jhuus/HawkEars

Scan audio recordings and identify birds, amphibians and other classes.

What's novel

Scan audio recordings and identify birds, amphibians and other classes.

Code Analysis

11 files read · 3 rounds

A bioacoustic desktop application that detects and classifies bird and amphibian species in audio recordings using ensemble deep learning inference, with region-specific heuristics for correcting common misidentifications, and a full GUI for project-based analysis, review, and export.

Strengths

Genuinely sophisticated domain-specific heuristics (soundalike correction using scipy signal processing, low-band secondary classifier, occurrence-based rare/common species disambiguation) layered on top of ensemble inference, combined with a well-architected GUI featuring resumable analysis, 15+ reproducible review queue strategies, and atomic model installation with SHA-256 verification.

Weaknesses

The main window file is 219KB which is a significant maintainability concern, and the analyzer's ensemble offset logic (1-12 models) is hardcoded with if/elif chains rather than being data-driven, making it fragile to extend.

Score Breakdown

Innovation

6 (25%)

Craft

77 (35%)

Traction

14 (15%)

Scope

77 (25%)

Signal breakdown

Innovation

Not Fork+1

Code Novelty+1

Concept Novelty+2

Craft

Ci-1

Tests+8

Polish+0

Releases+3

Has License+5

Code Quality+25

Readme Quality+15

Recent Activity+7

Structure Quality+5

Commit Consistency+5

Has Dependency Mgmt+5

Traction

Forks+0

Stars+6

Hn Points+0

Watchers+3

Early Traction+5

Devto Reactions+0

Community Contribs+0

Scope

Commits+8

Languages+5

Subsystems+5

Bloat Penalty+0

Completeness+7

Contributors+5

Authored Files+12

Readme Code Match+3

Architecture Depth+7

Implementation Depth+8

Evidence

Commits

145

Contributors

Files

Active weeks

TestsCI/CDREADMELicenseContributing

Repository

Language

Python

Stars

Forks

License

MIT