Dual-Channel Speech Enhancement Based on Extended Kalman Filter Relative Transfer Function Estimation

This paper deals with speech enhancement in dual-microphone smartphones using beamforming along with postfiltering techniques. The performance of these algorithms relies on a good estimation of the acoustic channel and speech and noise statistics. In this work we present a speech enhancement system that combines the estimation of the relative transfer function (RTF) between microphones using an extended Kalman filter framework with a novel speech presence probability estimator intended to track the noise statistics’ variability. The available dual-channel information is exploited to obtain more reliable estimates of clean speech statistics. Noise reduction is further improved by means of postfiltering techniques that take advantage of the speech presence estimation. Our proposal is evaluated in different reverberant and noisy environments when the smartphone is used in both close-talk and far-talk positions. The experimental results show that our system achieves improvements in terms of noise reduction, low speech distortion and better speech intelligibility compared to other state-of-the-art approaches.Spanish MINECO/FEDER Project TEC2016-80141-PSpanish Ministry of Education through the National Program FPU under Grant FPU15/0416

EMD-based filtering (EMDF) of low-frequency noise for speech enhancement

An Empirical Mode Decomposition based filtering (EMDF) approach is presented as a post-processing stage for speech enhancement. This method is particularly effective in low frequency noise environments. Unlike previous EMD based denoising methods, this approach does not make the assumption that the contaminating noise signal is fractional Gaussian Noise. An adaptive method is developed to select the IMF index for separating the noise components from the speech based on the second-order IMF statistics. The low frequency noise components are then separated by a partial reconstruction from the IMFs. It is shown that the proposed EMDF technique is able to suppress residual noise from speech signals that were enhanced by the conventional optimallymodified log-spectral amplitude approach which uses a minimum statistics based noise estimate. A comparative performance study is included that demonstrates the effectiveness of the EMDF system in various noise environments, such as car interior noise, military vehicle noise and babble noise. In particular, improvements up to 10 dB are obtained in car noise environments. Listening tests were performed that confirm the results

On the application of minimum noise tracking to cancel cosine shaped residual noise

It has been shown recently that for coherence based dual microphone array speech enhancement systems, cross-spectral subtraction is an efficient technique aimed to reduce the correlated noise components. The zero-phase filtering criterion employed in these methods is derived from the standard coherence function that is modified to incorporate the noise cross power spectrum between the two channels. However, there has been limited success at applying coherence based filters when speech processing is carried out under relatively harsh acoustic conditions (SNR below -5dB) or when the speech and noise sources are closely spaced. We propose an alternative method that is effective, and that attempts to use a phase-based filtering criterion by substituting the cross power spectrum of the noisy signals received on the two channels by its real part. Then, a variant of the running minimum noise tracking procedure is applied on the estimated speech spectrum as an adaptive postfiltering to reduce the cosine shaped power spectrum of the remaining residual musical noise to a minimum spectral floor. Using that adaptive postfilter, a softdecision scheme is implemented to control the amount of noise suppression. Our preliminary results based on experiments conducted on real speech signals show an improved performance of the proposed method over the coherence based approaches. These results also show that it performs well on speech while producing less spectral distortion even in severe noisy conditions

Postfiltering Using Multichannel Spectral Estimation in Multispeaker Environments

This paper investigates the problem of enhancing a single desired speech source from a mixture of signals in multispeaker environments. A beamformer structure is proposed which combines a fixed beamformer with postfiltering. In the first stage, the fixed multiobjective optimal beamformer is designed to spatially extract the desired source by suppressing all other undesired sources. In the second stage, a multichannel power spectral estimator is proposed and incorporated in the postfilter, thus enabling further suppression capability. The combined scheme exploits both spatial and spectral characteristics of the signals. Two new multichannel spectral estimation methods are proposed for the postfiltering using, respectively, inner product and joint diagonalization. Evaluations using recordings from a real-room environment show that the proposed beamformer offers a good interference suppression level whilst maintaining a low-distortion level of the desired source