• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.2623-2627
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• 2014

The objective of this study is to experimentally investigate the heat transfer characteristics of 200W woofer speaker unit with the input voice signals such as 500 Hz, 1000 Hz, 2000 Hz, and 3000 Hz. The temperature and heat transfer characteristics of the woofer speaker unit were evaluated with the input signals. As results. the temperature of the voice-coil for woofer speaker unit increased with a decrease of the input signals and the temperature differences between parts of the tested speaker unit increased with the decrease of the input voice signals. In addition, the voice-coil temperature for the input signal of 500 Hz showed 48.4 % lower than that of 3000 Hz during 18000 sec.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.671-676
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• 2006

A convolution with HRTF DB and the original sound is generally used to make the method of sound image localization for virtual speaker realization. But it can decline localization by the confusion between up and down or front and back directions due to the non-individual HRTF depending on each listener. In this paper, we study a virtual speaker using a new HRTF, which is grouping the HRTF around the virtual speaker to improve localization between up and down or front and back directions. To effective HRTF grouping, we decide the location and number of HRTF using informal listening test. A performance test result of virtual speaker using the grouped HRTF shows that the proposed method improves the front-back and up-down sound localization characteristics much better than the conventional methods.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1223-1226
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• 1998

The pre-emephasis filter as the conventional method emphasizes all components of high frequency that reflects the speaker characteristics. However this filter don't show the auditory characteristics of speaker's speech. In order to emphasize the perceptual characteristics, we propose the speaker recognition system that uses the perceptual weighting as the preprocessor because the Auditory characteristic of human is sensitive to the formant peaks. This filter has the characteristcs that both deemphasizes the low-formants and emphasizes the high formants. As a result of the proposed method, we improve the total recognition rate 1.7% better than the conventional method.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.157-160
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• 2000

In the generally a voice conversion has used VQ(Vector Quantization) for partitioning the spectral feature and has performed by adding an appropriate offset vector to the source speaker's spectral vector. But there is not represented the target speaker's various characteristics because of discrete characteristics of transformed parameter. In this paper, these problems are solved by using the LMR(Linear Multivariate Regression) instead of the mapping codebook which is determined to the relationship of source and target speaker vocal tract characteristics. Also we propose the method for solved the discontinuity which is caused by applying to time aligned parameters using Dynamic Time Warping the time or pitch-scale modified speech. In our proposed algorithm for overcoming the transitional discontinuities, first of all, we don't change time or pitch scale and by using the LMR change a speaker's vocal tract characteristics in speech with non-modified time or pitch. Compared to existed methods based on VQ and LMR, we have much better voice quality in the result of the proposed algorithm.

• Speech Sciences
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• v.15 no.3
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• pp.7-15
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• 2008

This paper proposes the method to extract new feature vectors using the difference between the cepstrum for static characteristics and delta cepstrum for dynamic characteristics in speaker recognition (SR). The difference vector (DV) which it proposes from this paper is containing the static and the dynamic characteristics simultaneously at the intermediate characteristic vector which uses the deference between the static and the dynamic characteristics and as the characteristic vector which is new there is a possibility of doing. Compared to the conventional method, the proposed method can achieve new feature vector without increasing of new parameter, but only need the calculation process for the difference between the cepstrum and delta cepstrum. Experimental results show that the proposed method has a good performance more than 2.03%, on average, compared with conventional method in speaker identification (SI).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.1247-1248
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• 2010

• Korean Journal of Fisheries and Aquatic Sciences
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• v.44 no.5
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• pp.543-549
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• 2011

An underwater speaker was developed for use as an acoustic deterrent device that transmits acoustic energy through the water omnidirectionally over a broadband frequency range to eliminate marine mammal attacks and to prevent physical damage to the inshore and coastal fishing grounds of Korea. The underwater speaker was constructed of two vibration caps machined from 6061-T6 aluminum alloy and a stack of PZ 26 piezoelectric ceramic rings (Ferroperm Piezoceramics A/S) connected mechanically in series and electrically in parallel. The performance characteristics of the underwater speaker were measured and analyzed in an experimental water tank of $5\;m{\times}5\;m{\times}6\;m$. The peak transmitting voltage response (TVR) was measured at 11.16 kHz with 163.45 dB re $1\;{\mu}Pa$/V at 1m. The underwater speaker showed a near omnidirectional beam pattern at the peak TVR resonance frequency. The usable frequency range was 4-25 kHz with a lower TVR limit of approximately 140 dB. We conclude that this underwater speaker could be satisfactorily used as an acoustic deterrent device against marine mammals, particularly the bottlenose dolphin, to protect catches and fishing grounds as well as the mammals themselves, for example, by keeping them away from fishing gear and/or vessels.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4E
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• pp.156-163
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• 2002

We apply independent component analysis (ICA) for extracting an optimal basis to the problem of finding efficient features for representing speech signals of a given speaker The speech segments are assumed to be generated by a linear combination of the basis functions, thus the distribution of speech segments of a speaker is modeled by adapting the basis functions so that each source component is statistically independent. The learned basis functions are oriented and localized in both space and frequency, bearing a resemblance to Gabor wavelets. These features are speaker dependent characteristics and to assess their efficiency we performed speaker identification experiments and compared our results with the conventional Fourier-basis. Our results show that the proposed method is more efficient than the conventional Fourier-based features in that they can obtain a higher speaker identification rate.

• The Journal of the Acoustical Society of Korea
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• v.19 no.8
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• pp.63-67
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• 2000

This paper deals with text-prompt speaker recognition based on dynamic time warping (DTW). The Genetic Algorithm was applied to the creation of reference patterns for suitable reflection of the speaker characteristics, one of the most important determinants in the fields of speaker recognition. In order to overcome the weakness of text-dependent and text-independent speaker recognition, the text-prompt type was suggested. Performed speaker identification and verification in close and open set respectively, hence the Genetic algorithm-based reference patterns had been proven to have better performance in both recognition rate and speed than that of conventional reference patterns.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.156-156
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• 2002

We apply independent component analysis (ICA) for extracting an optimal basis to the problem of finding efficient features for representing speech signals of a given speaker The speech segments are assumed to be generated by a linear combination of the basis functions, thus the distribution of speech segments of a speaker is modeled by adapting the basis functions so that each source component is statistically independent. The learned basis functions are oriented and localized in both space and frequency, bearing a resemblance to Gabor wavelets. These features are speaker dependent characteristics and to assess their efficiency we performed speaker identification experiments and compared our results with the conventional Fourier-basis. Our results show that the proposed method is more efficient than the conventional Fourier-based features in that they can obtain a higher speaker identification rate.