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Awesome Audio DSP

repository·main·Indexed 23 days ago

https://github.com/billydm/awesome-audio-dsp

A curated collection of resources for audio Digital Signal Processing (DSP) and plugin development. It covers code libraries, plugin APIs, mathematics, and hardware optimization. The repository includes learning paths for beginners, guidance on realtime safety in audio threads, and detailed software optimization tips regarding SIMD, block-based processing, and CPU cache efficiency.

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What's inside awesome-audio-dsp

  1. Compare modern audio plugin APIs (AUv2/v3, CLAP, LV2, VST3)

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    When choosing a modern plugin standard, consider the following trade-offs regarding platform support, licensing, and DAW compatibility:

    • AUv2/AUv3 (Audio Units): Best for Apple ecosystems (Mac, iOS). It is the only standard supported by Logic Pro. Licensed under Apache 2.0.
    • CLAP: A modern, fully open-source (MIT) standard designed for developers. It features a stable header-only C API, making it easy to create bindings for any language. It is cross-platform (Linux, Mac, Windows, Android, iOS) but currently has limited DAW support (e.g., Bitwig, MultitrackStudio).
    • LV2: The standard for the Linux ecosystem (ISC license). While it has great support in open-source DAWs (Ardour, REAPER, etc.), support on Mac and Windows is rare.
    • VST3: The industry standard for most commercial DAWs on Linux, Mac, and Windows. However, it is proprietary and requires a signed license agreement from Steinberg for closed-source distribution. It uses a complex C++ codebase.

  2. Explore curated audio DSP and development resources

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    The following external curated lists provide specialized knowledge for audio DSP, plugin development, and music production:

    Research and Development

    • Audio Effects Research: Scientific literature regarding audio effects.
    • Audio Plugin Development Resources: Links and resources for facilitating audio plugin development.
    • Awesome JUCE: Resources specifically for the JUCE framework.
    • Awesome Rust Audio: Resources for audio development in Rust.
    • dsp-learning: Learning resources for Digital Signal Processing.
    • Freeverb3 - DSP References: Technical DSP resources focused on reverb design.
    • SpotlightKid's Audio Software Development: Tools and libraries for audio development.
    • Useful Links for DSP and Audio Programming: Curated resources from rust.audio.
    • Valhalla Blog: Industry-leading insights and resources from Valhalla DSP.
    • WolfSound Resources: Recommended resources for learning audio programming.
    • More Awesome Music DSP: A large collection of audio DSP and plugin development resources.

    Electronics and Hardware

    • Awesome Electronics: Resources for electronics engineering, with a focus on low-power DC circuits.

    Software and Music Production

    • Awesome Music Production: Software and resources for music production.
    • Awesome Music: Categorized collection of music libraries, tools, frameworks, and software.
    • HyMPS: Open source software and libraries for audio/video content production.
    • OpenAudio: Open source audio plugins, apps, and related projects.
    • SpotlightKid's Audio & MIDI Applications: An actively updated list of open source audio plugins and apps.

  3. Explore Awesome Audio DSP resources

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    Awesome Audio DSP is a curated collection of resources for audio digital signal processing (DSP) and plugin development. The repository is organized into several specialized categories to help developers find specific tools and knowledge:

  4. Browse other free DSP textbooks and courses

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    The following resources are also available for studying audio DSP:

    • Discrete Time Signal Processing by Ekeeda: A large collection of free DSP video lectures.
    • The Art of VA Filter Design by Vadim Zavalishin: Focuses on techniques for adapting analogue designs into the digital realm (mathematically heavy).
    • The Scientist and Engineer's Guide to Digital Signal Processing by Steven W. Smith, Ph.D.: A general DSP resource (not limited to audio; mathematically heavy).
    • The Theory and Technique of Electronic Music by Miller Puckette: Focuses on musical DSP, teaching core concepts for common audio effects and synthesizers (mathematically heavy).

  5. Explore Electronics & Embedded Audio DSP resources

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    This section provides a curated list of resources for developing audio software and hardware using analog electronics and embedded microcontrollers. Key resources include:

  6. Apply UX design principles to audio plugins

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    To avoid "too much visual noise" and "choice paralysis" in audio software, follow these UX principles:

    • Visual Hierarchy: Make the most important parameters (those users reach for first) the most immediately apparent. Advanced controls should be less immediately apparent or hidden behind tabs if they don't disrupt workflow.
    • Guiding the Eye: Use color brightness, size, and empty space to separate and differentiate sections.
    • Brightness over Hue: Prioritize color brightness over hue. A well-designed UI should remain easily readable even when converted to grayscale.
    • Learning Resources: Consult Awesome UX for a large list of resources to help learn UI/UX design.

  7. Prerequisite mathematical knowledge for DSP

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    To effectively learn and implement Digital Signal Processing (DSP), you should have a foundation in the following mathematical areas:

    Essential Prerequisites

    • Algebra: Proficiency with polynomials, fractions, and exponents (exponents are critical).
    • Trigonometry: Understanding sine, cosine, tangent, arctangent, etc.
    • Logarithms: Specifically natural logarithms and the constant $e$.
    • Complex Numbers: Complex (imaginary) numbers and complex algebra are core pillars of DSP mathematics.

    Recommended (Nice to haves)

    • Linear Algebra: Knowledge of matrix transformations.
    • Calculus: Understanding of derivatives and integrals.
    • Differential Equations: General understanding of what they are and their importance (solving them is not strictly required for getting started).

  8. Use block-based processing for efficiency

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    Instead of processing audio sample-by-sample, process chunks (blocks) of samples in a loop. This reduces the overhead of function calls and branching logic.

    Implementation Strategies:

    • Full Block Processing: Wait until a block is fully filled before processing. This is easiest but adds latency equal to the block size.
    • Dynamic Block Processing: Design the algorithm to work with a dynamic number of samples per iteration. This avoids extra latency but is more complex.
    • Multiple Loops: Create specific loops for different sample counts (e.g., a specialized loop for when the host sends only 1 sample). This avoids latency but duplicates code.

    Optimal Sizes:

    • Block sizes typically range from 8 to 1024 samples.
    • Buffer sizes to avoid cache misses typically range from 8 to 2048 samples.

  10. Prototype DSP using Visual Code environments

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    Visual programming languages allow you to create DSP logic by connecting nodes in a graphical interface, bridging the gap between codeless and text-based coding.

    Available Visual Code Tools

    • Max: A popular proprietary visual programming language. (Proprietary).
    • Pure Data (Pd): An open-source visual programming language. (Free/Open Source).
      • plugdata: A wrapper for Pure Data providing a significantly improved GUI.
      • HVCC: A tool used to convert Pure Data graphs into C/C++ code.

  12. Explore Machine Learning resources for audio processing

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    The repository provides a curated list of resources for learning and implementing machine learning in the context of audio processing. Note that the scope is strictly limited to using neural networks for audio processing; Large Language Models (LLMs) and generative AI are explicitly excluded.

    Learning Resources

    • 3Blue1Brown - Neural Networks: A YouTube series explaining the fundamental mechanics of machine learning.
    • Deep Learning for Audio: A dedicated course for applying deep learning techniques to audio processing tasks.

    Tooling and Libraries

    • audioFlux: A deep learning tool library designed for audio and music analysis.
    • nnAudio: An audio processing toolbox built on the PyTorch convolutional neural network backend.
    • RTNeural: A high-performance C++ library for neural inferencing, optimized specifically for real-time audio plugins.
    • SmartCore: A comprehensive machine learning library written in Rust.