SE-AGCNet: An End-to-End Framework for Joint Speech Enhancement and Loudness Control in Meeting Scenarios

Researchers propose SE-AGCNet, an end-to-end framework that jointly optimizes speech enhancement and automatic gain control for meeting scenarios. The approach addresses limitations of traditional discrete audio processing pipelines by leveraging synergy between the two tasks, improving speech quality, loudness consistency, and automatic speech recognition accuracy.

SE-AGCNet tackles a fundamental challenge in audio processing pipelines: the interaction between speech enhancement and volume normalization. Traditional approaches treat these as separate sequential steps, creating suboptimal outcomes where early gain control amplifies noise or aggressive speech enhancement distorts quieter speech. This research demonstrates that joint optimization can resolve these tradeoffs through task interdependence.

The work extends beyond algorithmic innovation by introducing SE-AGC-DataGen, a specialized data simulation pipeline addressing the scarcity of realistic training data for meeting scenarios with natural volume variations. By incorporating standardized loudness metrics—integrated loudness (LUFS), short-term loudness, and loudness range—the framework aligns with broadcast and production standards, improving practical deployment viability.

For developers building communication platforms, this represents a meaningful advancement in audio quality. Meeting applications increasingly handle challenging acoustic environments with participants at varying distances and microphone levels. Better joint optimization reduces post-processing artifacts and improves accessibility for users with hearing challenges who rely on consistent loudness normalization.

The implications extend to enterprise communication tools, transcription services, and hearing aid applications where automatic gain control and speech clarity are critical. Improved ASR accuracy directly impacts downstream applications relying on meeting transcription and analysis. The standardized loudness metrics adoption suggests potential for industry standardization rather than proprietary solutions.

• →Joint optimization of speech enhancement and gain control outperforms sequential pipeline approaches in meeting scenarios
• →SE-AGC-DataGen simulation pipeline addresses data scarcity for training on natural volume variations
• →Integration of broadcast-standard loudness metrics (LUFS, LRA) improves production readiness and compliance
• →Improved ASR accuracy and speech quality have applications across transcription, accessibility, and communication platforms
• →Framework synergy preserves quiet speech while achieving consistent target loudness levels