: float
+
     def preprocess_file(
         self,
         path: data.PathLike,