SE-AGCNet: An End-to-End Framework for Joint Speech Enhancement and Loudness Control in Meeting Scenarios
Jinming Zhang, Wei Rao, Xionghu Zhong, Eng Siong Chng
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Key claim
SE-AGCNet improves speech quality and ASR accuracy.
In plain English
Imagine you're trying to improve the clarity of speech in a noisy environment, like a crowded meeting. Traditionally, you might use separate tools to enhance the speech and control the volume, but this can lead to problems. For example, if you boost the volume before enhancing the speech, you might just amplify the background noise instead. On the other hand, if you focus too much on enhancing the speech, you could end up making quiet voices too soft to hear. This is what's called a failure mode in audio processing, where the separate modules don't work well together and can actually make things worse.
To solve this, the authors propose a new approach that combines speech enhancement and automatic gain control into one system called SE-AGCNet. The idea is that by working together, these two tasks can support each other: enhancing the speech helps the volume control work better, especially in situations where the volume varies a lot. They also introduce a new way to simulate data for training this system and use standardized metrics to evaluate loudness.
In practice, this means that SE-AGCNet can maintain the right loudness levels while improving the quality of the speech and the accuracy of automatic speech recognition. This is a significant improvement over previous methods, making it a valuable tool for anyone working with audio in dynamic environments.
The proposed SE-AGCNet framework meaningfully extends existing audio processing methods by integrating speech enhancement and automatic gain control into a single optimized system.
The claims are well-supported by experiments demonstrating improved performance over competitive baselines and the use of standardized evaluation metrics.
Deep reliability assessment
The methodology, as shown in the provided text, supports the claim that jointly training speech enhancement and AGC is a plausible way to avoid failure modes of cascaded pipelines in simulated meeting-room audio. The broader claim that this will robustly improve real meeting deployments is less supported unless the full paper shows strong real-room, real-device, latency, and generalization evaluations beyond the SE-AGC-DataGen simulation setup.
Reproducibility
Partially reproducible: the paper mentions 'Code and demo' at a public project URL, and proposes SE-AGC-DataGen as a reproducible simulation pipeline, but the provided excerpt does not confirm whether pretrained models, generated datasets, scripts, or exact training recipes are fully released.
Key figure
Figure 1 shows a two-stage STFT-domain pipeline where noisy, volume-unbalanced input audio passes through a speech enhancement module, RMS normalization, and an AGC module, with joint training against separate SE and AGC target audios before ISTFT reconstruction.