pyAudioAnalysis vs librosa

Side-by-side — pricing, strengths, tradeoffs, and fit.

pyAudioAnalysis

Library for audio feature extraction and machine learning applications.

librosa

Python library for audio and music analysis.

At a glance

Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
● Established·From $0

Overview

One-liner

Library for audio feature extraction and machine learning applications.

One-liner

Python library for audio and music analysis.

Maturity

Established

Maturity

Established

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Open source — free to use

Starts at

$0

Starts at

$0

Model

Flat rate

Model

Flat rate

Enterprise

✕ None

Enterprise

✕ None

Best for

✓ Best for

Developers working on projects that require feature extraction from audio files using Python.
Researchers conducting studies in music information retrieval and speech processing.

✓ Best for

Researchers working on music information retrieval projects who need robust feature extraction capabilities.
Developers building real-time audio analysis tools that require efficient signal processing.
Academics studying acoustic scenes and needing precise pitch detection.

Not ideal for

✕ Not for

Projects requiring real-time audio analysis, as PyAudioAnalysis is optimized for batch processing.
Applications needing a web-based interface or cloud-hosted service.

✕ Not for

Projects requiring real-time streaming of large audio files due to computational overhead.
Applications where the Python environment is not feasible or preferred.

Strengths

↑ Strengths

Feature extraction from audio files
Classification of audio data using machine learning techniques
Segmentation and analysis of long audio recordings

↑ Strengths

Efficient audio loading and resampling
Feature extraction including MFCCs, chroma features, and spectral contrast
Time-frequency analysis with STFT and CQT
Beat tracking and tempo estimation

Weaknesses

↓ Weaknesses

Limited documentation and examples
Performance limitations with large datasets
Narrow focus on specific use cases
Small community and limited support

↓ Weaknesses

Frequent breaking changes between versions
Limited support for real-time audio processing
Resource-intensive operations at scale
Full pyAudioAnalysis profile →Full librosa profile →