Abstract: In the context of mobile telephony there is a need for low resource, computationally efficient noise compensation and speech enhancement approaches. This paper assesses the performance of efficient quantile-based noise estimation integrated into a nonlinear spectral subtraction framework. The approach has been implemented in real-time with minimal latency on a 500Mhz processor and is well within the processing capabilities. Experiments are reported on the AURORA 2 and AURORA 3 corpa. Results show an average relative improvement of 15% on the clean and multicondition training sets of the AURORA 2 database and an overall average relative improvement of 20% across the four AURORA 3 databases. It is acknowledged that these are not state-of-the-art results and further optimisation is anticipated.

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