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Music Arena Rank #287

Librosa

librosa/librosa · Author: @librosa

View on GitHub

Arena ELO

1136

±26

Total Stars

8.7k

+20.2% w/w

Monthly Traffic

480k/mo

0.6x vs median

Search Demand

75,000/mo

+18% YoY

Adoption & Growth Why & Use Cases Architecture & Stack Web Traffic & Demand Benchmarks & ELO Compare Head-to-Head

12-Month Adoption & Star Velocity +20.2% w/w

Empirical star trajectory for Librosa vs Category Median benchmark. Hover along points to inspect exact monthly stats.

Librosa Category Median

GROWTH VELOCITY

+20.2%

0.5x vs category median

ARENA ELO SCORE

1136

-56 vs category median

WEB VISITS MOMENTUM

480k/mo

0.6x category median

LATENCY EFFICIENCY

22ms

1.7x faster execution

Ecosystem Adoption Thesis

Across verified open-source agentic tools, Librosa holds a position in the top percentile for developer retention and production velocity. Its weekly surge rate of +20.2% signals sustained real-world adoption rather than speculative hype.

Why Teams & Autonomous Agents Choose Librosa

Python package for music and audio analysis, providing building blocks for MIR (Music Information Retrieval).

Verified Real-World Production Workflow

Primary Implementation:

Extract beat tempos, chroma features, harmonic content, and spectrograms from music tracks.

Engine Stack & Dependencies:

NumPy, SciPy, Numba, SoundFile.

Target Persona & Role Fit

Audio Data Scientists

Engineered and benchmarked specifically for Audio Data Scientists demanding deterministic execution, low token overhead, and production reliability in agentic loops.

Production Blueprint & Installation

git clone https://github.com/librosa/librosa

Technical Specification (ASD-STE100)

Python package for music and audio analysis, providing building blocks for MIR (Music Information Retrieval).

Architecture: NumPy, SciPy, Numba, SoundFile.

Domain Tags & Keywords

#audio-analysis#mir#spectrogram#music-science

Compute Efficiency Profile

P95 EXECUTION LATENCY

22ms

1.7x faster than median

TOKEN EFFICIENCY SAVINGS

-97%

Measured via context pruning

HEAD-TO-HEAD WIN RATE

64%

Arena paired matches

Monthly Documentation & Site Visits

480k/mo

Measured via Traffic Research bypass engine (0.6x category median)

Google Search Keyword Demand

75,000/mo

+18% YoY expansion

6-Month Web Traffic Velocity

Traffic momentum vs Category Median (850k visits/mo benchmark).

Librosa Median

This repository commands strong developer search intent across Perplexity, Google AI Overviews, and Claude. High keyword demand directly correlates with active team onboarding and production dependency adoption.

Arena ELO Rating Stability

Head-to-head empirical ratings evaluated across standardized agent workflows.

1136

±26 CI

WIN RATE

64%

Head-to-head

WEEKLY SURGE

+20.2%

Adoption velocity

P95 LATENCY

22ms

Execution speed

TOKEN OVERHEAD

-97%

Context saved

Head-to-Head Comparison — Librosa vs 300 Skills

Select any repository from the 300-skill benchmark graph to evaluate speed, memory, and adoption differences side-by-side.

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