• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.7
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• pp.1571-1576
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• 2013

This paper proposes an improvement algorithm of signal-to-noise ratios (SNRs) for speech signals under noisy environments. The proposed algorithm first estimates the SNRs in a low SNR, mid SNR and high SNR areas, in order to improve the SNRs in the speech signal from background noise, such as white noise and car noise. Thereafter, this algorithm subtracts the noise signal from the noisy speech signal at each bands using a spectrum sharpening method. In the experiment, good signal-to-noise ratios (SNR) are obtained for white noise and car noise compared with a conventional spectral subtraction method. From the experiment results, the maximal improvement in the output SNR results was approximately 4.2 dB and 3.7 dB better for white noise and car noise compared with the results of the spectral subtraction method, in the background noisy environment, respectively.

• Journal of Biomedical Engineering Research
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• v.34 no.1
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• pp.24-33
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• 2013

To enhance the speech perception of hearing aid users in noisy environment, most hearing aid devices adopt various beamforming algorithms such as the first-order differential microphone (DM1) and the two-stage directional microphone (DM2) algorithms that maintain sounds from the direction of the interlocutor and reduce the ambient sounds from the other directions. However, these conventional algorithms represent poor directionality ability in low frequency area. Therefore, to enhance the speech perception of hearing aid uses in low frequency range, our group had suggested a fractional delay subtraction and integration (FDSI) algorithm and estimated its theoretical performance using computer simulation in previous article. In this study, we performed a KEMAR test in non-reverberant room that compares the performance of DM1, DM2, broadband beamforming (BBF), and proposed FDSI algorithms using several objective indices such as a signal-to-noise ratio (SNR) improvement, a segmental SNR (seg-SNR) improvement, a perceptual evaluation of speech quality (PESQ), and an Itakura-Saito measure (IS). Experimental results showed that the performance of the FDSI algorithm was -3.26-7.16 dB in SNR improvement, -1.94-5.41 dB in segSNR improvement, 1.49-2.79 in PESQ, and 0.79-3.59 in IS, which demonstrated that the FDSI algorithm showed the highest improvement of SNR and segSNR, and the lowest IS. We believe that the proposed FDSI algorithm has a potential as a beamformer for digital hearing aid devices.

• ETRI Journal
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• v.33 no.6
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• pp.841-848
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• 2011

A signal-to-noise ratio (SNR) enhancement algorithm using multiple chirp symbols with clock drift is proposed for accurate ranging. Improvement of the ranging performance can be achieved by using the multiple chirp symbols according to Cramer-Rao lower bound; however, distortion caused by clock drift is inevitable practically. The distortion induced by the clock drift is approximated as a linear phase term, caused by carrier frequency offset, sampling time offset, and symbol time offset. SNR of the averaged chirp symbol obtained from the proposed algorithm based on the phase derotation and the symbol averaging is enhanced. Hence, the ranging performance is improved. The mathematical analysis of the SNR enhancement agrees with the simulations.

• The Journal of the Acoustical Society of Korea
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• v.24 no.1
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• pp.43-51
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• 2005

In this paper, we propose an algorithm that improves a tone quality of a noisy audio signal in order to enhance a Performance of perceptual filter using noise energy control. Most of the algorithms which were proposed by the other researchers usually applied a filter using the noise energy acquired from a silent range. In this case. the improvement rate of tone quality decreases if the noise energy is changed by the magnitude or environment variation in a signal frame. But the Proposed method Provides the means to find a food estimated noise through energy control of the estimated noise which is obtained from a silent range. Also we can get the enhancement of tone qualify in low frequency band unlike other methods. To show the performance of the Proposed algorithm, various input signals which had a different signal-to-noise ratio (SNR) such as 5dB, l0dB, 15dB and 20dB were used to test the proposed algorithm. With the proposed algorithm, we could confirm the enhancement of tone quality in terms of segmental SNR (SSNR). noise-to-mask ration (NMR) and mean opinion score (MOS) test.

• Journal of Biomedical Engineering Research
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• v.26 no.3
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• pp.177-184
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• 2005

In this paper, we proposed a new hearing aid algorithm to improve SNR(signal to noise ratio) of noisy speech signal and speech perception. The proposed hearing aid algorithm is a multi-band loudness compensation based independent component analysis (ICA). The proposed algorithm was compared with a conventional spectral subtraction algorithm on behind-the-ear type hearing aid. The proposed algorithm successfully separated a target speech signal from background noise and from a mixture of the speech signals. The algorithms were compared each other by means of SNR. The average improvement of SNR by ICA based algorithm was 16.64dB, whereas spectral subtraction algorithm was 8.67dB. From the clinical tests, we concluded that our proposed algorithm would help hearing aid user to hear clearly a target speech in noisy conditions.

• ETRI Journal
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• v.35 no.3
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• pp.371-377
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• 2013

An effective signal detection algorithm with low complexity is presented for multiple-input multiple-output orthogonal frequency division multiplexing systems. The proposed technique, QR-MLD, combines the conventional maximum likelihood detection (MLD) algorithm and the QR algorithm, resulting in much lower complexity compared to MLD. The proposed technique is compared with a similar algorithm, showing that the complexity of the proposed technique with T=1 is a 95% improvement over that of MLD, at the expense of about a 2-dB signal-to-noise-ratio (SNR) degradation for a bit error rate (BER) of $10^{-3}$. Additionally, with T=2, the proposed technique reduces the complexity by 73% for multiplications and 80% for additions and enhances the SNR performance about 1 dB for a BER of $10^{-3}$.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11C
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• pp.1161-1168
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• 2003

In this paper, we propose an improved statistical model-based voice activity detection algorithm and threshold update method. We first improve signal-to-noise ratio by using speech enhancement preprocessing algorithm combined power subtraction method and matched filter, then apply it to LLR test optimum decision rule for improving the performance even in low SNR conditions. And we propose an adaptive threshold update method that was not concerned in any papers. We also perform extensive computer simulations to demonstrate the performance improvement of the proposed VAD algorithm employing the proposed speech enhancement preprocessing algorithm and adaptive threshold update method under various background noise environments. Finally we verify our results by comparing ITU-T G.729 Annex B.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.61-64
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• 1999

The turbo code appropriate to IMT-2000 is known to have a good performance whenever the size of frame increases. But it is not appropriate to a sort of video service to need real time because of decoding complexity and long delay time by the size of frame. Therefore this paper proposes decoding decision algorithm of short frame in which soft output is weighted according to iteration number in turbo decoder. Performance of the proposed algorithm is analysed in the AWGN channel when short length of frame is 100, 256, 640. As the result. it is appeared that the proposed decoding decision algorithm has improved in BER other than in the existing MAP decoding algorithm.

• Investigative Magnetic Resonance Imaging
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• v.20 no.4
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• pp.215-223
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• 2016

Purpose: The management of metal-induced field inhomogeneities is one of the major concerns of distortion-free magnetic resonance images near metallic implants. The recently proposed method called "Slice Encoding for Metal Artifact Correction (SEMAC)" is an effective spin echo pulse sequence of magnetic resonance imaging (MRI) near metallic implants. However, as SEMAC uses the noisy resolved data elements, SEMAC images can have a major problem for improving the signal-to-noise ratio (SNR) without compromising the correction of metal artifacts. To address that issue, this paper presents a novel reconstruction technique for providing an improvement of the SNR in SEMAC images without sacrificing the correction of metal artifacts. Materials and Methods: Low-rank approximation in each coil image is first performed to suppress the noise in the slice direction, because the signal is highly correlated between SEMAC-encoded slices. Secondly, SEMAC images are reconstructed by the best linear unbiased estimator (BLUE), also known as Gauss-Markov or weighted least squares. Noise levels and correlation in the receiver channels are considered for the sake of SNR optimization. To this end, since distorted excitation profiles are sparse, $l_1$ minimization performs well in recovering the sparse distorted excitation profiles and the sparse modeling of our approach offers excellent correction of metal-induced distortions. Results: Three images reconstructed using SEMAC, SEMAC with the conventional two-step noise reduction, and the proposed image denoising for metal MRI exploiting sparsity and low rank approximation algorithm were compared. The proposed algorithm outperformed two methods and produced 119% SNR better than SEMAC and 89% SNR better than SEMAC with the conventional two-step noise reduction. Conclusion: We successfully demonstrated that the proposed, novel algorithm for SEMAC, if compared with conventional de-noising methods, substantially improves SNR and reduces artifacts.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.10
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• pp.67-74
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• 2007

Turbo code, a kind of channel coding technique, has ben used in the field of digital mobile communication system if the number of iterations is increased in the several channel environments, my further iteration results in very little improvement, and requires much delay and computation in proportion to the number of iterations. In this paper, it proposes an efficient stopping rules for the iteration process in turbo decoding. By using absolute mean values for the LLR difference values between the first and second decoder in the present decoding process, the proposed algorithm can largely reduce the average number of iterations without BER performance degradation in all SNR regions. As a result of simulation, the average number of iterations of proposed algorithm is reduced by about $18.25%{\sim}20.58%$ compared to SDR algerian in the lower SNR region, and is reduced by about $22.96%{\sim}28.74%$ compared to method using variance values of extrinsic information in the upper SNR region.