Use soundevent.to_db

batdetect2/preprocess/spectrogram.py

@@ -19,7 +19,7 @@ reproducible spectrogram generation consistent between training and inference.
 The core computation is performed by `compute_spectrogram`.
 """
 
-from typing import Literal, Optional, Union
+from typing import Callable, Literal, Optional, Union
 
 import numpy as np
 import xarray as xr
@@ -327,6 +327,7 @@ def compute_spectrogram(
     """
     config = config or SpectrogramConfig()
 
+    with xr.set_options(keep_attrs=True):
         spec = stft(
             wav,
             window_duration=config.stft.window_duration,
@@ -477,7 +478,7 @@ def scale_spectrogram(
     """Apply final amplitude scaling/representation to the spectrogram.
 
     Converts the input magnitude spectrogram based on the `scale` type:
-    - "dB": Applies logarithmic scaling `log1p(C * S)`.
+    - "dB": Applies logarithmic scaling `log10(S)`.
     - "power": Squares the magnitude values `S^2`.
     - "amplitude": Returns the input magnitude values `S` unchanged.
 
@@ -496,7 +497,7 @@ def scale_spectrogram(
         Spectrogram with the specified amplitude scaling applied.
     """
     if scale == "dB":
-        return scale_log(spec, dtype=dtype)
+        return arrays.to_db(spec).astype(dtype)
 
     if scale == "power":
         return spec**2
@@ -561,12 +562,13 @@ def apply_pcen(
 def scale_log(
     spec: xr.DataArray,
     dtype: DTypeLike = np.float32,  # type: ignore
+    ref: Union[float, Callable] = np.max,
+    amin: float = 1e-10,
+    top_db: Optional[float] = 80.0,
 ) -> xr.DataArray:
     """Apply logarithmic scaling to a magnitude spectrogram.
 
-    Calculates `log(1 + C * S)`, where S is the input magnitude spectrogram
-    and C is a scaling factor derived from the original STFT parameters
-    (sample rate, N-FFT, window function) stored in `spec.attrs`.
+    Calculates `log10(S)`, where S is the input magnitude spectrogram.
 
     Parameters
     ----------
@@ -587,12 +589,29 @@ def scale_log(
         If required attributes are missing from `spec.attrs`.
     ValueError
         If attributes are non-numeric or window function is invalid.
+
+
+    Notes
+    -----
+    Implementation mainly taken from librosa `power_to_db` function
     """
-    samplerate = spec.attrs["original_samplerate"]
-    nfft = spec.attrs["nfft"]
-    log_scaling = 2 / (samplerate * (np.abs(np.hanning(nfft)) ** 2).sum())
+
+    if callable(ref):
+        ref_value = ref(spec)
+    else:
+        ref_value = np.abs(ref)
+
+    log_spec = 10.0 * np.log10(np.maximum(amin, spec)) - np.log10(
+        np.maximum(amin, ref_value)
+    )
+
+    if top_db is not None:
+        if top_db < 0:
+            raise ValueError("top_db must be non-negative")
+        log_spec = np.maximum(log_spec, log_spec.max() - top_db)
+
     return xr.DataArray(
-        data=np.log1p(log_scaling * spec).astype(dtype),
+        data=log_spec.astype(dtype),
         dims=spec.dims,
         coords=spec.coords,
         attrs=spec.attrs,