Lightweight speech enhancement with state-space model and depthwise separable convolution

Key Points

• Speech enhancement achieved notable clarity and performance improvement using a depthwise separable convolution approach.
• The results showed a 30% increase in speech intelligibility compared to traditional methods when tested on diverse datasets.
• Analysis employed signal processing techniques and advanced neural networks to optimize the speech enhancement quality.
• Improvements highlight the potential for this lightweight model in real-world applications, particularly in noisy environments.