Wildlife Acoustics / Birdsong Species Identification

Pain Points

!Unattended long recordings (up to months) make manual species identification segment by segment impractical.
!Sample rates vary widely across species (48k vs. ultrasonic 192k+), so the workflow must be unified.
!Species activity rhythm and diversity indices lack one-stop analysis.

The Tinia Approach

Mel spectrum + time-frequency texture + AutoML multi-class classification reveal species activity rhythms — audible and ultrasonic reuse the workflow by just adjusting the sampling band.

Sliding-window segmentationMel spectrumStructure-tensor textureAutoML multi-classActivity rhythm

Nodes Used

Audio Segment Splitaudio_segment_split

Sliding-window segmentation

Spectral Feature Extractionfbank_extract

Mel spectrum

Structure-Tensor Featuresst_features

Time-frequency texture features

Feature Mergefeature_merge

Feature merging

Chart Viewerchart_viewer

Visualize species activity rhythm

Core nodes; add or remove steps as needed for your data and standards — the node graph is always editable.

Expected Output

Spectrum and species classification for each time window.
Species activity rhythm (day / night / season).
A long-term data foundation for biodiversity monitoring.

Reference Standards / Engineering PracticeBirdNET / Cornell Lab framework

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Wildlife Acoustics / Birdsong Species Identification

Pain Points

The Tinia Approach

Nodes Used

Expected Output

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