• 제목/요약/키워드: categorical boundary

검색결과 19건 처리시간 0.019초

Effect of Music Training on Categorical Perception of Speech and Music

  • L., Yashaswini;Maruthy, Sandeep
    • Journal of Audiology & Otology
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    • 제24권3호
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    • pp.140-148
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    • 2020
  • Background and Objectives: The aim of this study is to evaluate the effect of music training on the characteristics of auditory perception of speech and music. The perception of speech and music stimuli was assessed across their respective stimulus continuum and the resultant plots were compared between musicians and non-musicians. Subjects and Methods: Thirty musicians with formal music training and twenty-seven non-musicians participated in the study (age: 20 to 30 years). They were assessed for identification of consonant-vowel syllables (/da/ to /ga/), vowels (/u/ to /a/), vocal music note (/ri/ to /ga/), and instrumental music note (/ri/ to /ga/) across their respective stimulus continuum. The continua contained 15 tokens with equal step size between any adjacent tokens. The resultant identification scores were plotted against each token and were analyzed for presence of categorical boundary. If the categorical boundary was found, the plots were analyzed by six parameters of categorical perception; for the point of 50% crossover, lower edge of categorical boundary, upper edge of categorical boundary, phoneme boundary width, slope, and intercepts. Results: Overall, the results showed that both speech and music are perceived differently in musicians and non-musicians. In musicians, both speech and music are categorically perceived, while in non-musicians, only speech is perceived categorically. Conclusions: The findings of the present study indicate that music is perceived categorically by musicians, even if the stimulus is devoid of vocal tract features. The findings support that the categorical perception is strongly influenced by training and results are discussed in light of notions of motor theory of speech perception.

Effect of Music Training on Categorical Perception of Speech and Music

  • L., Yashaswini;Maruthy, Sandeep
    • 대한청각학회지
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    • 제24권3호
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    • pp.140-148
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    • 2020
  • Background and Objectives: The aim of this study is to evaluate the effect of music training on the characteristics of auditory perception of speech and music. The perception of speech and music stimuli was assessed across their respective stimulus continuum and the resultant plots were compared between musicians and non-musicians. Subjects and Methods: Thirty musicians with formal music training and twenty-seven non-musicians participated in the study (age: 20 to 30 years). They were assessed for identification of consonant-vowel syllables (/da/ to /ga/), vowels (/u/ to /a/), vocal music note (/ri/ to /ga/), and instrumental music note (/ri/ to /ga/) across their respective stimulus continuum. The continua contained 15 tokens with equal step size between any adjacent tokens. The resultant identification scores were plotted against each token and were analyzed for presence of categorical boundary. If the categorical boundary was found, the plots were analyzed by six parameters of categorical perception; for the point of 50% crossover, lower edge of categorical boundary, upper edge of categorical boundary, phoneme boundary width, slope, and intercepts. Results: Overall, the results showed that both speech and music are perceived differently in musicians and non-musicians. In musicians, both speech and music are categorically perceived, while in non-musicians, only speech is perceived categorically. Conclusions: The findings of the present study indicate that music is perceived categorically by musicians, even if the stimulus is devoid of vocal tract features. The findings support that the categorical perception is strongly influenced by training and results are discussed in light of notions of motor theory of speech perception.

Predictive Spatial Data Fusion Using Fuzzy Object Representation and Integration: Application to Landslide Hazard Assessment

  • Park, No-Wook;Chi, Kwang-Hoon;Chung, Chang-Jo;Kwon, Byung-Doo
    • 대한원격탐사학회지
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    • 제19권3호
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    • pp.233-246
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    • 2003
  • This paper presents a methodology to account for the partial or gradual changes of environmental phenomena in categorical map information for the fusion/integration of multiple spatial data. The fuzzy set based spatial data fusion scheme is applied in order to account for the fuzziness of boundaries in categorical information showing the partial or gradual environmental impacts. The fuzziness or uncertainty of boundary is represented as two kinds of fuzzy membership functions based on fuzzy object concept and the effects of them are quantitatively evaluated with the help of a cross validation procedure. A case study for landslide hazard assessment demonstrates the better performance of this scheme as compared to traditional crisp boundary representation.

Bayesian approach for categorical Table with Nonignorable Nonresponse

  • Choi, Bo-Seung;Park, You-Sung
    • 한국통계학회:학술대회논문집
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    • 한국통계학회 2005년도 추계 학술발표회 논문집
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    • pp.59-65
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    • 2005
  • We propose five Bayesian methods to estimate the cell expectation in an incomplete multi-way categorical table with nonignorable nonresponse mechanism. We study 3 Bayesian methods which were previously applied to one-way categorical tables. We extend them to multi-way tables and, in addition, develop 2 new Bayesian methods for multi-way categorical tables. These five methods are distinguished by different priors on the cell probabilities: two of them have the priors determined only by information of respondents; one has a constant prior; and the remaining two have priors reflecting the difference in the response mechanisms between respondent and non-respondent. We also compare the five Bayesian methods using a categorical data for a prospective study of pregnant women.

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Complex Segregation Analysis of Categorical Traits in Farm Animals: Comparison of Linear and Threshold Models

  • Kadarmideen, Haja N.;Ilahi, H.
    • Asian-Australasian Journal of Animal Sciences
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    • 제18권8호
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    • pp.1088-1097
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    • 2005
  • Main objectives of this study were to investigate accuracy, bias and power of linear and threshold model segregation analysis methods for detection of major genes in categorical traits in farm animals. Maximum Likelihood Linear Model (MLLM), Bayesian Linear Model (BALM) and Bayesian Threshold Model (BATM) were applied to simulated data on normal, categorical and binary scales as well as to disease data in pigs. Simulated data on the underlying normally distributed liability (NDL) were used to create categorical and binary data. MLLM method was applied to data on all scales (Normal, categorical and binary) and BATM method was developed and applied only to binary data. The MLLM analyses underestimated parameters for binary as well as categorical traits compared to normal traits; with the bias being very severe for binary traits. The accuracy of major gene and polygene parameter estimates was also very low for binary data compared with those for categorical data; the later gave results similar to normal data. When disease incidence (on binary scale) is close to 50%, segregation analysis has more accuracy and lesser bias, compared to diseases with rare incidences. NDL data were always better than categorical data. Under the MLLM method, the test statistics for categorical and binary data were consistently unusually very high (while the opposite is expected due to loss of information in categorical data), indicating high false discovery rates of major genes if linear models are applied to categorical traits. With Bayesian segregation analysis, 95% highest probability density regions of major gene variances were checked if they included the value of zero (boundary parameter); by nature of this difference between likelihood and Bayesian approaches, the Bayesian methods are likely to be more reliable for categorical data. The BATM segregation analysis of binary data also showed a significant advantage over MLLM in terms of higher accuracy. Based on the results, threshold models are recommended when the trait distributions are discontinuous. Further, segregation analysis could be used in an initial scan of the data for evidence of major genes before embarking on molecular genome mapping.

Categorical Perception in intonation

  • Lee, Ho-Young
    • 대한음성학회:학술대회논문집
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    • 대한음성학회 2002년도 11월 학술대회지
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    • pp.86-89
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    • 2002
  • According to Pierrehumbert (1980), two level tones - H and L - are enough in representing intonation of intonational languages. But in Korean, high fall and low fall boundary tones, both of which must be represented as HL% in intonational phonology as in Jun (1993, 1999), are distinct not only acoustically but also functionally. The same is true in the case of high level and mid level boundary tones, which must be represented as H% in intonational phonology. In this paper, I conducted two identification tests to provide crucial evidence that H and L are not enough in intonational phonology. The results of the identification tests show that categorical perception occur between high level and low level as well as between high fall and low fall. Based on this fact and the results of the acoustic analyses in Lee (1999, 2000), I strongly propose to adopt one more level tone - M - to represent Korean boundary tones.

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Gradient Reduction of $C_1$ in /pk/ Sequences

  • Son, Min-Jung
    • 음성과학
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    • 제15권4호
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    • pp.43-60
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    • 2008
  • Instrumental studies (e.g., aerodynamic, EPG, and EMMA) have shown that the first of two stops in sequence can be articulatorily reduced in time and space sometimes; either gradient or categorical. The current EMMA study aims to examine possible factors_linguistic (e.g., speech rate, word boundary, and prosodic boundary) and paralinguistic (e.g., natural context and repetition)_to induce gradient reduction of $C_1$ in /pk/ cluster sequences. EMMA data are collected from five Seoul-Korean speakers. The results show that gradient reduction of lip aperture seldom occurs, being quite restricted both in speaker frequency and in token frequency. The results also suggest that the place assimilation is not a lexical process, implying that speakers have not fully developed this process to be phonologized in the abstract level.

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유한 셀룰러 오토마타 규칙 15에 대한 카테고리적 분석 (Categorical Analysis for Finite Cellular Automata Rule 15)

  • 박정희;이현열
    • 한국정보과학회논문지:시스템및이론
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    • 제27권8호
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    • pp.752-757
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    • 2000
  • 두가지 상태값 (0, 1)과 서로 다른 네가지 경계조건을 갖는 1차원 셀룰러 오토마타 규칙 15의 상태전이그래프를 자기 재생시킬 수 있는 재귀식을 카테고리적 접근법으로 발견하였다. 카테고리적 접근법은 서로 다른 도메인을 갖는 오토마타들 간의 매핑을 가능케하므로 오토마타의 진화과정을 쉽게 표현할 수 있도록 한다.

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Effects of Uncertain Spatial Data Representation on Multi-source Data Fusion: A Case Study for Landslide Hazard Mapping

  • Park No-Wook;Chi Kwang-Hoon;Kwon Byung-Doo
    • 대한원격탐사학회지
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    • 제21권5호
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    • pp.393-404
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    • 2005
  • As multi-source spatial data fusion mainly deal with various types of spatial data which are specific representations of real world with unequal reliability and incomplete knowledge, proper data representation and uncertainty analysis become more important. In relation to this problem, this paper presents and applies an advanced data representation methodology for different types of spatial data such as categorical and continuous data. To account for the uncertainties of both categorical data and continuous data, fuzzy boundary representation and smoothed kernel density estimation within a fuzzy logic framework are adopted, respectively. To investigate the effects of those data representation on final fusion results, a case study for landslide hazard mapping was carried out on multi-source spatial data sets from Jangheung, Korea. The case study results obtained from the proposed schemes were compared with the results obtained by traditional crisp boundary representation and categorized continuous data representation methods. From the case study results, the proposed scheme showed improved prediction rates than traditional methods and different representation setting resulted in the variation of prediction rates.

범주형 데이터의 분류를 위한 퍼지 군집화 기법 (A Fuzzy Clustering Algorithm for Clustering Categorical Data)

  • 김대원;이광형
    • 한국지능시스템학회논문지
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    • 제13권6호
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    • pp.661-666
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    • 2003
  • 본 논문에서는 범주형 데이터의 분류를 위한 새로운 기법을 제시한다. 기존의 대표적인 퍼지 군집화 방법인 k-modes 알고리즘과 fuzzy k-modes 알고리즘은 군집의 중심을 단일 값으로 표현하고, 군집에 속하는 데이터의 빈도 수에 기반한 중신 갱신 기법을 사용하였다. 이와 같은 기존의 방법들은 분류의 경계가 모호한 데이트를 군집화할 경우, 알고리즘의 각 단계에서 발생하는 분류의 에러를 보정하지 못해 최종적으로 지역해에 빠지는 단점이 있다. 이를 극복하기 위해 본 논문에서는 군집 중심을 퍼지 집합을 이용하여 정의한다. 퍼지 군집 중심은 주어진 데이터와 군집간의 거리 관계를 퍼지 값을 이용해 표현하며, 각 군집의 중심은 데이터의 소속 정도 값을 이용해 갱신된다. 이와 같은 퍼지 중심 표현기법을 도입하여 범주형 데이터의 분류 시에 보다 세밀한 결정을 내림으로써, 인접한 군집들의 경계에서 발생하는 불확실성을 최소화한다. 기존의 대표적인 방법들과의 비교실험을 수행함으로써 제안한 방법의 성능을 검증하였다.