Recording a meeting and obtaining clean speech signals of each speaker is a challenging task. Even with a multichannel recording, in which all speakers are equipped with a close-talk microphone, speech of an active speaker still couples not only into his dedicated microphone, but also into all other microphone channels at a certain level. This is denoted as crosstalk and requires a multichannel speaker interference reduction to enhance the microphone channels for further processing. To solve this issue, we use a Wiener filter which is based on all individual microphone channels. We extend an existing approach by integrating methods from acoustic echo cancellation to improve the estimation of the interferer (noise) components of the filter, which leads to an improved signal-to-interferer ratio by up to 2.1 dB absolute at constant speech component quality.

Estimating time-frequency domain masks for speech enhancement using deep learning approaches has recently become a popular field in research. In this paper, we propose a novel components loss (CL) for the training of neural networks for speech enhancement. During the training process, the proposed CL offers separate control over suppression of the noise component and preservation of the speech component. We illustrate the potential of the proposed CL by example of a convolutional neural network (CNN) for mask-based speech enhancement. We show improvement in almost all employed instrumental quality metrics over the baseline losses, which comprises the conventional mean squared error (MSE) loss and also perceptual evaluation of speech quality (PESQ) loss. On average, more than 0.3 dB higher SNR improvement and an at least 0.1 points higher PESQ score on the speech component are obtained. In addition to that, a more naturally sounding residual noise and a consistently best PESQ on the enhanced speech is obtained. All improvements are more distinct at low SNR conditions.

This contribution aims at speech model-based speech enhancement by exploiting the source-filter model of human speech production. The proposed method enhances the excitation signal in the cepstral domain by making use of a deep neural network (DNN). We investigate two types of target representations along with the significant effects of their normalization. The new approach exceeds the performance of a formerly introduced classical signal processing-based cepstral excitation manipulation (CEM) method in terms of noise attenuation by about 1.5 dB. We show that this gain also holds true when comparing serial combinations of envelope and excitation enhancement. In the important low-SNR conditions, no significant trade-off for speech component quality or speech intelligibility is induced, while allowing for substantially higher noise attenuation. In total, a traditional purely statistical state-of-the-art speech enhancement system is outperformed by more than 3 dB noise attenuation.

A priori signal-to-noise ratio (SNR) contains critical information about the single-channel mixture of a speech and noise signal, and can be used by speech enhancement algorithms. In this paper, we propose a novel a priori SNR estimator using the estimates obtained from discriminative non-negative matrix factorization (DNMF). The idea of our new approach is to utilize the DNMF to perform the preliminary speech components estimation, which can be either directly used to estimate the a priori SNR, or can be combined with the well-known decision-directed (DD) approach by Ephraim and Malah to perform the a priori SNR estimation. We present a speaker-independent but noise-dependent DNMF-based a priori SNR estimator. Speech enhancement simulation results in the presence of non-stationary noise validate our new approach combined with well-known spectral weighting rules, outperforming several NMF-based and non-NMF-based state-of-the-art methods, w.r.t. both SNR improvement and speech perceptual quality.

In this paper, we propose and compare various techniques for the estimation of clean spectral envelopes in noisy conditions. The source-filter model of human speech production is employed in combination with a hidden Markov model and/or a deep neural network approach to estimate clean envelope-representing coefficients in the cepstral domain. The cepstral estimators for speech spectral envelope-based noise reduction are both evaluated alone and also in combination with the recently introduced cepstral excitation manipulation (CEM) technique for a priori SNR estimation in a noise reduction framework. Relative to the classical MMSE short time spectral amplitude estimator, we obtain more than 2 dB higher noise attenuation, and relative to our recent CEM technique still 0.5 dB more, in both cases maintaining the quality of the speech component and obtaining considerable SNR improvement.

As the a priori signal-to-noise ratio (SNR) contains crucial information about a signal's mixture of speech and noise, its estimation is subject to steady research. In this paper, we introduce a novel a priori SNR estimator based on synthesizing an idealized excitation signal in the cepstral domain. Our approach utilizes a source-filter decomposition in combination with a cepstral excitation manipulation in order to recreate an idealized excitation, which is subsequently shaped by an immanent envelope. In contrast to the well-known decision-directed approach by Ephraim and Malah, an instantaneous estimate is obtained, which is less prone to sudden acoustic environmental changes and musical noise. Additionally, the proposed estimator is able to preserve weak harmonic structures resulting in a spectrum that is more full-bodied. We present both a speaker-independent and a speaker-dependent variant of the new a priori SNR estimator, both showing more than 2 dB ΔSNR improvement versus state of the art, without any significant increase in speech distortion.