• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.335-336
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• 2000

A psychoacoustic model based noise shaping method is proposed, where noise's presence with a host signal will not be perceptually noticeable. The derivation of imperceptible noise levels from the masking thresholds of the signal involves a deconvolution associated with the spreading function in the psychoacoustic model, which results in an ill-conditioned problem. In this paper, the problem is formulated as a constrained optimization, and it is demonstrated that the solution provides noise shaping where the noise excitation level conforms to the masking thresholds of the signal.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.337-338
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• 2000

The perceptual filter for speech enhancement was analytically derived where the frequency content of the input noisy signal was made the same as that of the estimated clean signal in auditory domain. However, the analytical derivation should rely on the deconvolution associated with the spreading function in the psychoacoustic model, which results in an ill-conditioned problem. In order to cope with the problem associated with the deconvolution, we propose a novel psychoacoustic model based speech enhancement filter whose principle is the same as the perceptual filter, however the filter is derived by a constrained optimization which provides solutions to the ill-conditioned problem.

• The Journal of the Acoustical Society of Korea
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• v.19 no.3E
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• pp.12-18
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• 2000

Psychoacoustic model based methods have recently been introduced in order to enhance speech signals corrupted by ambient noise. In particular, the perceptual filter is analytically derived where the frequency content of the input noisy signal is made the same as that of the estimated clean signal in auditory domain. However, the analytical derivation should rely on the deconvolution associated with the spreading function in the psychoacoustic model, which results in an ill-conditioned problem. In order to cope with the problem associated with the deconvolution, we propose a novel psychoacoustic model based speech enhancement filter whose principle is the same as the perceptual filter, however the filter is derived by a constrained optimization which provides solutions to the ill-conditioned problem. It is demonstrated with artificially generated signals that the proposed filter operates according to the principle. It is shown that superior performance results from the proposed filter over the perceptual filter provided that a clean speech signal is separable from noise.

• The Journal of the Acoustical Society of Korea
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• v.20 no.2E
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• pp.9-16
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• 2001

A psychoacoustic model based noise shaping method which shapes the noise in the frequency domain is proposed, where its presence with a host signal will not be perceptually noticeable. The derivation of imperceptible noise levels from the masking thresholds of the signal involves a deconvolution associated with the spreading function in the psychoacoustic model, which results in an ill-conditioned problem. In this paper, the problem is formulated as a constrained optimization, and it is demonstrated that the solution provides noise shaping where the noise excitation level conforms to the masking thresholds of the signal, and thus the noises embedded in the signal will not be perceived by human ear.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.11a
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• pp.43-44
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• 2011

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.1
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• pp.73-83
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• 2013

In previous works, psychoacoustic parameters have been used for objective quantification. However, these parameters do not agree well with subjective assessment. Therefore, the correlation between psychoacoustic parameters and the subjective rating of door closing sounds of sampled cars is low, and it is not sufficient to use psychoacoustic parameters as an objective metric to quantify the sound quality of door closing sounds. In this paper, a new method is proposed to objectively quantify the sound quality based on physiological acoustics and statistical signal processing. The gammatone filter, as a pre-processing, is used in models of the auditory system and kurtosis, which is the fourth-order moment of temporal signal, and is used to extract information about sound quality quantification for door closing sounds. The new metric obtained through the proposed method is highly correlated with subjective rating, and it is successfully applied to the quantification of the sound quality of door closing sounds.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.11C
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• pp.1491-1497
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• 2004

The psychoacoustic model in MPEG audio layer-III (MP3) encoder is optimized for the fixed-point processing. The optimization process consists of determining the data word length of arithmetic unit and the algorithm for transcendental functions that are often used in the psychoacoustic model. In order to determine the data word length, we defined a statistical model expressing the relation between the fixed-point operation errors of the psychoacoustic model and the probability of alteration of the allocated bits doe to these errors. Based on the simulations using this model, we chose a 24-bit data path and constructed a 24-bit fixed-point MP3 encoder. Sound quality tests using the constructed fixed-point encoder showed a mean degradation of -0.2 on ITU-R 5-point audio impairment scale.

• ETRI Journal
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• v.35 no.5
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• pp.859-868
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• 2013

The active noise control (ANC) technique attenuates acoustic noise in a flexible and effective way. Traditional ANC design aims to minimize the residual noise energy, which is indiscriminative in the frequency domain. However, human hearing perception exhibits selective sensitivity for different frequency ranges. In this paper, we aim to improve the noise attenuation performance in perceptual perspective by incorporating noise weighting into ANC design. We also introduce psychoacoustic analysis to evaluate the sound quality of the residual noise by using a predictive pleasantness model, which combines four psychoacoustic parameters: loudness, sharpness, roughness, and tonality. Simulations on synthetic random noise and realistic noise show that our method improves the sound quality and that ITU-R 468 noise weighting even performs better than A-weighting.

• Fibers and Polymers
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• v.1 no.1
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• pp.59-65
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• 2000

In order to investigate psychoacoustic characteristics of fibers, and to compare them with sound physical parameters, each sound of 25 different fabrics consisted of a single fiber such as wool, cotton, silk, polyester, and nylon was recorded. Sounds of specimens were transformed into critical band diagram and psychoacoustic characteristics including loudness and sharpness for each sound were calculated based on Zwicker＇s models. Physical parameters such as the level pressure of total sound (LPT), level ranges (ΔL), frequency differences (Δf), AR coefficients (ARC, ARF, ARE) were obtained in fast fourier transform (FFT) spectrum. Nylon taffeta showed higher values for loudness than 2.5 sone corresponding to human low conversation, while most silk fibers generated less louder showing lower values for loudness than 1.0 sone. Wool fibers had higher loudness mean value than that of cotton, while the two fibers didn't differ in LPT. Loudness showed high positive correlation coefficients with both LPT and ARC. Sharpness values were higher for wool fiber group than other fibers. Sharpness was not concerned with loudness, LPT, and ARC, but the fabrics with higher values for sharpness tended to show higher ΔL.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.301-302
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• 2007

A speech enhancement algorithm based on both a decomposition of speech into deterministic and stochastic components and a psychoacoustic model is proposed. Noisy speech is decomposed into deterministic and stochastic components, and then each component is enhanced preserving its individual characteristics. A psychoacoustic model is taken into account when enhancing the stochastic component. Simulation results show that the proposed algorithm performs better than some of the more popular algorithms.