Audio changepoints#

For audio data or high-sample-rate periodic signals (loaded as .wav file).

Four methods are available, drawn from two libraries.

VocalPy methods#

Reference: VocalPy documentation

Mean-squared energy (meansquared): Computes a smoothed energy envelope via mean-squared amplitude, then thresholds to find vocal segments. Simple and fast.

AVA (ava): The segmentation method from the Animal Vocalization Analysis pipeline. Uses a spectrogram-based approach with multiple threshold levels.

VocalSeg methods#

Reference: VocalSeg (Sainburg et al., 2020)

Dynamic thresholding (vocalseg): Adaptive threshold segmentation that adjusts to local spectral energy. Good for signals with varying background noise.

Continuity filtering (continuity): Extends dynamic thresholding with temporal continuity constraints to merge fragmented detections.

Usage#

  1. Open the Audio CPs panel.

  2. Choose a method and click Configure… to adjust parameters.

  3. Click Detect.

Detected onsets and offsets are drawn as vertical lines on the plot and stored in the dataset as audio_cp_onsets / audio_cp_offsets.

Data format#

Note

Audio changepoints format is subject to change, still in development.

Audio changepoints use a different storage format because dense binary arrays at audio sample rates (e.g. 44 kHz) would be prohibitively large. They are stored as onset/offset time pairs in seconds:

ds["audio_cp_onsets"]  = xr.DataArray(
    onset_times_s,
    dims=["audio_cp"],
    attrs={"type": "audio_changepoints", "target_feature": "audio"},
)
ds["audio_cp_offsets"] = xr.DataArray(
    offset_times_s,
    dims=["audio_cp"],
    attrs={"type": "audio_changepoints", "target_feature": "audio"},
)

References#