Practical Solutions and Value of TRAMBA for Mobile and Wearable Platforms
Introduction
Wearables have revolutionized health monitoring and the market is projected to grow significantly. However, background noise compromises speech quality in head-worn devices.
Challenges and Solutions
Traditional methods for separating speech from background noise face limitations in diverse noisy environments. Bone conduction microphones offer noise robustness but suffer from frequency attenuation. TRAMBA addresses these challenges by pre-training with widely available audio speech datasets and fine-tuning with bone conduction data, enabling real-time speech super-resolution and reduced power consumption.
Technical Details
TRAMBA integrates a modified U-Net structure with self-attention and Mamba in the narrow bottleneck layer. It operates on 512ms windows of single-channel audio and preprocesses acceleration data from an accelerometer.
Performance and Comparison
TRAMBA outperforms other models in various metrics and sampling rates, demonstrating superior speech quality in noisy environments. It exhibits efficient processing for real-time operation and a compact memory footprint.
Conclusion and Impact
TRAMBA represents a crucial advancement for integrating speech enhancement into practical mobile and wearable platforms, extending battery life and enhancing speech quality in noisy environments.
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