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acoustic-monitoring

Compute acoustic indices and detect species from WAV/FLAC recordings.

2|Updated Mar 2, 2026
Search Tags:#monitoring#detection#indices#soundscape#pam#acoustic#birdnet
One-click install
npx skills add https://github.com/baratadiego/ecological-agent-skills --skill acoustic-monitoring
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: acoustic-monitoring
Source: https://github.com/baratadiego/ecological-agent-skills/tree/main/skills/acoustic-monitoring
Command: npx skills add https://github.com/baratadiego/ecological-agent-skills --skill acoustic-monitoring

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, librosa, soundfile, birdnet_analyzer, and includes scripts (resource) components.

What problem does it solve?

This skill automates the analysis of passive acoustic monitoring (PAM) data to compute soundscape indices and detect species from audio recordings, enabling scalable biodiversity assessments from acoustic streams.

Core Features & Use Cases

  • Compute acoustic indices (ACI, NDSI, BI, H, ADI, AEI) across PAM recordings to quantify soundscape diversity and structure.
  • Run BirdNET-based species detections on WAV/FLAC files and summarize detections by species, site, and time.
  • Support temporal trend analysis, diel pattern assessments, and species richness estimation for long-term monitoring programs.
  • Use cases include assessing dawn chorus dynamics, urban-rural soundscape gradients, and recovery of restored habitats.

Quick Start

Load a directory of WAV/FLAC PAM recordings and run the acoustic-monitoring workflow.

Frequently Asked Questions about acoustic-monitoring

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compute acoustic indices like ACI and NDSI from passive acoustic monitoring recordings?▼

Acoustic indices quantify soundscape diversity by computing ACI, NDSI, BI, H, ADI, and AEI values from WAV or FLAC PAM recordings. This skill automates that calculation across multiple sites and time periods to enable scalable biodiversity assessments.

How do I run BirdNET species detection on a directory of WAV or FLAC audio files?▼

BirdNET species detection processes WAV or FLAC audio files to identify vocalizing species. This skill uses the BirdNET-Analyzer tooling to run batch detections and summarize results by species, site, and time for long-term monitoring.

Can I analyze diel patterns and temporal trends in soundscape recordings?▼

Temporal trend analysis of soundscape recordings is supported for assessing diel patterns and dawn chorus dynamics. The skill processes time-series PAM data to estimate species richness and track biodiversity changes across temporal scales.

Do I need librosa and numpy installed to batch process acoustic monitoring data?▼

Yes, librosa, numpy, soundfile, and birdnet_analyzer are required dependencies. These Python libraries handle audio loading, numerical operations, and species detection automation when running the acoustic monitoring scripts.

What is the best way to estimate species richness from passive acoustic monitoring data?▼

Species richness estimation from PAM data uses BirdNET-based detections summarized across sites and recording times. This approach automates the identification of vocalizing species to calculate biodiversity metrics for habitat recovery and soundscape gradient assessments.

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