This paper presents a system aiming at joint dereverberation and noise reduction by applying a combination of a beamformer with a single-channel spectral enhancement scheme. First, a minimum variance distortionless response beamformer with an online estimated noise coherence matrix is used to suppress noise and reverberation. The output of this beamformer is then processed by a single-channel spectral enhancement scheme, based on statistical room acoustics, minimum statistics, and temporal cepstrum smoothing, to suppress residual noise and reverberation. The evaluation is conducted using the REVERB challenge corpus, designed to evaluate speech enhancement algorithms in the presence of both reverberation and noise. The proposed system is evaluated using instrumental speech quality measures, the performance of an automatic speech recognition system, and a subjective evaluation of the speech quality based on a MUSHRA test. The performance achieved by beamforming, single-channel spectral enhancement, and their combination are compared, and experimental results show that the proposed system is effective in suppressing both reverberation and noise while improving the speech quality. The achieved improvements are particularly significant in conditions with high reverberation times
Magnetic particle imaging (MPI) is a tracer-based imaging technique that can be used for imaging vessels and organ perfusion with high temporal resolution. Background signals are a major source for image artifacts and in turn restrict the sensitivity of the method in practice. While static background signals can be removed from the measured signal by taking a dedicated background scan and performing subtraction, this simple procedure is not applicable in case of non-stationary background signals that occur in practice due to e.g. temperature drifts in the electromagnetic coils of the MPI scanner. Within this work we will investigate a dynamic background subtraction method that is based on two background measurements taken before and after the object measurement. Using first-order interpolation it is possible to remove linear background changes and in turn significantly suppress artifacts. The method is evaluated using static and dynamic phantom measurements and it is shown that dynamic background subtraction is capable of reducing the artifact level approximately by a factor of four.
This paper presents extended techniques aiming at the improvement of automatic speech recognition (ASR) in single-channel scenarios in the context of the REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge. The focus is laid on the development and analysis of ASR front-end technologies covering speech enhancement and feature extraction. Speech enhancement is performed using a joint noise reduction and dereverberation system in the spectral domain based on estimates of the noise and late reverberation power spectral densities (PSDs). To obtain reliable estimates of the PSDs-even in acoustic conditions with positive direct-to-reverberation energy ratios (DRRs)-we adopt the statistical model of the room impulse response explicitly incorporating DRRs, as well in combination with a novel proposed joint estimator for the reverberation time T 60 and the DRR. The feature extraction approach is inspired by processing strategies of the auditory system, where an amplitude modulation filterbank is applied to extract the temporal modulation information. These techniques were shown to improve the REVERB baseline in our previous work. Here, we investigate if similar improvements are obtained when using a state-of-the-art ASR framework, and to what extent the results depend on the specific architecture of the back-end. Apart from conventional Gaussian mixture model (GMM)-hidden Markov model (HMM) back-ends, we consider subspace GMM (SGMM)-HMMs as well as deep neural networks in a hybrid system. The speech enhancement algorithm is found to be helpful in almost all conditions, with the exception of deep learning systems in matched training-test conditions. The auditory feature type improves the baseline for all system architectures. The relative word error rate reduction achieved by combining our front-end techniques with current back-ends is 52.7% on average with the REVERB evaluation test set compared to our original REVERB result.
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