snrldr

snrldr is an open-source software toolkit designed to estimate and improve the signal-to-noise ratio of audio and speech signals. It provides a modular framework for analyzing incoming audio, computing SNR estimates across time-frequency regions, and applying denoising operations. The project targets researchers, audio engineers, and field-recording workflows that require measurable noise suppression and transparent reporting of noise levels.

The architecture centers on a flexible pipeline with distinct stages for analysis, estimation, denoising, and evaluation. The SNR estimation engine supports multiple algorithms, including traditional spectral-based approaches and modern learning-assisted methods. Denoising modules range from classical spectral subtraction and Wiener filtering to neural-network–driven denoisers, and can be combined in configurable sequences. snrldr exposes a C++ core with bindings for Python and other languages, a command-line interface, and a plugin system that allows users to add new estimators, denoisers, or quality metrics.

Input and output formats emphasize interoperability, with support for common audio formats such as WAV and FLAC, as well as real-time streaming. The toolkit is designed to run cross-platform, including Linux, Windows, and macOS environments, and emphasizes reproducible results through configurable pipelines and presets.

snrldr originated from a collaboration among researchers and developers focused on quantitative noise suppression tools. It is distributed under an open-source license and maintained via a public repository, with documentation and example workflows available to assist new users. The project seeks to balance computational efficiency with denoising quality, and it is often evaluated against established benchmarks in audio denoising and SNR estimation.

See also: noise reduction, audio processing, signal processing libraries.