• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.1442-1445
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• 2007

Just noticeable differences (jnds) of autocorrelation function (ACF) parameters - Phi (0), Tau 1 and Phi 1 - of household refrigerator noise were investigated by psychoacoustical analyses. Phi (0) of five refrigerators' noise was changed with equal (${\pm}$) interval level of 0.5-1.0 dB up to five intervals by manipulating sound pressure level of the noise. Tau 1 and Phi 1 were varied with equal (${\pm}$) interval of around 0.10 ms and 0.02, respectively. Pitch shifting and strengthen methods were applied for the Tau 1 and Phi 1 variations. As results of subjective evaluations, about 2.0 dB was shown as jnd of Phi (0). The values of 0.30 ms and 0.06 were found as jnds of Tau 1 and Phi 1, respectively. The jnd results of each ACF parameter can be applied to explain substantial amount of sound quality (SQ) enhancement in the SQ prediction indices which were proposed in the authors' previous study [Sato et al. (2007) J. Acoust. Soc. Am.].

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 추계학술대회논문집
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• pp.661-665
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• 2006

This study investigates objective and subjective evaluations of refrigerator noise. To describe the fluctuations like a click, a rapid increase of sound level, a change of pitch, a transition into the stationary and ending phase, the psychoacoustical and autocorrelation function(ACF) parameters have been employed. First, subjective evaluation of the noisiness of 24 kinds of refrigerators was conducted. Then, the relationship between objective measures of the refrigerator noise on perceived noisiness was examined with multiple regression analyses. Sound Quality Indices using the psychoacoustical and ACF parameters were also developed. The important psychoacoustical parameters for evaluating noisiness are loudness and roughness of stationary phase. The relationship between the noisiness and the ACF parameters shows that sound energy ${\Phi}(0)$ and its fluctuations are important. Also, refrigerator sounds that had a fluctuation of pitch were rated as more annoying. The fluctuation of pitch is expressed by ${\tau}_1\;and\;{\Phi}_1$ defined by the delay time and the amplitude of the first peak of the ACF.

• ETRI Journal
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• 제38권3호
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• pp.425-434
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• 2016

A novel average magnitude difference function (AMDF)-based pitch detection scheme (PDS) is proposed to achieve better performance in speech quality. A performance evaluation of the proposed PDS is carried out through both a simulation and a real-time implementation of a speech analysis-synthesis system. The parameters used to compare the performance of the proposed PDS with that of PDSs that are based on either a cepstrum, an autocorrelation function (ACF), an AMDF, or circular AMDF (CAMDF) methods are as follows: percentage gross pitch error (%GPE); a subjective listening test; an objective speech quality assessment; a speech intelligibility test; a synthesized speech waveform; computation time; and memory consumption. The proposed PDS results in lower %GPE and better synthesized speech quality and intelligibility for different speech signals as compared to the cepstrum-, ACF-, AMDF-, and CAMDF-based PDSs. The computational time of the proposed PDS is also less than that for the cepstrum-, ACF-, and CAMDF-based PDSs. Moreover, the total memory consumed by the proposed PDS is less than that for the ACF- and cepstrum-based PDSs.

• 예술인문사회 융합 멀티미디어 논문지
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• 제7권4호
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• pp.235-242
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• 2017

본 논문은 음성 신호의 Average Magnitude Difference Function (AMDF)에서 peaks (혹은 nulls)들을 찾는 알고리즘들을 제안하였다. AMDF 함수는 Autocorrelation Function (ACF)과 같이 음성 신호의 피치를 추정하는 함수로 널리 사용 하고 있다. 음성신호에서 fundamental frequency (F0)를 estimation하는 것은 매우 중요한 task이며 또한 상당한 어려움이 따른다는 것이 여러 연구들을 통해서 잘 알려진 사실이다. 본 논문에서는 AMDF 함수의 특성을 이용하여 개발한 두 가지의 알고리즘을 제시하였다. 첫째는 Local Minima에 Threshold 값을 적용하여 피치 주기를 측정 할 수 있는 nulls들을 찾아내는 알고리즘이고, 다음은 AMDF 함수와 ACF 함수 사이의 관계식을 응용한 알고리즘이다. 한국어의 감정 표현 언어들로 구성된 제시문을 널리 사용하고 있는 상용 기기로 녹음한 음성 신호를 본 논문이 제안한 알고리즘들에 적용하여서 시뮬레이션을 통해 음성 신호의 피치 주기를 측정하여서 그 성능을 알아보았다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 추계학술대회논문집
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• pp.666-670
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• 2006

It has been considered that scattered sounds have a positive effect on a hearing impression of a sound filed. This study investigates the degree and the quality of a scattered sound field by using the acoustical parameters and autocorrelation function(ACF) of impulse responses. The acoustical parameters and fine structure of the ACF of an impulse response were used for the evaluation of the scattered sound field. The relationship between the scattering coefficient of surfaces with various hemisphere diffuser configurations and the acoustical parameters and ACF parameters of impulse responses was investigated.

• 한국음향학회:학술대회논문집
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• 한국음향학회 2001년도 추계학술발표대회 논문집 제20권 2호
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• pp.271-274
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• 2001

강의실의 언어 요해도에 관한 연구는 그 평가방법과 지표에 대한 주제로 꾸준히 이루어져 왔다. 기존의 지표들에서는 전체 음에너지에 대한 초기음에너지의 비율로 계산하거나(C80, D50) 주파수 대역별 음에너지의 감쇠율(RASTI)을 구하여 명료도를 평가하였다. 본 연구에서는 Autocorrelation Function (ACF)의 시간적 요소를 통해 강의실의 언어 요해도를 평가하였다. 먼저 시뮬레이션 대상 강의실에서 무향실 음원이 제시되었을 때 12개 수음점에서 음향특성을 측정$\cdot$분석하였으며 청감실험과 ACF 분석을 통해 강의실 요해도와의 상관관계 규명하였다

• 한국컴퓨터정보학회논문지
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• 제21권12호
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• pp.131-137
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• 2016

In this paper, the peak finding algorithms corresponding to the Autocorrelation Function (ACF), which are widely exploited for detecting the pitch of voiced signal, are proposed. According to various researchers, it is well known fact that the estimation of fundamental frequency (F0) in speech signal is not only very important task but quite difficult mission. The proposed algorithms, presented in this paper, are implemented by using many characteristics - such as monotonic increasing function - of ACF function. Thus, the proposed algorithms may be able to estimate both reliable and correct the fundamental frequency as long as the autocorrelation function of speech signal is accurate. Since the proposed algorithms may reduce the computational complexity it can be applied to the real-time processing. The speech data, is composed of Korean emotion expressed words, is used for evaluation of their performance. The pitches are measured to compare the performance of proposed algorithms.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2001년도 추계학술대회논문집 II
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• pp.945-950
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• 2001

Floor impact noise has been evaluated by investigating the temporal and spectral characteristics of the noise. The noises generated by different impactors were analyzed to find out whether there is any correlation with the factors of ACF /IACF (Autocorrelation Function/Inter-aural Cross-correlation Function) [1] and Zwicker parameters [2]. Experiments were undertaken to compare the objective and subjective parameters of the floor impact noises generated by a bang/tapping machine, a rubber ball [3], and a walker. As a result, it was found that $\phi$ (0) and IACC extracted from ACF/IACF, and Loudness, Unbiased Annoyance from Zwicker parameters showed high correlation with subjective evaluations of loudness concerning floor impact noises. In addition, it was revealed that jumping is similar to the ball.

• International Journal of Railway
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• 제6권3호
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• pp.120-124
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• 2013

The aim of this study was to clarify the characteristics of interior noise in Japanese, Korean, and Chinese subways. The octave-band noise levels, A-weighted equivalent continuous sound pressure level ($L_{Aeq}$) and parameters extracted from interaural cross-correlation/autocorrelation functions (ACF/IACFs) were analyzed to evaluate the noise inside running train cars quantitatively and qualitatively. The average $L_{Aeq}$ was 72-83 dBA. The IACF/ACF parameters of the noise showed variations in their values, suggesting they are affected by the characteristics of the trains running, wheel-rail interaction, and cross-section of the tunnels.

• ETRI Journal
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• 제43권1호
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• pp.82-94
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• 2021

In this work, six voiced/unvoiced speech classifiers based on the autocorrelation function (ACF), average magnitude difference function (AMDF), cepstrum, weighted ACF (WACF), zero crossing rate and energy of the signal (ZCR-E), and neural networks (NNs) have been simulated and implemented in real time using the TMS320C6713 DSP starter kit. These speech classifiers have been integrated into a linear-predictive-coding-based speech analysis-synthesis system and their performance has been compared in terms of the percentage of the voiced/unvoiced classification accuracy, speech quality, and computation time. The results of the percentage of the voiced/unvoiced classification accuracy and speech quality show that the NN-based speech classifier performs better than the ACF-, AMDF-, cepstrum-, WACF- and ZCR-E-based speech classifiers for both clean and noisy environments. The computation time results show that the AMDF-based speech classifier is computationally simple, and thus its computation time is less than that of other speech classifiers, while that of the NN-based speech classifier is greater compared with other classifiers.