The Potential for Speech Intelligibility Improvement Using the Ideal Binary Mask and the Ideal Wiener Filter in Single Channel Noise Reduction Systems: Application to Auditory Prostheses

Madhu, Nilesh; Spriet, Ann; Jansen, Sofie; Koning, Raphael; Wouters, Jan

doi:10.1109/tasl.2012.2213248

Cited by 51 publications

(55 citation statements)

References 27 publications

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“…For intelligibility to be maintained with the IBM, the optimal LC should change according to the overall mixture SNR (as in [5]). Compared with the IBM, the IWF does not depend on the setting of an LC, it is more robust to estimation errors and it is preferred in terms of perceived quality [9,10,22]. Thus, the IWF was expected to present a more reliable training target for speech enhancement purposes.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, the ideal Wiener filter (IWF) (also known as ideal ratio mask, IRM) applies a gradual weight to each T-F unit according to its local SNR and does not depend on the choice of a LC [8,9]. Listening tests conducted with NH listeners have shown that the IWF is less sensitive to estimation errors, leads to higher intelligibility scores in low SNR conditions, and is preferred in terms of perceived quality compared with the IBM [10].…”

Section: Introductionmentioning

confidence: 99%

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Speech enhancement based on neural networks applied to cochlear implant coding strategies

Bolner

Goehring

Monaghan

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Traditionally, algorithms that attempt to significantly improve speech intelligibility in noise for cochlear implant (CI) users have met with limited success, particularly in the presence of a fluctuating masker. In the present study, a speech enhancement algorithm integrating an artificial neural network (NN) into CI coding strategies is proposed. The algorithm decomposes the noisy input signal into timefrequency units, extracts a set of auditory-inspired features and feeds them to the NN to produce an estimation of which CI channels contain more perceptually important information (higher signal-to-noise ratio, SNR). This estimate is then used accordingly to retain a subset of channels for electrical stimulation, as in traditional n-of-m coding strategies. The proposed algorithm was tested with 10 normal-hearing participants listening to CI noise-vocoder simulations against a conventional Wiener filter based enhancement algorithm. Significant improvements in speech intelligibility in stationary and fluctuating noise were found over both unprocessed and Wiener filter processed conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Speech enhancement based on neural networks applied to cochlear implant coding strategies

Bolner

Goehring

Monaghan

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…In the ideal case (i.e. when the speech and noise components are known), these algorithms can lead to highly increased intelligibility, close to that for noise-free speech for NH listeners (Madhu et al., 2013) and CI users (Koning et al., 2015, Mauger et al., 2012a, Qazi et al., 2013). Similarly, extensive studies on the SI benefits of time-frequency masking with the ideal binary mask (IBM) support the potential of SNR-based suppression criteria for improving the intelligibility of speech in noise (Anzalone et al., 2006, Brungart et al., 2006, Hu and Loizou, 2008, Wang et al., 2009).…”

Section: Introductionmentioning

confidence: 99%

Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users

et al. 2017

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Speech understanding in noisy environments is still one of the major challenges for cochlear implant (CI) users in everyday life. We evaluated a speech enhancement algorithm based on neural networks (NNSE) for improving speech intelligibility in noise for CI users. The algorithm decomposes the noisy speech signal into time-frequency units, extracts a set of auditory-inspired features and feeds them to the neural network to produce an estimation of which frequency channels contain more perceptually important information (higher signal-to-noise ratio, SNR). This estimate is used to attenuate noise-dominated and retain speech-dominated CI channels for electrical stimulation, as in traditional n-of-m CI coding strategies. The proposed algorithm was evaluated by measuring the speech-in-noise performance of 14 CI users using three types of background noise. Two NNSE algorithms were compared: a speaker-dependent algorithm, that was trained on the target speaker used for testing, and a speaker-independent algorithm, that was trained on different speakers. Significant improvements in the intelligibility of speech in stationary and fluctuating noises were found relative to the unprocessed condition for the speaker-dependent algorithm in all noise types and for the speaker-independent algorithm in 2 out of 3 noise types. The NNSE algorithms used noise-specific neural networks that generalized to novel segments of the same noise type and worked over a range of SNRs. The proposed algorithm has the potential to improve the intelligibility of speech in noise for CI users while meeting the requirements of low computational complexity and processing delay for application in CI devices.

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“…The Binary mask was proposed by Cooke et al in 2001 in the context of complex auditory scene analysis (CASA) applications [5]. The idea is to suppress noise-dominant timefrequency units, keeping information in which the target signal power is dominant over noise [3] [6]. Such approach counts on the ability of the human hearing to deal with missing data to reconstitute audio cognition.…”

Section: Introductionmentioning

confidence: 99%

New insights on the optimality of parameterized Wiener filters for speech enhancement applications

Chiea

Costa

Barrault³

2019

Speech Communication

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This work presents a unified framework for defining a family of noise reduction techniques for speech enhancement applications. The proposed approach provides a unique theoretical foundation for some widely-applied soft and hard time-frequency masks, which encompasses the well-known Wiener filter and the heuristically-designed Binary mask. These techniques can now be considered as optimal solutions of the same minimization problem. The proposed cost function is defined by two design parameters that not only establish a desired trade-off between noise reduction and speech distortion, but also provide an insightful relationship with the mask morphology. Such characteristic may be useful for applications that require online adaptation of the suppression function according to variations of the acoustic scenario. Simulation examples indicate that the derived conformable suppression mask has approximately the same quality and intelligibility performance capability of the classical heuristically-defined parametric Wiener filter. The proposed approach may be of special interest for real-time embedded speech enhancement applications such as hearing aids and cochlear implants.

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The Potential for Speech Intelligibility Improvement Using the Ideal Binary Mask and the Ideal Wiener Filter in Single Channel Noise Reduction Systems: Application to Auditory Prostheses

Cited by 51 publications

References 27 publications

Speech enhancement based on neural networks applied to cochlear implant coding strategies

Speech enhancement based on neural networks applied to cochlear implant coding strategies

Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users

New insights on the optimality of parameterized Wiener filters for speech enhancement applications

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