463 research outputs found
Recommended from our members
Efficient alternatives to the Ephraim and Malah suppression rule for audio signal enhancement
Audio signal enhancement often involves the application of a time-varying filter, or suppression rule, to the frequency-domain transform of a corrupted signal. Here we address suppression rules derived under a Gaussian model and interpret them as spectral estimators in a Bayesian statistical framework. With regard to the optimal spectral amplitude estimator of Ephraim and Malah, we show that under the same modelling assumptions, alternative methods of Bayesian estimation lead to much simpler suppression rules exhibiting similarly effective behaviour. We derive three of such rules and demonstrate that, in addition to permitting a more straightforward implementation, they yield a more intuitive interpretation of the Ephraim and Malah solution
Model-Based Speech Enhancement in the Modulation Domain
This paper presents an algorithm for modulationdomain speech enhancement using a Kalman filter. The proposed estimator jointly models the estimated dynamics of the spectral amplitudes of speech and noise to obtain an MMSE estimation of the speech amplitude spectrum with the assumption that the speech and noise are additive in the complex domain. In order to include the dynamics of noise amplitudes with those of speech amplitudes, we propose a statistical “Gaussring” model that comprises a mixture of Gaussians whose centres lie in a circle on the complex plane. The performance of the proposed algorithm is evaluated using the perceptual evaluation of speech quality (PESQ) measure, segmental SNR (segSNR) measure and shorttime objective intelligibility (STOI) measure. For speech quality measures, the proposed algorithm is shown to give a consistent improvement over a wide range of SNRs when compared to competitive algorithms. Speech recognition experiments also show that the Gaussring model based algorithm performs well for two types of noise
Single-Microphone Speech Enhancement and Separation Using Deep Learning
The cocktail party problem comprises the challenging task of understanding a
speech signal in a complex acoustic environment, where multiple speakers and
background noise signals simultaneously interfere with the speech signal of
interest. A signal processing algorithm that can effectively increase the
speech intelligibility and quality of speech signals in such complicated
acoustic situations is highly desirable. Especially for applications involving
mobile communication devices and hearing assistive devices. Due to the
re-emergence of machine learning techniques, today, known as deep learning, the
challenges involved with such algorithms might be overcome. In this PhD thesis,
we study and develop deep learning-based techniques for two sub-disciplines of
the cocktail party problem: single-microphone speech enhancement and
single-microphone multi-talker speech separation. Specifically, we conduct
in-depth empirical analysis of the generalizability capability of modern deep
learning-based single-microphone speech enhancement algorithms. We show that
performance of such algorithms is closely linked to the training data, and good
generalizability can be achieved with carefully designed training data.
Furthermore, we propose uPIT, a deep learning-based algorithm for
single-microphone speech separation and we report state-of-the-art results on a
speaker-independent multi-talker speech separation task. Additionally, we show
that uPIT works well for joint speech separation and enhancement without
explicit prior knowledge about the noise type or number of speakers. Finally,
we show that deep learning-based speech enhancement algorithms designed to
minimize the classical short-time spectral amplitude mean squared error leads
to enhanced speech signals which are essentially optimal in terms of STOI, a
state-of-the-art speech intelligibility estimator.Comment: PhD Thesis. 233 page
- …