• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.4
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• pp.363-370
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• 2017

In this paper, we proposed a low-complexity speech enhancement algorithm based on a improved minima controlled recursive averaging (IMCRA) and log minimum mean square error (logMMSE). The IMCRA algorithm track the minima value of input power within buffers in local window and identify the speech presence using ratio between input power and its minima value. In this process, many number of operations are required. To reduce the number of operations of IMCRA algorithm, minima value is tracked using time-varying frequency-dependent smoothing based on speech presence probability. The proposed algorithm enhanced speech quality by 2.778%, 3.481%, 2.980% and 2.162% in 0, 5, 10 and 15dB SNR respectively and reduced computational complexity by average 9.570%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12C
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• pp.1197-1207
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• 2009

Orthogonal Frequency Division Multiple Access (OFDMA) is widely used as a multiple access technique for next generation mobile communication systems, however, its main drawback is the high peak-to-average ratio (PAPR). Thus for the uplink case where the transmit power is strictly limited due to the battery life of mobile units, single carrier frequency division multiple access (SC-FDMA) with low PAPR is preferred to OFDMA method. In this paper, we propose a method to improve the performance of SC-FDMA using frequency domain MMSE equalization. The proposed improved log-likelihood ratio (LLR) generation method exploits both the diversity characteristic of channels and the reciprocity that is obtained from the received signals. The complexity of the proposed method is analyzed and its performance gain is demonstrated via a set of computer simulations.

• Phonetics and Speech Sciences
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• v.6 no.2
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• pp.29-34
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• 2014

In the Vector Taylor Series (VTS)-based noisy speech recognition methods, Hidden Markov Models (HMM) are usually trained with clean speech. However, better performance is expected by training the HMM with noisy speech. In a previous study, we could find that Minimum Mean Square Error (MMSE) estimation of the training noisy speech in the log-spectrum domain produce improved recognition results, but since the proposed algorithm was done in the log-spectrum domain, it could not be used for the HMM adaptation. In this paper, we modify the previous algorithm to derive a novel mathematical relation between test and training noisy speech in the cepstrum domain and the mean and covariance of the Multi-condition TRaining (MTR) trained noisy speech HMM are adapted. In the noisy speech recognition experiments on the Aurora 2 database, the proposed method produced 10.6% of relative improvement in Word Error Rates (WERs) over the MTR method while the previous MMSE estimation of the training noisy speech produced 4.3% of relative improvement, which shows the superiority of the proposed method.

• Phonetics and Speech Sciences
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• v.3 no.1
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• pp.87-94
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• 2011

The decision directed (DD) approach is widely used to determine a priori SNR from noisy speech signals. In conventional speech enhancement systems with a DD approach, a priori SNR is estimated by using only the magnitude components and consequently follows a posteriori SNR with one frame delay. We propose a phase-dependent two-step a priori SNR estimator based on the minimum mean square error (MMSE) in the log-mel spectral domain so that we can consider both magnitude and phase information, and it can overcome the performance degradation caused by one frame delay. From the experimental results, the proposed estimator is shown to improve the output SNR of enhanced speech signals by 2.3 dB compared to the conventional DD approach-based system.

• ETRI Journal
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• v.36 no.5
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• pp.721-729
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• 2014

We propose a novel phase-based method for single-channel speech enhancement to extract and enhance the desired signals in noisy environments by utilizing the phase information. In the method, a phase-dependent a priori signal-to-noise ratio (SNR) is estimated in the log-mel spectral domain to utilize both the magnitude and phase information of input speech signals. The phase-dependent estimator is incorporated into the conventional magnitude-based decision-directed approach that recursively computes the a priori SNR from noisy speech. Additionally, we reduce the performance degradation owing to the one-frame delay of the estimated phase-dependent a priori SNR by using a minimum mean square error (MMSE)-based and maximum a posteriori (MAP)-based estimator. In our speech enhancement experiments, the proposed phase-dependent a priori SNR estimator is shown to improve the output SNR by 2.6 dB for both the MMSE-based and MAP-based estimator cases as compared to a conventional magnitude-based estimator.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.7C
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• pp.616-626
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• 2007

In spatially multiplexed MIMO systems that enable high data rate transmission over wireless communication channels, the spatial demultiplexing at the receiver is a challenging task, and various demultiplexing methods have been developed recently by many researchers. Among the previous methods, maximum likelihood detection with QR decomposition and M-algorithm (QRM-MM)), and sphere decoding (SD) schemes have been reported to achieve a (near) maximum likelihood (ML) performance. In this paper, we propose a novel signal detection method that achieves a near ML performance in a computationally efficient manner. The proposed method is demonstrated via a set of computer simulations that the proposed method achieves a near ML performance while requiring a complexity that is comparable to that of the conventional MMSE-OSIC. We also show that the log likelihood ratio (LLR) values for all bits are obtained without additional calculation but as byproduct in the proposed detection method, while in the previous QRM-MLD, SD, additional computation is necessary after the hard decision for LLR calculation.

• The Journal of the Acoustical Society of Korea
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• v.29 no.2E
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• pp.86-99
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• 2010

This paper analyzes the performance of various single channel speech enhancement algorithms when they are applied to automatic speech recognition (ASR) systems as a preprocessor. The functional modules of speech enhancement systems are first divided into four major modules such as a gain estimator, a noise power spectrum estimator, a priori signal to noise ratio (SNR) estimator, and a speech absence probability (SAP) estimator. We investigate the relationship between speech recognition accuracy and the roles of each module. Simulation results show that the Wiener filter outperforms other gain functions such as minimum mean square error-short time spectral amplitude (MMSE-STSA) and minimum mean square error-log spectral amplitude (MMSE-LSA) estimators when a perfect noise estimator is applied. When the performance of the noise estimator degrades, however, MMSE methods including the decision directed module to estimate a priori SNR and the SAP estimation module helps to improve the performance of the enhancement algorithm for speech recognition systems.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.159-166
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• 2017

In this paper, we have experimented to improve the verification performance of speaker change detection on broadcast news. It is to enhance the input noisy speech and to apply the KL distance $D_s$ using the SNR-based weighting function $w_m$. The basic experimental system is the verification system of speaker change using GMM-UBM based KL distance D(Experiment 0). Experiment 1 applies the input noisy speech enhancement using MMSE Log-STSA. Experiment 2 applies the new KL distance $D_s$ to the system of Experiment 1. Experiments were conducted under the condition of 0% MDR in order to prevent missing information of speaker change. The FAR of Experiment 0 was 71.5%. The FAR of Experiment 1 was 67.3%, which was 4.2% higher than that of Experiment 0. The FAR of experiment 2 was 60.7%, which was 10.8% higher than that of experiment 0.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.10-21
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• 2015

Cellular Network assisted device-to-device (D2D) communication has been growing to reduce the overload of eNodeB and mitigate the frequency shortage. However, by sharing the uplink frequency resource with the cellular network, the interference between cellular and D2D is increased. In this paper, we propose the advanced receiver based on soft decision to reduce the interference between cellular and D2D. The proposed receiver can suppress and cancel the interference by calculating the unbiased estimation value of interference signal using minimum mean square error (MMSE) or interference rejection combing (IRC) receiver. The interference signal is updated using soft information expressed by log-likelihood ratio (LLR). We perform a system level simulation based on the 20-MHz bandwidth of the 3GPP LTE-A system. Simulation results show that the proposed receiver can improve SINR, throughput and spectral efficiency compared to conventional receivers.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.3-11
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• 2015

An advanced receiver which can manage inter-cell interference is required to cope with the explosively increasing mobile data traffic. 3rd Generation Partnership Project (3GPP) has discussed network assisted interference cancellation and suppression (NAICS) to improve signal-to-noise-plus-interference ratio (SINR) and receiver performance by suppression or cancellation of interference signal from inter-cells. In this paper, we propose the advanced receiver based on soft decision to reduce the interference from neighbor cell in LTE-Advanced downlink system. The proposed receiver can suppress and cancel the interference by calculating the unbiased estimation value of interference signal using minimum mean square error (MMSE) or interference rejection combing (IRC) receiver. The interference signal is updated using soft information expressed by log-likelihood ratio (LLR). We perform the system level simulation based on 20MHz bandwidth of 3GPP LTE-Advanced downlink system. Simulation results show that the proposed receiver can improve SINR, throughput, and spectral efficiency of conventional system.