Most Bayesian clean speech estimators, like the Wiener filter or Ephraim and Malah's amplitude estimators, are derived under the assumption that the true power spectral density (PSD) of speech is known. In practice, however, only estimates are available. When the PSD estimation errors are neglected, they propagate through to the final speech estimate, resulting in undesired artifacts such as musical noise and speech distortions. To increase the robustness to PSD estimation errors, recently a linear estimator has been proposed that explicitly takes into account the uncertainty of the available speech PSD estimate. In this paper, we show that in the derivation of this estimator a limiting statistical assumption is made, and that avoiding this assumption leads to a novel, potentially more powerful nonlinear estimator under PSD uncertainty. In combination with a sophisticated speech PSD estimator, the proposed approach achieves a higher predicted speech quality than the linear alternative and its conventional counterpart, the Wiener filter.

We present in this paper two loss functions tailored for rare audio event detection in audio streams. The weighted loss is designed to tackle the common issue of imbalanced data in background/foreground classification while the multi-task loss enables the networks to simultaneously model the class distribution and the temporal structures of the target events for recognition. We study the proposed loss functions with deep neural networks (DNNs) and convolutional neural networks (CNNs) coupled with state-of-the-art phase-aware signal enhancement. Experiments on the DCASE 2017 challenge's data show that our system with the proposed losses significantly outperforms not only the DCASE 2017 baseline but also our baseline which has a similar network architecture and a standard loss function.

Many well-known and frequently employed Bayesian clean speech estimators have been derived under the assumption that the true power spectral densities (PSDs) of speech and noise are exactly known. In practice, however, only power spectral density (PSD) estimates are available. Simply neglecting PSD estimation errors and handling the estimates as true values leads to speech estimation errors causing musical noise and undesired suppression of speech. In this paper, the uncertainty of the available speech PSD estimates is addressed. The main contributions are the following. First, we summarize and examine ways to model and incorporate the uncertainty of PSD estimates for a more robust speech enhancement performance. Second, a novel nonlinear clean speech estimator is derived that takes into account prior knowledge about the absolute value of typical speech PSDs. Third, we show that the derived statistical framework provides uncertainty-aware counterparts to a number of well-known conventional clean speech estimators such as the Wiener filter and Ephraim and Malah's amplitude estimators. Fourth, we show how modern PSD estimators can be incorporated into the theoretical framework and propose to employ frequency dependent priors. Finally, the effects and benefits of considering the uncertainty of speech PSD estimates are analyzed, discussed, and evaluated via instrumental measures and a listening experiment.

Conventional statistical clean speech estimators, like the Wiener filter, are frequently used for the spectro-temporal enhancement of noise corrupted speech. Most of these approaches estimate the clean speech independently for each time-frequency point, neglecting the structure of the underlying speech sound. In this work, we derive a statistical estimator that explicitly takes into account information about the characteristic structure of voiced speech by means of a harmonic signal model. To this end, we also present a way to estimate a harmonic model-based clean speech representation and the corresponding error variance directly in the short-time Fourier transform domain. The resulting estimator is optimal in the minimum-mean-squared error sense and can conveniently be formulated in terms of a multichannel Wiener filter. The proposed estimator outperforms several reference algorithms in terms of speech quality and intelligibility as predicted by instrumental measures.

Among the most commonly used single-channel approaches for the enhancement of noise corrupted speech are Bayesian estimators of clean speech coefficients in the short-time Fourier transform domain. However, the vast majority of these approaches effectively only modifies the spectral amplitude and does not consider any information about the clean speech spectral phase. More recently, clean speech estimators that can utilize prior phase information have been proposed and shown to lead to improvements over the traditional, phase-blind approaches. In this work, we revisit phase-aware estimators of clean speech amplitudes and complex coefficients. To complete the existing set of estimators, we first derive a novel amplitude estimator given uncertain prior phase information. Second, we derive a closed-form solution for complex coefficients when the prior phase information is completely uncertain or not available. We put the novel estimators into the context of existing estimators and discuss their advantages and disadvantages.

For the enhancement of speech degraded by noise, accurate estimation of the noise power spectral density (PSD) is indispensable, especially if only a single microphone signal is available. Fast and accurate tracking of the noise PSD is particularly challenging in highly non-stationary noise types, since the distinction between speech and noise components becomes more difficult. Short-time discrete Fourier transform (STFT) based noise PSD estimation algorithms which employ estimates of the speech presence probability (SPP) with fixed priors have been shown to yield good tracking performance even in adverse noise conditions. In this paper, we compare two methods to incorporate spectro-temporal correlations to improve the tracking performance. The first method smoothes the noisy observation over time and frequency before computing the SPP, while the second is based on a Hidden Markov Model (HMM) of the speech presence and absence states. We show that the proposed modifications lead to improved noise PSD estimators which are less sensitive to spectral outliers of the noise and track changes in the noise PSD more quickly than the reference method. Further, when employed in a common speech enhancement setup, the proposed estimators achieve an increased noise reduction while keeping speech distortions at a comparable level.

In this paper, we consider single-channel speech enhancement in the short time Fourier transform (STFT) domain. We suggest to improve an STFT phase estimate by estimating the initial phases. The method is based on the harmonic model and a model for the phase evolution over time. The initial phases are estimated by setting up a least squares problem between the noisy phase and the model for phase evolution. Simulations on synthetic and speech signals show a decreased error on the phase when an estimate of the initial phase is included compared to using the noisy phase as an initialisation. The error on the phase is decreased at input SNRs from -10 to 10 dB. Reconstructing the signal using the clean amplitude, the mean squared error is decreased and the PESQ score is increased.

In this paper, we focus on methods for estimating the a posteriori probability of a signal segment being voiced which employ a harmonic signal model. Fisher et al. [1] present two likelihood functions for voiced and unvoiced speech from which the posterior probability can be derived. However, due to the chosen models, the a posteriori probability of a signal segment being voiced does not go to 0 % in unvoiced speech. Thus, a novel algorithm is proposed, which incorporates the expected unvoiced speech energy and allows for obtaining low probabilities. Further, it explicitly models the statistics of the segment energy and employs a state-of-the-art noise tracker. Experiments which were conducted on the TIMIT database for different noise types and noise levels show that the proposed method results in lower over-estimation and under-estimation of the voicing probability as compared to [1].

The enhancement of speech which is corrupted by noise is commonly performed in the short-time discrete Fourier transform domain. In case only a single microphone signal is available, typically only the spectral amplitude is modified. However, it has recently been shown that an improved spectral phase can as well be utilized for speech enhancement, e.g., for phase-sensitive amplitude estimation. In this paper, we therefore present a method to reconstruct the spectral phase of voiced speech from only the fundamental frequency and the noisy observation. The importance of the spectral phase is highlighted and we elaborate on the reason why noise reduction can be achieved by modifications of the spectral phase. We show that, when the noisy phase is enhanced using the proposed phase reconstruction, instrumental measures predict an increase of speech quality over a range of signal to noise ratios, even without explicit amplitude enhancement.

In this letter, we derive a minimum mean squared error (MMSE) optimal estimator for clean speech spectral amplitudes, which we apply in single channel speech enhancement. As opposed to state-of-the-art estimators, the optimal estimator is derived for a given clean speech spectral phase. We show that the phase contains additional information that can be exploited to distinguish outliers in the noise from the target signal. With the proposed technique, incorporating the phase can potentially improve the PESQ-MOS by 0.5 in babble noise as compared to state-of-the-art amplitude estimators. In a blind setup we achieve a PESQ improvement of around 0.25 in voiced speech.

We propose a novel, robust estimator for the probability of speech presence at each time-frequency point in the short-time discrete Fourier domain. While existing estimators perform quite reliably in stationary noise environments, they usually exhibit a large false-alarm rate in nonstationary noise that results in a great deal of noise leakage when applied to a speech enhancement task. The proposed estimator overcomes this problem by temporally smoothing the cepstrum of the a posteriori signal-to-noise ratio (SNR), and yields considerably less noise leakage and low speech distortions in both, stationary and nonstationary noise as compared to state-of-the-art estimators. Especially in babble noise, this results in large SNR improvements.

The enhancement of short-term spectra of noisy speech can be achieved by statistical estimation of the clean speech spectral components. We present a minimum mean-square error estimator of the clean speech spectral magnitude that uses both a parametric compression function in the estimation error criterion and a parametric prior distribution for the statistical model of the clean speech magnitude. The novel parametric estimator has many known magnitude estimators as a special solution and, additionally, affords estimators that combine the beneficial properties of different known solutions. The new estimator is evaluated in terms of segmental SNR, speech distortion, and noise suppression.