• 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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.96-104
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• 2009

In this paper, we propose a no-reference objective Quality assessment metrics of image. The blockiness and blurring of edge areas which are sensitive to the human visual system are modeled as step functions. Blocking and blur metrics are obtained by estimating local visibility of blockiness and edge width, For the blocking metric, horizontal and vertical blocking lines are first determined by accumulating weighted differences of adjacent pixels and then the local visibility of blockiness at the intersection of blocking lines is obtained from the total difference of amplitudes of the 2-D step function which is modelled as a blocking region. The blurred input image is first re-blurred by a Gaussian blur kernel and an edge mask image is generated. In edge blocks, the local edge width is calculated from four directional projections (horizontal, vertical and two diagonal directions) using local extrema positions. In addition, the kurtosis and SSIM are used to compute the blur metric. The final no-reference objective metric is computed after those values are combined using an appropriate function. Experimental results show that the proposed objective metrics are highly correlated to the subjective data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.4
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• pp.613-631
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• 2013

Cognitive radio (CR) is proposed as a key solution to improve spectral efficiency and overcome the spectrum scarcity. Spectrum sensing is an important task in each CR system with the aim of identifying the spectrum holes and using them for secondary user's (SU) communications. Several conventional methods for spectrum sensing have been proposed such as energy detection, matched filter detection, etc. However, the main limitation of these classical methods is that the CR network is not able to communicate with its own base station during the spectrum sensing period and thus a fraction of the available primary frame cannot be exploited for data transmission. The other limitation in conventional methods is that the SU data frames should be synchronized with the primary network data frames. To overcome the above limitations, here, we propose a spectrum sensing technique based on blind source separation (BSS) that does not need time synchronization between the primary network and the CR. Moreover, by using the proposed technique, the SU can maintain its transmission with the base station even during spectrum sensing and thus higher rates are achieved by the CR network. Simulation results indicate that the proposed method outperforms the accuracy of conventional BSS-based spectrum sensing techniques.