From 7e0892801c683b047ce7a6eabf0ffc8ea2f2f701 Mon Sep 17 00:00:00 2001 From: f4exb Date: Sun, 11 Jan 2026 09:18:15 +0100 Subject: [PATCH] Denoiser feature: build only for Linux --- external/CMakeLists.txt | 2 +- plugins/feature/denoiser/readme.md | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/external/CMakeLists.txt b/external/CMakeLists.txt index d200b8d84..7afc022f4 100644 --- a/external/CMakeLists.txt +++ b/external/CMakeLists.txt @@ -916,7 +916,7 @@ if(ENABLE_FEATURE_MORSEDECODER) endif() # For denoiser feature -if(NOT APPLE AND ENABLE_FEATURE_DENOISER) +if(LINUX AND ENABLE_FEATURE_DENOISER) if (WIN32) set(RNNOISE_LIBRARIES "${SDRANGEL_BINARY_LIB_DIR}/rnnoise.lib" CACHE INTERNAL "") set(RNNOISE_ARGS "-DRNN_ENABLE_X86_RTCD=OFF") diff --git a/plugins/feature/denoiser/readme.md b/plugins/feature/denoiser/readme.md index 09d41e397..f1148d614 100644 --- a/plugins/feature/denoiser/readme.md +++ b/plugins/feature/denoiser/readme.md @@ -13,7 +13,7 @@ It connects to the "demod" stream of Rx channels similarly to the Demod analyzer - WFM demodulator - WDSP plugin (multimode) -Note that this plugin is not available on Mac O/S +Note that this plugin is available only in Linux The following noise reduction schemes are covered. It can be selected via the (6) combo box: @@ -21,7 +21,7 @@ The following noise reduction schemes are covered. It can be selected via the (6 Noise reduction based on the RNnoise library originally from J.M. Valin. It uses a fork for easier integration in the build system (Cmake support with download of the model file): https://github.com/f4exb/rnnoise -The noise reduction is based on a mix of DSP functions and a recursive neural network (RNN). Basically the RNN helps the DSP functions to adjust the gain in various spectral bands thus very efficiently cancelling the background noise in many situations. Although the model was not particularly trained on radio transmissions it can do a pretty good job at AM, SSB, FM noise reduction however you will need a reasonable SNR to get something out of it else it will consider the audio is just noise. Do not expect it to dig signals out of the noise the goal is to reduce ear fatigue by removing background white noise and other noises e.g birdies. +The noise reduction is based on a mix of DSP functions and a recursive neural network (RNN). Basically the RNN helps the DSP functions to adjust the gain in various spectral bands thus very efficiently cancelling the background noise in many situations. Although the model was not particularly trained on radio transmissions it can do a pretty good job at AM, SSB noise reduction however you will need a reasonable SNR to get something out of it else it will consider the audio is just noise. Do not expect it to dig signals out of the noise the goal is to reduce ear fatigue by removing background white noise and other noises e.g birdies. Results for FM signals may vary. You will find all the details about RNnoise here: https://jmvalin.ca/demo/rnnoise/