What's the best Python library for audio feature extraction?

For general use, librosa is popular due to its ease and comprehensiveness. For performance, aubio or essentia (C++ with Python bindings) are better, as listed in the 'Feature extraction' section.

How to get started with music information retrieval in Python?

Start with librosa for analysis and mir_eval for scoring. The list includes resources like 'MIR Notebooks' tutorials and the 'Fundamentals of Music Processing' book for guided learning.

Python audio processing vs MATLAB for research?

Python offers open-source tools and better ML integration (e.g., PyTorch), while MATLAB has built-in toolboxes. This list highlights Python's growing ecosystem, but MATLAB remains strong in some acoustic fields.

Is librosa good for deep learning audio tasks?

Librosa excels at preprocessing, but for deep learning, pair it with Kapre for Keras or TorchAudio for PyTorch, as noted in the 'Deep Learning' section for neural network integration.

How to install and use the aubio library in Python?

Install via pip ('pip install aubio'). Use it for features like pitch detection; the README links to its GitHub and PyPI pages for documentation and examples.

Scientific Audio

A curated list of Python software and packages for scientific audio and music research.

GitHub

1.7k stars182 forks0 contributors

What is Scientific Audio?

Awesome Python Scientific Audio is a curated directory of Python libraries and tools specifically for scientific audio and music research. It solves the problem of discovering specialized audio processing packages by categorizing them into domains like feature extraction, source separation, music information retrieval, and deep learning for audio. The list focuses on research-grade tools used in academic and scientific applications rather than general-purpose audio manipulation.

Target Audience

Audio researchers, computational musicologists, signal processing engineers, and data scientists working with audio data who need to discover specialized Python tools for scientific analysis. It's particularly valuable for graduate students and academics entering the field of audio signal processing.

Value Proposition

Developers choose this resource because it provides a specialized, research-focused alternative to general Python package lists. It saves significant discovery time by vetting and categorizing packages specifically for scientific audio work, with clear domain classifications that help researchers find tools matching their exact needs.

Overview

Curated list of python software and packages related to scientific research in audio

Use Cases

Best For

Related Projects

Community-curated · Updated weekly · 100% open source

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Researchers discovering Python tools for audio signal processing experiments
Graduate students starting projects in music information retrieval (MIR)
Data scientists looking for audio feature extraction libraries
Academics comparing different source separation implementations
Engineers building scientific audio analysis pipelines
Developers needing specialized audio DSP libraries beyond general-purpose options

Not Ideal For

Developers needing production-grade audio software with commercial support and extensive documentation
Projects requiring low-latency, real-time audio processing with guaranteed performance
Beginners seeking integrated tutorials or hands-on coding examples
Teams working primarily with non-Python ecosystems like C++ or JavaScript

Pros & Cons

Pros

Curated Scientific Focus

Prioritizes research-grade tools over general-purpose libraries, with dedicated sections like 'Perceptial Models - Auditory Models' and 'Source Separation' for academic use.

Structured Categorization

Organizes 66+ packages into 15+ logical domains such as feature extraction and deep learning, making discovery efficient for specific research needs.

Comprehensive Resource Aggregation

Includes tutorials, books, and scientific papers beyond packages, providing a holistic learning path as seen in the 'Tutorials' and 'Books' sections.

Community-Vetted Directory

Actively maintained with contribution guidelines, aiming to reduce fragmentation in the scientific audio Python ecosystem.

Cons

No Maintenance Indicators

The list lacks information on package versioning, active development status, or compatibility, risking reliance on outdated or abandoned libraries.

No Comparative Guidance

While well-categorized, it offers no advice on which tool is best for specific tasks, leaving users to trial each option independently.

Potential for Staleness

As a manually curated list, updates may be infrequent, similar to the 'PythonInMusic' list it criticizes, leading to deprecated entries over time.

Frequently Asked Questions

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