Prediction based filtering and smoothing to exploit temporal dependencies in NMF

Mohammadiha, Nasser; Smaragdis, Paris; Leijon, Arne

doi:10.1109/icassp.2013.6637773

Cited by 15 publications

(27 citation statements)

References 15 publications

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“…The addition of temporal modeling in other NMF-based methods [23,24] has been shown to improve noticeably source separation quality. Other potential improvements include the adaptive selection of some of the parameters, such as the number of noise spectral features KN or the sparsity parameter λ.…”

Section: Resultsmentioning

confidence: 99%

Speaker and noise independent online single-channel speech enhancement

Germain

Mysore

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Desirable properties of real-world speech enhancement methods include online operation, single-channel operation, operation in the presence of a variety of noise types including non-stationary noise, and no requirement for isolated training examples of the specific speaker and noise type at hand. Methods in the literature typically possess only a subset of these properties. Source separation methods particularly rarely simultaneously possess the first and last properties. We extend universal speech model-based speech enhancement to adaptively learn a noise model in an online fashion. We learn a model from a general corpus of speech in place of speakerdependent training examples before deployment. This setup provides all of these desirable properties, making it easy to deploy in real-world systems without the need to provide additional training examples, while explicitly modeling speech. Our experimental results show that our method achieves the same performance as in the case in which speaker-dependent training data is available.Index Termsonline speech enhancement, non-negative matrix factorization, universal speech models

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Section: Resultsmentioning

confidence: 99%

Speaker and noise independent online single-channel speech enhancement

Germain

Mysore

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…The algorithms in [38] track the time evolution of the clean STFT amplitude domain coefficients in every frequency bin. In [64], speech inter-frame correlation is modeled. Considering KF algorithms, many papers, such as [50] [51] and [53], use the non-linear observation model relating clean and noisy speech in the log-spectral domain.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The modulation domain models the inter-frame correlation of clean speech and does not consider each time-frame independently. In [64], inter-frame speech correlation is modeled and is then followed by NMF. Inter-frame correlations of speech are considered in several papers and books by J. Benesty, i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

Phase-Aware Single-Channel Speech Enhancement With Modulation-Domain Kalman Filtering

Dionelis

Brookes

2018

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-We present a speech enhancement algorithm that performs modulation-domain Kalman filtering to track the speech phase using circular statistics, along with the log-spectra of speech and noise. In the proposed algorithm, the speech phase posterior is used to create an enhanced speech phase spectrum for the signal reconstruction of speech. The Kalman filter prediction step separately models the temporal inter-frame correlation of the speech and noise spectral log-amplitudes and of the speech phase, while the Kalman filter update step models their nonlinear relations under the assumption that speech and noise add in the complex short-time Fourier transform domain. The phasesensitive enhancement algorithm is evaluated with speech quality and intelligibility metrics, using a variety of noise types over a range of SNRs. Instrumental measures predict that tracking the speech log-spectrum and phase with modulation-domain Kalman filtering leads to consistent improvements in speech quality, over both conventional enhancement algorithms and other algorithms that perform modulation-domain Kalman filtering.

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“…A linear nonnegative dynamical system is presented in [38] to model temporal dependencies in NMF. The proposed causal filtering and fixed-lag smoothing algorithms use Kalmanlike prediction in NMF and PLCA.…”

Section: Review Of State-of-the-art Nmf-based Speech Enhancementmentioning

confidence: 99%

Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization

Mohammadiha

Smaragdis

Leijon

2013

IEEE Trans. Audio Speech Lang. Process.

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365

221

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Abstract-Reducing the interference noise in a monaural noisy speech signal has been a challenging task for many years. Compared to traditional unsupervised speech enhancement methods, e.g., Wiener filtering, supervised approaches, such as algorithms based on hidden Markov models (HMM), lead to higher-quality enhanced speech signals. However, the main practical difficulty of these approaches is that for each noise type a model is required to be trained a priori. In this paper, we investigate a new class of supervised speech denoising algorithms using nonnegative matrix factorization (NMF). We propose a novel speech enhancement method that is based on a Bayesian formulation of NMF (BNMF). To circumvent the mismatch problem between the training and testing stages, we propose two solutions. First, we use an HMM in combination with BNMF (BNMF-HMM) to derive a minimum mean square error (MMSE) estimator for the speech signal with no information about the underlying noise type. Second, we suggest a scheme to learn the required noise BNMF model online, which is then used to develop an unsupervised speech enhancement system. Extensive experiments are carried out to investigate the performance of the proposed methods under different conditions. Moreover, we compare the performance of the developed algorithms with state-of-the-art speech enhancement schemes using various objective measures. Our simulations show that the proposed BNMF-based methods outperform the competing algorithms substantially.

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Prediction based filtering and smoothing to exploit temporal dependencies in NMF

Cited by 15 publications

References 15 publications

Speaker and noise independent online single-channel speech enhancement

Speaker and noise independent online single-channel speech enhancement

Phase-Aware Single-Channel Speech Enhancement With Modulation-Domain Kalman Filtering

Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization

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