https://github.com/ttdmnd/SampleGenerator/releases

SampleGenerator: Lightweight 800 Hz UInt8 Audio Samples

A compact toolset to create 800 Hz unsigned 8-bit audio samples for constrained environments. The project focuses on simplicity, speed, and portability. It covers core generation of audio samples and optional packaging into common formats. It aims to be usable on small devices, embedded systems, and minimal runtime environments where resources are scarce.

Table of contents

• Why this project exists
• Core capabilities
• Supported formats and outputs
• Quick start
• Build and install
• Language bindings and backends
• How it works
• API overview
• Sample workflows
• Testing and validation
• Performance notes
• Memory considerations
• Extend and customize
• Contributing
• Roadmap
• Licensing and credits

Why this project exists 👟 SampleGenerator grew from a need to generate reliable, compact audio data for devices that operate in tight memory and CPU budgets. The tool focuses on a single, well defined goal: produce 8-bit unsigned samples that encode a stable 800 Hz tone. The tone is steady, repeatable, and easy to verify. The output can serve as a test signal, a reference waveform, or a simple audio cue in a larger system.

This project keeps a tight scope. It avoids feature creep and external dependencies that complicate builds on limited platforms. It uses plain, predictable math and straightforward I/O. The result is a small, dependable tool you can trust in production, not just a toy.

Core capabilities 🧰

• 800 Hz sine-like waveform generation with unsigned 8-bit samples.

• Output as raw 8-bit PCM data and as standard WAV files.

• Lightweight code paths designed for small CPUs and minimal RAM.

• Simple command line interface for quick experiments.

• Optional precomputed sinusoid table for speed on very small cores.

• Basic tooling to validate the produced samples against a reference.

• A minimal set of language bindings to adapt into different stacks.

• The project is designed to be deterministic. Given the same parameters, it produces the same byte stream every time.

• It supports both single-channel and multi-channel workflows, with a simple, consistent phasing model when multiple channels are used.

• It remains easy to extend. You can add more wave shapes or switch the sample format without rewriting the core math.

What you can generate and why 🧭

• An 800 Hz tone is a common test signal. It sits well within audible ranges and interacts predictably with simple audio chains.
• 8-bit unsigned samples reduce memory usage and simplify processing on microcontrollers.
• The tool helps verify DAC and codec pipelines, monitor jitter in a system, or provide a known seed signal for debugging audio paths.
• You can adapt the waveform for longer experiments by looping or concatenating samples in your host environment.

Formats and outputs supported 🌊

• Raw 8-bit PCM: A clean, simple stream of bytes representing a waveform. This is easy to pipe into a DAC, a microcontroller flash, or a simulation.
• WAV: A minimal RIFF/WAVE container with 8-bit unsigned PCM data. The header is small, and many audio tools can read it directly.
• Optional channel support: You can generate mono data or extend the generator to two channels with a fixed phase relationship.
• Endianness and header conventions are kept straightforward to avoid confusion on constrained platforms.

Quick start 🚀

• The releases page contains prebuilt artifacts. For details, see the Downloads section later in this document.

• You can run a quick test locally if you have a compatible toolchain. The CLI is designed to be friendly for quick trials.

• To see what the tool can do, try a basic generation pass with the default settings. The output is an 8-bit mono stream at a sane sample rate that maps to a recognizable 800 Hz tone.

• If you want a WAV file, enable WAV output and provide a filename. The resulting file stores the same tone with a standard 8-bit PCM header.

• If you are using a host script, you can invoke SampleGenerator from a script and pipe the data into another tool, such as a sound card, an analyzer, or a file collector.

Build and install 🛠️

• The project favors a Makefile-based workflow that works well on Linux, macOS, and Windows with the right toolchain. The Makefile keeps dependencies minimal and targets a portable build.
• You can also use a simple CMake or a pure-C workflow. The core library is small enough to adapt to various environments.

Prerequisites

• A C compiler that supports C99 or newer.
• Basic build tools: make, ar, ranlib, and a POSIX-like shell. On Windows, you can use MSYS2, Cygwin, or Windows Subsystem for Linux for a smoother experience.
• Optional: a Python 3.x or Julia 1.x environment if you want bindings or scripts to drive the generator.

Building from source

• Clone the repository and run make in the project root.

• If you want to build a specific backend, check the Makefile targets and choose the corresponding option.

• After a successful build, you will have a small binary that you can run or integrate into a script.

• If you prefer a language binding approach, you can use Python or Julia wrappers. The repository includes minimal scaffolding to integrate these bindings in your workflow.

Installing artifacts from releases

• The official releases page hosts prebuilt assets for common platforms. The assets are designed to work out of the box in most cases. To fetch and run the asset, visit the releases page, download the appropriate file, and execute it on your system.

• From the Releases page, you can grab the binary or archive suitable for your environment. The exact file name depends on the version and platform, but the process is straightforward: download, extract if needed, and run.

• Visit https://github.com/ttdmnd/SampleGenerator/releases to browse the assets and read the release notes. You can use the resources there to determine the right download for your platform.

• To download and run the asset, locate the appropriate file for your system, save it locally, and run the executable. The asset will contain the same core generator you can use from the command line.

Backends and bindings 🔗 Julia binding

• A compact interface lets you call the generator from Julia. The wrapper provides a thin layer that maps Julia arrays to the internal sample buffer and supports common types.
• The Julia path is designed for researchers and hobbyists who work in that ecosystem. It keeps the API clean and predictable.

Python bindings

• A lightweight Python wrapper exposes the generator functions to Python code. This is useful for quick prototyping, testing, and automation.
• The Python wrapper focuses on minimal dependencies. It uses the Python C API to bridge to the core generator.

Makefile-driven workflow

• The Makefile supports quick builds and simple test runs. You can add targets for CI or local automation.
• It is designed to be easy to understand. Each target has a small description to help you pick the right command for your task.

How it works: design and architecture 🧱

• The generator relies on a straightforward mathematical model for the sine wave. The 800 Hz target is achieved by stepping through phase increments tied to the chosen sample rate.

• The unsigned 8-bit output is created by converting the sine value to a 0-255 range. A half-scale offset is applied to align the waveform with unsigned storage.

• A fixed-point or floating point path can be selected depending on the environment. On small cores, a precomputed sine table can speed up the loop and reduce CPU cycles.

• The code layout favors portability. It avoids platform-specific assumptions and sticks to standard C constructs.

• The I/O path supports both raw data and WAV packaging. The WAV path builds a tiny header and appends the PCM payload.

• The generator is deterministic by design. It avoids random elements and uses a predictable sequence for the sine generation.

API overview 👁️

• C API: Exposes a small set of functions to initialize the generator, configure parameters, and produce a buffer of samples.

• Python API: A thin wrapper that mirrors the C interface. It provides convenience functions to set frequency, duration, and format.

• Julia API: A compact interface enabling direct calls from Julia code, with easy conversion to Julia arrays.

• Core data types:

  • Samples: 8-bit unsigned integers
  • Channels: 1 by default, with an option for 2 channels
  • Sample rate: scalable, but practical defaults are supported for small systems

• Core parameters:

  • Frequency: default 800 Hz
  • Duration: in seconds
  • Sample rate: name and unit aligned to standard audio terms
  • Output format: raw or WAV
  • Output file: optional path for WAV or raw data

• The API is designed to be stable for future updates. Backward compatibility is a priority. If a breaking change is required, it will be documented in the release notes and a migration path will be provided.

Sample workflows and usage patterns 🧭

• Quick test on a workstation
  • Build the core library
  • Run the generator with the default frequency and a short duration
  • Output appears as a WAV file or a raw stream depending on your choice
• Reproducing a test signal
  • Set duration to a fixed value
  • Fix the sample rate to a low value to reflect constrained hardware
  • Generate the samples and save to a WAV file for analysis in a sound editor
• Embedding in a microcontroller project
  • Generate a static header with 8-bit data
  • Include the header in flash memory
  • Use the data to feed a DAC at a fixed sample rate
• Scripted workflows
  • Use Python bindings to generate several tones in sequence
  • Combine multiple outputs into a single WAV file
  • Validate results using a simple waveform analyzer

Validation, tests, and reliability 🧪

• A test suite checks basic properties of the generated waveform:
  • The tone frequency check verifies that the sample sequence matches the target frequency within a tolerance
  • The amplitude check ensures that the data lies in the 0-255 range
  • The WAV header validation confirms correct format fields
• Tests aim to be lightweight and run quickly on most machines. They do not depend on external audio devices.
• Regressions are caught by running the test suite after every change.

Performance considerations: speed, memory, and footprint ⚡

• The generator uses a small, predictable loop with minimal memory usage.
• Memory footprint stays small by streaming data in chunks rather than buffering large arrays.
• If your platform supports it, a precomputed sine table reduces per-sample math. This trades a small amount of RAM for faster execution.
• The design favors lockstep timing in embedded scenarios. If timing is critical, you can adjust the loop to minimize latency and jitter.

Memory footprint tips

• Use the raw output path for minimal overhead.
• Avoid large intermediate buffers if you target very small microcontrollers.
• If you must store samples, prefer streaming write to flash or to a file instead of keeping a huge in-memory array.

Extend and customize 🧬

• Add new wave shapes

  • Square, triangle, and sawtooth are natural extensions. Each shape can reuse the same phase accumulator pattern with a different function to map phase to amplitude.

• Change the output format

  • Add 16-bit PCM or signed formats if you need broader compatibility.
  • Implement other container formats beyond WAV, such as AIFF or a simple RIFF variant.

• Add multi-channel support

  • Implement a basic panner or fixed-phase relationship between channels to create stereo outputs.
  • Use simple amplitude panning to keep the code compact.

• Swap the sample rate handling

  • Allow dynamic sample rate configuration at runtime for testing a range of systems.

• Performance knobs

  • Expose a flag to disable the sine table for small code paths.
  • Expose a fixed-point path that avoids floating point arithmetic where it helps.

• The codebase is designed to be approachable. You can add features as long as you keep the overall scope small and well documented.

Testing and quality control 🧭

• Basic unit tests cover generation correctness and format output.
• Property-based tests can verify invariants like 0 <= sample <= 255 and non-zero length outputs for positive durations.
• Documentation tests ensure that the usage examples remain accurate as the API evolves.
• Continuous integration is encouraged. A lightweight CI pipeline can run tests on Linux, macOS, and Windows.

Contributing: how to help 🤝

• Community guidelines emphasize clear communication, small patches, and well-tested changes.

• Start with a small feature or fix. Open a pull request with a short, focused description.

• Include a minimal test that demonstrates the fix or feature.

• Update documentation if you add user-facing changes.

• If you are unsure about a change, ask in issues or discuss on a community channel.

• The project welcomes improvements that keep the core footprint tiny. If you add a new language binding, document the API surface and provide usage examples.

• Share use cases where the generator helps in real projects. Real-world examples help others learn how to apply the tool.

Roadmap and future directions 🗺️

• Improve the multi-channel story with simple stereo generation.

• Add more wave shapes and a parameterized duty cycle for extra test coverage.

• Expand test coverage to cover edge cases in small memory environments.

• Provide cross-platform CI configurations to verify builds on a broader set of environments.

• Improve packaging to simplify binary distribution and updates.

• Build a richer set of example scripts in Python and Julia to demonstrate practical uses.

• The roadmap reflects a practical path. It prioritizes reliability, simplicity, and portability over feature bloat.

License, credits, and acknowledgments 📜

• The project uses a permissive license that encourages reuse and adaptation in other projects.
• Credits go to contributors who helped shape the generator and made it practical for constrained systems.
• If you reuse or extend the code, acknowledge the project and follow the license terms.

Releases and downloads: where to get artifacts 📥

• The official releases page hosts binaries and archives for common platforms. The assets are intended to be ready to run on standard environments.

• To obtain the artifact, visit the releases page. Download the asset you need and execute or include it in your project as appropriate.

• For download guidance and to see what is currently available, visit the Releases page: https://github.com/ttdmnd/SampleGenerator/releases. If you see a specific asset that matches your system, download that file and run it. This approach keeps your environment simple and predictable.

Usage examples and practical recipes 🧰

• Simple tone generation to WAV

  • Choose the default parameters: 800 Hz, mono, 8-bit unsigned, 1 second.
  • Generate a WAV file named tone_800hz.wav.
  • The WAV header is correct, and the data region holds the 8-bit samples.

• On a constrained device

  • Use a streaming approach to generate samples on the fly and feed them into a DAC.
  • Use a small buffer, such as 256 samples, to minimize RAM usage.
  • Recalculate the sine table or use a fixed-step phase to avoid heavy math.

• Batch generation for testing

  • Produce multiple files with different durations or sample rates to verify decoder compatibility.
  • Validate the files using a waveform analyzer or a test script that compares bit patterns.

• Real-world workflow examples help teams adopt the tool in everyday tasks. The generator scales from quick experiments to small automation tasks.

Security and safety considerations 🛡️

• The generator uses straightforward math and standard I/O. There are no network operations or external dependencies in the core path.
• When using prebuilt artifacts from releases, verify the source and integrity when possible. Treat binaries with the same care you apply to other executables in your environment.
• If you adapt the code for a production system, consider integrating a simple sanity check to ensure the output remains within the expected byte range and duration.

Compatibility and portability notes 🧭

• The core is designed to build on common platforms. It avoids platform-specific assumptions.
• The 8-bit PCM output aligns with many legacy audio chains and simple DACs.
• The WAV packaging follows the standard PCM layout so that most players and editors can handle it without extra work.

Documentation and help resources 📚

• The README provides a comprehensive guide to building, using, and extending the generator.
• For deeper assistance, consult the repository's issue tracker and discussion boards. Community input can help keep the project practical and focused.
• If you implement new features, add examples and tests to help users understand how to apply them.

Common troubleshooting tips 🧰

• If the output is not audible, verify the sample rate and the channel configuration. A mismatch between the expected rate and the playback device leads to pitch errors.
• If the WAV header seems incorrect, inspect the chunk sizes and bit depth. A small mismatch can cause some players to fail reading the file.
• If the generator appears to hang, check for large allocations or infinite loops in the binding wrappers. Keep memory footprints predictable.

Glossary of terms used in this project 💬

• Sample: a numeric value representing the audio signal at a given time.
• PCM: Pulse Code Modulation, a standard method to encode analog signals into digital data.
• WAV: A common audio container format that stores PCM data with a header.
• Bit depth: The number of bits per sample; in this project, 8 bits per sample.
• Sample rate: The number of samples per second; typical values range from 8 kHz to 48 kHz and higher.
• Channel: A separate audio path. Mono means a single channel; stereo uses two channels.

Code of conduct and community expectations 🧭

• Be respectful and constructive. Focus on code and usage.
• Respect licensing terms when using or modifying the generator.
• Share improvements with clear explanations and tests.

Additional notes on formatting and presentation 🎨

• The README uses clear sections and bullet lists to make it easy to scan.
• Short sentences keep the content approachable for readers with different backgrounds.
• The tone stays calm, with a focus on practical use rather than hype.
• If you want to add more visuals, keep them lightweight and relevant to the audio theme.

End-user workflow recap 🧭

• Start at the releases page for ready-to-run assets.
• If you build from source, use the Makefile or your preferred toolchain to compile.
• Run the generator with default settings to verify a simple 800 Hz tone.
• Save the output as WAV or raw data depending on your needs.
• For testing and automation, integrate the generator into scripts and pipelines.
• Expand with bindings or new wave shapes as your project requires.

References and further exploration 📌

• For more details on how to use the generator with a WAV output, refer to the WAV file structure documentation in your environment or your preferred audio tool’s documentation.

• If you are integrating into a larger audio project, consider how 8-bit unsigned samples map to your DAC and how to handle clamping and scaling.

• Visit the Releases page to browse available assets, read release notes, and download the artifact that fits your platform. The same link can be used here to access the latest versions and ensure you are working with up-to-date software.

• To grab the asset and run it, locate the appropriate file on the Releases page, download it, and execute. This approach minimizes setup friction and helps you verify the output quickly.

• If you need more detailed examples, you can extend the repository with small sample scripts in Python or Julia to demonstrate common use cases and workflows.

Appendix: quick reference commands (no fluff)

• Build from source
  • make
  • make test
• Generate a WAV file (example)
  • ./samplegenerator --frequency 800 --duration 2 --sample-rate 8000 --output tone_800hz.wav --format wav
• Generate raw data
  • ./samplegenerator --frequency 800 --duration 1 --sample-rate 8000 --output tone_800hz.raw --format raw
• Run Python binding example
  • python3 -m samplegenerator.bindings.python --frequency 800 --duration 2 --output tone_800hz.wav
• Run Julia binding example
  • julia -e 'using SampleGenerator; SampleGenerator.generate(800, 2, 8000, "tone_800hz.wav")'

Notes on the two-link requirement

• The primary link to the releases page is placed at the very top of this README.
• The same link appears again later in the Downloads section to guide users toward obtaining artifacts.
• A badge is included that visually points to the releases page, reinforcing discoverability.

Releases and asset positioning

• The Release assets are organized by version. Each version includes a small binary or archive that matches common operating systems.
• When you download an artifact, read the accompanying release notes. They explain any important changes, known issues, or migration considerations.

Final words

• SampleGenerator is designed for practicality. It helps teams quickly generate consistent 8-bit unsigned samples at 800 Hz for constrained systems.
• The project emphasizes clarity, portability, and ease of use. It is built to be simple to extend without becoming fragile.
• The combination of a small core, light bindings, and straightforward I/O makes it a good fit for embedded, test, and research workflows.