DTLN | Tensorflow 2.x implementation of the DTLN real time speech denoising model. With TF-lite, ONNX and r | Machine Learning library

DTLN is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Keras, Neural Network applications. DTLN has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However DTLN build file is not available. You can download it from GitHub.

The DTLN model was handed in to the deep noise suppression challenge (DNS-Challenge) and the paper was presented at Interspeech 2020. This approach combines a short-time Fourier transform (STFT) and a learned analysis and synthesis basis in a stacked-network approach with less than one million parameters. The model was trained on 500h of noisy speech provided by the challenge organizers. The network is capable of real-time processing (one frame in, one frame out) and reaches competitive results. Combining these two types of signal transformations enables the DTLN to robustly extract information from magnitude spectra and incorporate phase information from the learned feature basis. The method shows state-of-the-art performance and outperforms the DNS-Challenge baseline by 0.24 points absolute in terms of the mean opinion score (MOS). For more information see the paper. The results of the DNS-Challenge are published here. We reached a competitive 8th place out of 17 teams in the real time track.