iir1
A high-performance, header-only C++ library for realtime IIR digital signal processing with Butterworth, Chebyshev, and RBJ filters.
What is iir1?
IIR1 is a C++ library for infinite impulse response (IIR) digital signal processing, providing realtime sample-by-sample filtering. It implements common filter types like Butterworth, Chebyshev, and RBJ filters for lowpass, highpass, bandpass, and bandstop applications. The library is designed for high performance and memory safety, using template-based header-only code to avoid runtime allocations.
Developers and engineers working on realtime DSP applications, such as audio processing, sensor data filtering, embedded systems, and scientific computing where low-latency filtering is required.
IIR1 offers a combination of realtime performance, memory safety, and ease of integration through its header-only design. Unlike many DSP libraries, it avoids dynamic memory allocation entirely, making it suitable for safety-critical and embedded environments while supporting cross-platform development.
Overview
DSP IIR realtime filter library written in C++
Use Cases
Best For
- Realtime audio filtering in C++ applications
- Embedded systems requiring deterministic memory usage
- Sensor data stream processing with low latency
- Educational projects teaching IIR filter implementation
- Prototyping filters with Python (SciPy) and deploying in C++
- Cross-platform DSP applications on Linux, Windows, or macOS
Not Ideal For
- Projects requiring FIR filters or non-IIR DSP techniques, as the library is exclusively focused on IIR implementations.
- Applications with dynamic filter order requirements at runtime, since memory allocation is fixed at compile time via templates.
- Teams seeking out-of-the-box GUI tools or visual filter design interfaces, as IIR1 relies on code configuration and external Python scripts.
Pros & Cons
Pros
All filter code is in header files, enabling seamless compiler optimization and eliminating linking overhead, as stated in the README.
Uses C++ templates to allocate memory at compile time, preventing runtime allocations and memory leaks, crucial for embedded and realtime systems.
Tested on Linux, Windows, and macOS with GCC, Clang, and Visual Studio, ensuring reliability across different development environments.
Can import filter coefficients generated by SciPy's signal module, facilitating easy design in Python and efficient deployment in C++, as shown in the demo code.
Cons
Heavy use of C++ templates can increase compile times and require advanced knowledge, making it less accessible for developers unfamiliar with template metaprogramming.
Lacks built-in support for elliptic filters and other optimized designs; users must rely on Python integration for custom coefficients, as the README admits with the removal of optimization-based methods.
Default error handling via exceptions may introduce overhead in realtime loops, and disabling it requires manual configuration with IIR1_NO_EXCEPTIONS, which isn't always straightforward.
Frequently Asked Questions
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