🤖 AI Summary
This work addresses the challenge of flexibly balancing algorithmic latency and enhancement quality in streaming speech enhancement, where existing approaches are largely confined to binary choices between causal and non-causal processing. The authors propose a unified framework supporting continuously adjustable latency through asymmetric temporal padding, a dual-buffer mechanism comprising state and look-ahead buffers, and selective state updates. This design enables consistent multi-latency configurations during both training and inference within a single model. Under a strict 1.37M parameter budget, the proposed model achieves latencies ranging from 12.5 to 75.0 ms with a PESQ score of 3.43—outperforming the baseline by 0.08—and demonstrates superior performance at the 12.5 ms causal setting compared to current state-of-the-art causal models.
📝 Abstract
Streaming speech enhancement requires balancing algorithmic latency against quality, yet existing approaches largely treat this as a binary causal versus non-causal choice. LaCo-SENet addresses this issue with two mechanisms parameterized by a single training-time hyperparameter. First, asymmetric temporal padding redistributes past and future context in convolutions, enabling systematic latency configuration. Second, dual-buffer streaming combines state buffers for past context with lookahead buffers that supply future context at both the input and feature levels. Selective state updates also prevent future-frame leakage into the streaming state, ensuring training-inference consistency. On VoiceBank+DEMAND, a fixed-budget (1.37M parameters) backbone yields a family of models spanning 12.5-75.0 ms, with PESQ rising from 3.35 to 3.43. At just 12.5 ms (fully causal), a PESQ of 3.35 matches or exceeds the prior causal state-of-the-art (3.27 at 46.5 ms).