• The Journal of the Acoustical Society of Korea
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• v.34 no.3
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• pp.227-233
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• 2015

In this paper, we propose music and voice separation using kernel sptectrogram models backfitting based on log-spectral amplitude estimator. The existing method separates sources based on the estimate of a desired objects by training MSE (Mean Square Error) designed Winer filter. We introduce rather clear music and voice signals with application of log-spectral amplitude estimator, instead of adaptation of MSE which has been treated as an existing method. Experimental results reveal that the proposed method shows higher performance than the existing methods.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.2
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• pp.95-106
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• 2007

This paper proposes an effective tempo feature extraction method for music information retrieval. The tempo information is modeled by the narrow-band temporal modulation components, which are decomposed into a modulation spectrum via joint frequency analysis. In implementation, the tempo feature is directly extracted from the modified discrete cosine transform coefficients, which is the output of partial MP3(MPEG 1 Layer 3) decoder. Then, different features are extracted from the amplitudes of modulation spectrum and applied to different music information retrieval tasks. The logarithmic scale modulation frequency coefficients are employed in automatic music emotion classification and music genre classification. The classification precision in both systems is improved significantly. The bit vectors derived from adaptive modulation spectrum is used in audio fingerprinting task That is proved to be able to achieve high robustness in this application. The experimental results in these tasks validate the effectiveness of the proposed tempo feature.

• The Journal of the Acoustical Society of Korea
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• v.35 no.1
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• pp.49-54
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• 2016

In this paper, we propose a vocal separation method using weighted ${\beta}$-order minimum mean wquare error estimation (WbE) based on kernel back-fitting algorithm. In spoken speech enhancement, it is well-known that the WbE outperforms the existing Bayesian estimators such as the minimum mean square error (MMSE) of the short-time spectral amplitude (STSA) and the MMSE of the logarithm of the STSA (LSA), in terms of both objective and subjective measures. In the proposed method, WbE is applied to a basic iterative kernel back-fitting algorithm for improving the vocal separation performance from monaural music signal. The experimental results show that the proposed method achieves better separation performance than other existing methods.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.4
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• pp.65-73
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• 2003

This paper presents the implementation of an analog signal-processing ASIS to detect an angular velocity signal from a vibrator angular velocity detection sensor. The output of the sensor to be charge appeared as the variation of the capacitance value in the structure of the sensor was detected using charge amplifiers and a self oscillation circuit for driving the sensor was implemented with a sinusoidal self oscillation circuit using the resonance characteristics of the sensor. Specially an automatic gain control circuit was utilized to prevent the deterioration of self-oscillation characteristics due to the external elements such as the characteristic variation of the sensor process and the temperature variation. The angular velocity signal, amplitude-mod)Hated in the operation characteristics of the sensor, was demodulated using a synchronous detection circuit. A switching multiplication circuit was used in the synchronous detection circuit to prevent the magnitude variation of detected signal caused by the amplitude variation of the carrier signal. The ASIC was designed and implemented using 0.5${\mu}{\textrm}{m}$ CMOS process. The chip size was 1.2mm x 1mm. In the experiment under the supply voltage of 3V, the ASIC consumed the supply current of 3.6mA and noise spectrum density from dc to 50Hz was in the range of -95 dBrms/√Hz and -100 dBrms/√Hz when the ASIC, coupled with the sensor, was in normal operation.