Anti-Aliasing Framework for Time-Domain Speech Enhancement

Since the introduction of deep learning, research on speech enhancement has been developing rapidly. Until a few years ago, time-domain models performed poorly compared to time-frequency models. However, recent time-domain models such as DEMUCS, MANNER, and SE-Conformer have achieved competitive performance by adopting U-Net encoder-decoder architectures with skip connections. Despite this progress, these U-Net-based models have an overlooked weakness: aliasing artifacts from down-sampling and up-sampling operations. Prior work has shown that max pooling and strided convolutions introduce aliasing, which appears as network-generated high-frequency noise in the output. This contradicts the fundamental goal of speech enhancement noise reduction. Therefore, applying anti-aliasing methods can be useful for further improving time-domain models. We systematically apply anti-aliasing methods (BlurPooling, sub-pixel convolution with ICNR initialization) to MANNER, a time-domain speech enhancement model, on the VoiceBank+DEMAND dataset. Our results show improvements in perceptual quality metrics. Our contributions are threefold. First, we investigate aliasing artifacts caused by down-sampling and up-sampling operations in U-Net-based time-domain speech enhancement models. Second, we formulate a unified 1D anti-aliasing framework that integrates existing anti-aliasing operations, including BlurPooling, sub-pixel convolution, and ICNR initialization, into waveform-domain speech enhancement architectures. Third, we provide empirical validation on the framework through objective metrics, ablation studies, an additional Demucs experiment, and spectrogram/error-spectrogram visualizations.