Align-ULCNet: Towards Low-Complexity and Robust Acoustic Echo and Noise Reduction

Abstract

The successful deployment of deep learning-based acoustic echo and noise reduction (AENR) methods in consumer devices has spurred interest in developing low-complexity solutions, while emphasizing the need for robust performance in real-life applications. In this work, we propose a hybrid approach to enhance the state-of-the-art (SOTA) ULCNet model by integrating time alignment and parallel encoder blocks for the model inputs, resulting in better echo reduction and comparable noise reduction performance to existing SOTA methods. We also propose a channel-wise sampling-based feature reorientation method, ensuring robust performance across many challenging scenarios, while maintaining overall low computational and memory requirements.

Evaluation Scenarios

In our work, we evaluate our proposed methods in different scenarios, namely FarEnd SingleTalk, Doubletalk, NearEnd SingleTalk and for NR Task.

Following you can find some processed samples with different Methods:

1. FarEnd SingleTalk --> Go to the samples

2. Doubletalk --> Go to the samples

3. NearEnd SingleTalk --> Go to the samples

4. NR Task --> Go to the samples

1. FarEnd SingleTalk Scenarios

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2. DoubleTalk

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3. NearEnd SingleTalk

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4. NR Task

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