Chipper

Chipper performs semi-automated segmentation and quantitative analysis of acoustic signals from natural sound recordings, focusing on birdsong, to derive reproducible frequency, duration, syllable, note, and song-syntax measurements for comparative and ecological studies.

Key Features:

• Semi-Automated Segmentation: Automates segmentation of acoustic signals into syllables and notes for large audio recordings.
• Thresholding for Noise and Syllable Similarity: Applies user-set thresholds for background noise levels and syllable similarity to refine segment detection and measurement.
• Frequency and Duration Analysis: Extracts quantitative measures of frequency and duration from segmented signals.
• Spectrogram and Signal Segmentation: Computes spectrograms and segments signals for downstream feature extraction and song-syntax analysis.
• Synthetic Data Testing: Validates measurement accuracy and repeatability using synthetic songs embedded with varying levels of background noise.
• Optimization for Diverse Recordings: Tailors analysis parameters to accommodate recordings from multiple species and varying recording qualities.
• Support for Citizen-Science Recordings: Processes heterogeneous audio data typical of citizen-science contributions.

Scientific Applications:

• Birdsong Research: Generates reproducible syllable-, note-, and song-syntax measurements for studies of avian communication and behavior.
• Ecological Monitoring: Provides quantitative acoustic metrics for species monitoring and biodiversity assessment from natural sound recordings.
• Citizen Science Integration: Enables incorporation of heterogeneous citizen-science audio datasets into large-scale acoustic analyses.

Methodology:

Implemented in Python; performs semi-automated segmentation, applies thresholds for noise levels and syllable similarity, computes spectrograms, extracts frequency and duration measurements, and was validated using synthetic songs embedded with varying levels of background noise.