Benchmarking MLCommons Tiny Audio Denoising with Deployability Constraints

Speech enhancement is a critical field in audio signal processing given its essentiality to overcome obstacles related to loud and damaged speech signals. Due to the revolutionary capa-bilities of Deep Learning (DL) models, there has been significant interest on benchmarking them and studying their suitability for tiny embedded systems. In this paper, we thoroughly examine the growing field of voice improvement, with a specific emphasis on the use of DL-based techniques under consideration by the MLCommons standardization. In particular, among the others, the Legendre Memory Unit (LMU) model achieves an average Scale-Invariant Signal-to-Distortion Ratio (SISDR) on 8.613 in 627 KiB of FLASH memory, making it deployable on tiny microcontrollers by requiring only 7 ms per inference run.