Improving the perceptual quality of ideal binary masked speech

Lightburn, Leo; Sena, Enzo De; Moore, Alastair H.; Naylor, Patrick A.; Brookes, Mike

doi:10.1109/icassp.2017.7952238

Cited by 9 publications

(14 citation statements)

References 24 publications

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“…By defining the cost function on the log-spectral amplitude error, we determine a gain function in the form of a generalized binary mask, which enables improved speech intelligibility [16]. Recently the binary mask techniques have been proposed as a signal processing tool to study and analyze the time-frequency analysis and grouping process of the auditory system [17]- [20]. However, perhaps motivated by the significant intelligibility improvements achievable in this ideal setting, where local target-to-noise energy ratios are known with certainty, the binary mask framework has more recently been adapted to the practical problem of retrieving a target speech signal from a noisy mixture in the non-ideal situation where the local target-to-noise energy ratios are unknown, but must be estimated from the noisy mixture signal.…”

Section: Literature Surveymentioning

confidence: 99%

An Improved Bi-Level Thresholding Based Uncertainty Evaluation for Speech Enhancement in Non-Stationary Noises

Rao¹,

Sankar²,

Naidu³

2018

IJET

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This paper proposes a new speech enhancement framework to improve the quality of speeches recorded under adverse acoustic environments based on the speech presence uncertainty. Since the uncertainty evaluation gives a more and clear discrimination about the speech and noise, this paper proposes a new uncertainty evaluation mechanism as a preprocessing mechanism to the noise suppression methods. This mechanism relates with energies of a noisy speech signal and classifies the speech segments and noise segments more perfectly. In addition to the quality enhancement, this approach also reduces the unnecessary computational burden over the speech processing system. Extensive simulations are carried out over the speech signals with different types of non-stationary noises like babble noise, exhibition noise, restaurant noise and train station noises and the performance is measured with the performance metrics namely the Output SNR, AvgSegSNR, PESQ and COMP. The comparative analysis of proposed approach over the conventional approaches shows an outstanding performance in all environments.

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Section: Literature Surveymentioning

confidence: 99%

An Improved Bi-Level Thresholding Based Uncertainty Evaluation for Speech Enhancement in Non-Stationary Noises

Rao¹,

Sankar²,

Naidu³

2018

IJET

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“…Unfortunately, the direct application of a binary mask leads to poor perceived quality [11]. In this work, we (i) use a deep neural network (DNN) to estimate a binary mask that captures the modulations of the target source and (ii) use the mask to define the speech presence probability in a conventional speech enhancer [12,13].…”

Section: Introductionmentioning

confidence: 99%

Binaural Mask-Informed Speech Enhancement for Hearing AIDS with Head Tracking

Moore

Lightburn

Xue

et al. 2018

2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC)

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An end-to-end speech enhancement system for hearing aids is proposed which seeks to improve the intelligibility of binaural speech in noise during head movement. The system uses a reference beamformer whose look direction is informed by knowledge of the head orientation and the a priori known direction of the desired source. From this a time-frequency mask is estimated using a deep neural network. The binaural signals are obtained using bilateral beamformers followed by a classical minimum mean square error speech enhancer, modified to use the estimated mask as a speech presence probability prior. In simulated experiments, the improvement in a binaural intelligibility metric (DBSTOI) given by the proposed system relative to beamforming alone corresponds to an SNR improvement of 4 to 6 dB. Results also demonstrate the individual contributions of incorporating the mask and the head orientation-aware beam steering to the proposed system.

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“…Besides, mask-based SE methods have predominantly been applied in many SE and speech separation applications [7]. The key idea behind mask-based SE methods is to estimate a spectrographic binary or soft mask to suppress the unwanted spectrogram components [7][8][9][10][11]. For binary mask-based SE methods, the spectrographic masks are "hard binary masks" where a spectral component is either set to 1 for the target speech component or set to 0 for the non-target speech component.…”

Section: Introductionmentioning

confidence: 99%

“…For binary mask-based SE methods, the spectrographic masks are "hard binary masks" where a spectral component is either set to 1 for the target speech component or set to 0 for the non-target speech component. Experimental results have shown that the performance of binary mask SE methods degrades with the decrease of the signal-to-noise ratio (SNR) and the masked spectral may cause the loss of speech components due to the harsh black or white binary conditions [7,8]. To overcome this disadvantage, the soft mask-based SE methods have been developed [8].…”

Section: Introductionmentioning

confidence: 99%

“…Experimental results have shown that the performance of binary mask SE methods degrades with the decrease of the signal-to-noise ratio (SNR) and the masked spectral may cause the loss of speech components due to the harsh black or white binary conditions [7,8]. To overcome this disadvantage, the soft mask-based SE methods have been developed [8]. In soft mask-based SE methods, each time-frequency component is assigned a probability linked to the target speech.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor

2018

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Enhancing speech captured by distant microphones is a challenging task. In this study, we investigate the multichannel signal properties of the single acoustic vector sensor (AVS) to obtain the inter-sensor data ratio (ISDR) model in the time-frequency (TF) domain. Then, the monotone functions describing the relationship between the ISDRs and the direction of arrival (DOA) of the target speaker are derived. For the target speech enhancement (SE) task, the DOA of the target speaker is given, and the ISDRs are calculated. Hence, the TF components dominated by the target speech are extracted with high probability using the established monotone functions, and then, a nonlinear soft mask of the target speech is generated. As a result, a masking-based speech enhancement method is developed, which is termed the AVS-SMASK method. Extensive experiments with simulated data and recorded data have been carried out to validate the effectiveness of our proposed AVS-SMASK method in terms of suppressing spatial speech interferences and reducing the adverse impact of the additive background noise while maintaining less speech distortion. Moreover, our AVS-SMASK method is computationally inexpensive, and the AVS is of a small physical size. These merits are favorable to many applications, such as robot auditory systems.

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Improving the perceptual quality of ideal binary masked speech

Cited by 9 publications

References 24 publications

An Improved Bi-Level Thresholding Based Uncertainty Evaluation for Speech Enhancement in Non-Stationary Noises

An Improved Bi-Level Thresholding Based Uncertainty Evaluation for Speech Enhancement in Non-Stationary Noises

Binaural Mask-Informed Speech Enhancement for Hearing AIDS with Head Tracking

Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor

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