Modulation-domain speech enhancement using a kalman filter with a bayesian update of speech and noise in the log-spectral domain

We present a Bayesian estimator that performs log-spectrum esti- mation of both speech and noise, and is used as a Bayesian Kalman filter update step for single-channel speech enhancement in the mod- ulation domain. We use Kalman filtering in the log-power spectral domain rather than in the amplitude or power spectral domains. In the Bayesian Kalman filter update step, we define the posterior dis- tribution of the clean speech and noise log-power spectra as a two- dimensional multivariate Gaussian distribution. We utilize a Kalman filter observation constraint surface in the three-dimensional space, where the third dimension is the phase factor. We evaluate the re- sults of the phase-sensitive log-spectrum Kalman filter by comparing them with the results obtained by traditional noise suppression tech- niques and by an alternative Kalman filtering technique that assumes additivity of speech and noise in the power spectral domain

Phase-aware single-channel speech enhancement with modulation-domain Kalman filtering

We present a speech enhancement algorithm that performs modulation-domain Kalman filtering to track the speech phase using circular statistics, along with the log-spectra of speech and noise. In the proposed algorithm, the speech phase posterior is used to create an enhanced speech phase spectrum for the signal reconstruction of speech. The Kalman filter prediction step separately models the temporal inter-frame correlation of the speech and noise spectral log-amplitudes and of the speech phase, while the Kalman filter update step models their nonlinear relations under the assumption that speech and noise add in the complex short-time Fourier transform domain. The phase-sensitive enhancement algorithm is evaluated with speech quality and intelligibility metrics, using a variety of noise types over a range of SNRs. Instrumental measures predict that tracking the speech log-spectrum and phase with modulation-domain Kalman filtering leads to consistent improvements in speech quality, over both conventional enhancement algorithms and other algorithms that perform modulation-domain Kalman filtering

Corticosterone alters materno-fetal glucose partitioning and insulin signalling in pregnant mice.

Glucocorticoids affect glucose metabolism in adults and fetuses, although their effects on materno-fetal glucose partitioning remain unknown. The present study measured maternal hepatic glucose handling and placental glucose transport together with insulin signalling in these tissues in mice drinking corticosterone either from day (D) 11 to D16 or D14 to D19 of pregnancy (term = D21). On the final day of administration, corticosterone-treated mice were hyperinsulinaemic (P 0.05). Insulin receptor and insulin-like growth factor type I receptor abundance did not differ with treatment in either tissue. Corticosterone upregulated the stress-inducible mechanistic target of rapamycin (mTOR) suppressor, Redd1, in liver (D16 and D19) and placenta (D19), in ad libitum fed animals (P < 0.05). Concomitantly, hepatic protein content and placental weight were reduced on D19 (P < 0.05), in association with altered abundance and/or phosphorylation of signalling proteins downstream of mTOR. Taken together, the data indicate that maternal glucocorticoid excess reduces fetal growth partially by altering placental glucose transport and mTOR signalling.The studies described in this manuscript were supported by a graduate studentship to ORV from the Centre for Trophoblast Research in Cambridge.This is the accepted manuscript of a paper published in The Journal of Physiology Volume 593, Issue 5, pages 1307–1321, 1 March 2015, DOI: 10.1113/jphysiol.2014.28717

Active speech level estimation in noisy signals with quadrature noise suppression

We present a noise-robust algorithm for estimating the active level of speech, which is the average speech power during intervals of speech activity. The proposed algorithm uses the clean speech phase to remove the quadrature noise component from the short-time power spectrum of the noisy speech, as well as SNR-dependent techniques to improve the estimation. The pitch of voiced speech frames is determined using a noise-robust pitch tracker and the speech level is estimated from the energy of the pitch harmonics using the harmonic summation principle. At low noise levels, the resultant active speech level estimate is combined with that from the standardized ITU-T P.56 algorithm to give a final composite estimate. The algorithm has been evaluated using a range of noise signals and gives consistently lower errors than previous methods and than the ITU-T P.56 algorithm, which is accurate for SNR levels of above 15 dB

Modulation-domain Kalman filtering for monaural blind speech denoising and dereverberation

We describe a monaural speech enhancement algorithm based on modulation-domain Kalman filtering to blindly track the time-frequency log-magnitude spectra of speech and reverberation. We propose an adaptive algorithm that performs blind joint denoising and dereverberation, while accounting for the inter-frame speech dynamics, by estimating the posterior distribution of the speech log-magnitude spectrum given the log-magnitude spectrum of the noisy reverberant speech. The Kalman filter update step models the non-linear relations between the speech, noise and reverberation log-spectra. The Kalman filtering algorithm uses a signal model that takes into account the reverberation parameters of the reverberation time, T60, and the direct-to-reverberant energy ratio (DRR) and also estimates and tracks the T60 and the DRR in every frequency bin to improve the estimation of the speech log-spectrum. The proposed algorithm is evaluated in terms of speech quality, speech intelligibility and dereverberation performance for a range of reverberation parameters and reverberant speech to noise ratios, in different noises, and is also compared to competing denoising and dereverberation techniques. Experimental results using noisy reverberant speech demonstrate the effectiveness of the enhancement algorithm

Speech enhancement using kalman filtering in the logarithmic bark power spectral domain

We present a phase-sensitive speech enhancement algorithm based on a Kalman filter estimator that tracks speech and noise in the logarithmic Bark power spectral domain. With modulation-domain Kalman filtering, the algorithm tracks the speech spectral log-power using perceptually-motivated Bark bands. By combining STFT bins into Bark bands, the number of frequency components is reduced. The Kalman filter prediction step separately models the inter-frame relations of the speech and noise spectral log-powers and the Kalman filter update step models the nonlinear relations between the speech and noise spectral log-powers using the phase factor in Bark bands, which follows a sub-Gaussian distribution. The posterior mean of the speech spectral log-power is used to create an enhanced speech spectrum for signal reconstruction. The algorithm is evaluated in terms of speech quality and computational complexity with different algorithm configurations compared on various noise types. The algorithm implemented in Bark bands is compared to algorithms implemented in STFT bins and experimental results show that tracking speech in the log Bark power spectral domain, taking into account the temporal dynamics of each subband envelope, is beneficial. Regarding the computational complexity, the percentage decrease in the real-time factor is 44% when using Bark bands compared to when using STFT bins