In this work, we present a universal codebook-based speech enhancement framework that relies on a single codebook to encode both speech and noise components. The atomic speech presence probability (ASPP) is defined as the probability that a given codebook atom encodes speech at a given point in time. We develop ASPP estimators based on binaural cues including the interaural phase and level difference (IPD and ILD), the interaural coherence magnitude (ICM), as well as a combined version leveraging the full interaural transfer function (ITF). We evaluate the performance of the resulting ASPP-based speech enhancement algorithms on binaural mixtures of reverberant speech and real-world noise. The proposed approach improves both objective speech quality and intelligibility over a wide range of input SNR, as measured with PESQ and binaural STOI metrics, outperforming two binaural speech enhancement benchmark methods. We show that the proposed ITF-based ASPP approach achieves a good balance of the trade-off between binaural noise reduction and binaural cue preservation.

The problem of estimating the a priori signal-to-noise ratio (SNR) for single-channel speech enhancement is addressed. Similar to the decision-directed approach we linearly combine the maximum likelihood estimate of the a priori SNR with an estimate obtained from the previous frame. Based on the harmonic model for voiced speech we propose to smooth the a priori SNR estimate along harmonic trajectories instead of fixed discrete Fourier transform frequency bins. We interpolate by using a pitch-adaptive zero-padding in order to obtain the spectral coefficients at harmonic frequencies. The resulting pitch-adaptive decision-directed (PADDi) method increases the noise attenuation compared to the classical decision-directed approach and outperforms benchmark methods in terms of speech enhancement performance for several noise types at different SNRs, Quantified by objective evaluation criteria.

Speech enhancement methods formulated in the short-time Fourier transform (STFT) domain vary in the statistical assumptions made on the STFT coefficients, in the optimization criteria applied or in the models of the signal components. Recently, approaches relying on a stochastic-deterministic speech model have been proposed. The deterministic part of the signal corresponds to harmonically related sinusoids, often used to represent voiced speech. The stochastic part models signal components that are not captured by the deterministic components. In this paper, we consider this scenario under a new perspective yielding three main contributions. First, a pitch-synchronous signal representation is considered and shown to be advantageous for the estimation of the harmonic model parameters. Second, we model the harmonic amplitudes in voiced speech as random variables with frequency bin dependent Gamma distributions. Finally, distinct estimators for the different models of voiced speech, unvoiced speech, and speech absence are derived. To select from the arising estimates, we take into account the mutual impact of detection and estimation by proposing a binary decision framework that is derived from a Bayesian risk function. The resulting pitch-synchronous stochastic-deterministic estimator outperforms several benchmark methods in terms of speech intelligibility and perceived quality predicted by instrumental measures for various noise types and different signal-to-noise ratios.

Phase-aware signal processing has received increasing interest in many speech applications. The success of phase-aware processing depends strongly on the robustness of the clean spectral phase estimates to be obtained from a noisy observation. In this paper, we propose a novel harmonic phase estimator relying on the phase invariance property exploiting relations between harmonics using the phase structure. We present speech quality results achieved in speech enhancement to justify the effectiveness of the proposed phase estimator compared to noisy phase and other phase estimation benchmarks.