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Recurrent neural network for audio noise reduction
| Date | Stars |
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| 2026-07-31 | 5754 |
| 2026-08-01 | 5755 |
| 2026-08-02 | 5755 |
| 2026-08-03 | 5755 |
| 2026-08-04 | 5757 |
| 2026-08-05 | 5761 |
| 2026-08-06 | 5761 |
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RNNoise is a noise suppression library based on a recurrent neural network. A description of the algorithm is provided in the following paper: J.-M. Valin, A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancement, Proceedings of IEEE Multimedia Signal Processing (MMSP) Workshop, arXiv:1709.08243, 2018. https://arxiv.org/pdf/1709.08243.pdf An interactive demo of version 0.1 is available at: https://jmvalin.ca/demo/rnnoise/ To compile, just type: % ./autogen.sh % ./configure % make Optionally: % make install It is recommended to either set -march= in the CFLAGS to an architecture with AVX2 support or to add --enable-x86-rtcd to the configure script so that AVX2 (or SSE4.1) can at least be used as an option. Note that the autogen.sh script will automatically download the model files from the Xiph.Org servers, since those are too large to put in Git. While it is meant to be used as a library, a simple command-line tool is provided as an example. It operates on RAW 16-bit (machine endian) mono PCM files sampled at 48 kHz. It can be used as: % ./examples/rnnoise_demo <noisy speech> <output denoised> The output is also a 16-bit raw PCM file. NOTE AGAIN, THE INPUT and OUTPUT ARE IN RAW FORMAT, NOT WAV. The latest version of the source is available from https://gitlab.xiph.org/xiph/rnnoise . The GitHub repository is a convenience copy. == Training == The models distributed with RNNoise are now trained using only the publicly available datasets listed below and using the training precedure described here. Exact results will still depend on the the exact mix of data used, on how long the training is performed and on the various random seeds involved. To train an RNNoise model, you need both clean speech data, and noise data. Both need to be sampled at 48 kHz, in 16-bit PCM format (machine endian). Clean speech data can be obtained from the datasets listed in the datasets.txt file, or by downloaded the already-concatenation of those files in https://media.xiph.org/rnnoise/data/tts_speech_48k.sw For noise data, we suggest the background_noise.sw and foreground_noise.sw (or later versions) noise files from https://media.xiph.org/rnnoise/data/ The foreground_noise.sw file contains noise signals that are meant to be added to the background noise (e.g. keyboard sounds). Optionally, the foreground noise file can even be denoised with a traditional denoiser (e.g. libspeexdsp) to keep only the transient components. For background noise, the data from the original RNNoise noise collection have now been sufficiently filtered to provide good results -- either alone or in combination with the background_noise.sw file. The dataset can be downloaded (updated Jan 30th 2025) from: https://media.xiph.org/rnnoise/rnnoise_contributions.tar.gz The first step is to take the speech and noise, and mix them in a variety of ways to simulate real life conditions (including pauses, filtering and more). Assuming the files are called speech.pcm and noise.pcm, start by generating the training feature data with: % ./dump_features speech.pcm background_noise.pcm foreground_noise.pcm features.f32 <count> where <count> is the number of sequences to process. The number of sequences should be at least 10000, but the more the better (200000 or more is recommended). Optionally, training can also simulate reverberation, in which case room impulse responses (RIR) are also needed. Limited RIR data is available at: https://media.xiph.org/rnnoise/data/measured_rirs-v2.tar.gz The format for those is raw 32-bit floating-point (files are little endian). Assuming a list of all the RIR files is contained in a rir_list.txt file, the training feature data can be generated with: % ./dump_features -rir_list rir_list.txt speech.pcm background_noise.pcm foreground_noise.pcm features.f32 <count> To make the feature generation faster, you can use the script provided in script/dump_features_parallel.sh (you will need to modify the script if you want to add
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Jean-Marc Valin · Google
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Luca Barbato
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Tim Gates · IRESS · Australia
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:24850b6278fd1e6a, llm:description/readme: "Recurrent neural network for audio noise reduction"; topics: audio, noise-reduction, rnn; readme describes RNNoise as a noise suppression library based on an RNN and includes training details and demo.
matched fp:24850b6278fd1e6a, llm:description/readme: "Recurrent neural network for audio noise reduction"; topics: audio, noise-reduction, rnn; readme describes RNNoise as a noise suppression library based on an RNN and includes training details and demo.