# Real-Time Speech Enhancement Using Spectral Subtraction with Minimum Statistics and Spectral Floor

arXiv: https://arxiv.org/abs/2302.10313 · HTML: https://mlsanity.com/paper/2302.10313/

## Key facts

- Cited by 1 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 20 Feb 2023.

## Built on by (most cited first, 1 of 1)

- [A Hybrid Classical-Learning Framework for Adaptive Decision Directed Speech Enhancement](https://mlsanity.com/paper/2609.26183/) (2026)

## Abstract (arXiv)

An initial real-time speech enhancement method is presented to reduce the effects of additive noise. The method operates in the frequency domain and is a form of spectral subtraction. Initially, minimum statistics are used to generate an estimate of the noise signal in the frequency domain. The use of minimum statistics avoids the need for a voice activity detector (VAD) which has proven to be challenging to create. As minimum statistics are used, the noise signal estimate must be multiplied by a scaling factor before subtraction from the noise corrupted speech signal can take place. A spectral floor is applied to the difference to suppress the effects of "musical noise". Finally, a series of further enhancements are considered to reduce the effects of residual noise even further. These methods are compared using time-frequency plots to create the final speech enhancement design