TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids

Understand

Modern speech enhancement algorithms achieve remarkable noise suppression by means of large recurrent neural networks (RNNs).

• However, large RNNs limit practical deployment in hearing aid hardware (HW) form-factors, which are battery powered and run on resource-constrained microcontroller units (MCUs) with limited memory capacity and compute capability.
• In this work, we use model compression techniques to bridge this gap.
• We define the constraints imposed on the RNN by the HW and describe a method to satisfy them.