Abstract: Speech enhancement is provided in dual microphone noise reduction systems by including spectral subtraction algorithms using linear convolution, causal filtering and/or spectrum dependent exponential averaging of the spectral subtraction gain function. According to exemplary embodiments, when a far-mouth microphone is used in conjunction with a near-mouth microphone, it is possible to handle non-stationary background noise as long as the noise spectrum can continuously be estimated from a single block of input samples. The far-mouth microphone, in addition to picking up the background noise, also picks up the speaker's voice, albeit at a lower level than the near-mouth microphone. To enhance the noise estimate, a spectral subtraction stage is used to suppress the speech in the far-mouth microphone signal. To be able to enhance the noise estimate, a rough speech estimate is formed with another spectral subtraction stage from the near-mouth signal.

Abstract: Methods and apparatus for providing speech enhancement in noise reduction systems include spectral subtraction algorithms using linear convolution, causal filtering and/or spectrum dependent exponential averaging of the spectral subtraction gain function. According to exemplary embodiments, low order spectrum estimates are developed which have less frequency resolution and reduced variance as compared to spectrum estimates in conventional spectral subtraction systems. The low order spectra are used to form a gain function having a desired low variance which in turn reduces musical tones in the spectral subtraction output signal. Advantageously, the gain function can be further smoothed across blocks using input spectrum dependent exponential averaging. Additionally, the low order of the gain function permits a phase to be added during interpolation so that the spectral subtraction gain filter is causal and prevents discontinuities between blocks.