DeepVQE: Real Time Deep Voice Quality Enhancement for Joint Acoustic
  Echo Cancellation, Noise Suppression and Dereverberation
Evgenii Indenbom Nicolae-Catalin Ristea Ando Saabas Tanel Parnamaa Jegor Guzvin Ross Cutler
Acoustic echo cancellation (AEC), noise suppression (NS) and dereverberation
(DR) are an integral part of modern full-duplex communication systems. As the
demand for teleconferencing systems increases, addressing these tasks is
required for an effective and efficient online meeting experience. Most prior
research proposes solutions for these tasks separately, combining them with
digital signal processing (DSP) based components, resulting in complex
pipelines that are often impractical to deploy in real-world applications. This
paper proposes a real-time cross-attention deep model, named DeepVQE, based on
residual convolutional neural networks (CNNs) and recurrent neural networks
(RNNs) to simultaneously address AEC, NS, and DR. We conduct several ablation
studies to analyze the contributions of different components of our model to
the overall performance. DeepVQE achieves state-of-the-art performance on
non-personalized tracks from the ICASSP 2023 Acoustic Echo Cancellation
Challenge and ICASSP 2023 Deep Noise Suppression Challenge test sets, showing
that a single model can handle multiple tasks with excellent performance.
Moreover, the model runs in real-time and has been successfully tested for the
Microsoft Teams platform.