Verbal Attributes of Simultaneous Wind Instrument Timbres: II. Adjectives Induced from Piston's "Orchestration"

1993 ◽  
Vol 10 (4) ◽  
pp. 469-501 ◽  
Author(s):  
Roger A. Kendall ◽  
Edward C. Carterette
Keyword(s):  
Principal Components ◽  
Rating Scales ◽  
Perceptual Similarity ◽  
Two Dimensional ◽  
Wind Instrument ◽  
Principal Dimension ◽  
Components Analysis ◽  
The Relationship ◽  
Compare And Contrast ◽  
Verbal Attribute

Experiments were conducted to explore the relationship between wind instrument dyad timbres and verbal attributes drawn from the musicological literature in order to compare and contrast results with studies using artificial stimuli and amusical rating scales. In the first experiment, all adjectives in Piston's (1955) "Orchestration" were collated and subsequently reduced to 61 examples. A checklist procedure was used in response to the dyad timbres to further reduce the set of adjectives to 21. The 21 adjectives were used in verbal attribute magnitude estimation of the 10 wind instrument dyad timbres. Principal components analysis of ratings revealed four verbal attribute factors, accounting for 90.604% of the variance: power, strident, plangent, and reed. Correlational analyses demonstrated an improvement in mapping between the ratings and perceptual similarity spaces over the procedure used in Part I of this study. The two-dimensional timbral circumplex was interpreted as having a principal dimension of nasality versus richness and a secondary dimension of reediness versus brilliance.

Face Recognition Algorithm Based on Kernel Collaborative Representation

2013 ◽  
Vol 756-759 ◽  
pp. 3590-3595
Author(s):  
Liang Zhang ◽  
Ji Wen Dong
Keyword(s):  
Face Recognition ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Recognition Algorithm ◽  
Experimental Results ◽  
Collaborative Representation ◽  
Nonlinear Structures ◽  
Components Analysis ◽  
Kernel Principal Components Analysis ◽  
The Relationship

Aiming at solving the problems of occlusion and illumination in face recognition, a new method of face recognition based on Kernel Principal Components Analysis (KPCA) and Collaborative Representation Classifier (CRC) is developed. The KPCA can obtain effective discriminative information and reduce the feature dimensions by extracting faces nonlinear structures features, the decisive factor. Considering the collaboration among the samples, the CRC which synthetically consider the relationship among samples is used. Experimental results demonstrate that the algorithm obtains good recognition rates and also improves the efficiency. The KCRC algorithm can effectively solve the problem of illumination and occlusion in face recognition.

scholarly journals Impulsivity in the eye of the beholder: an analysis of teachers' concepts of impulsive and reflective behaviour

1993 ◽  
Vol 7 (1) ◽  
pp. 47-63 ◽  
Author(s):  
Mechteld R. M. Visser ◽  
Edith A. Das‐Smaal ◽  
Jan A. Feij
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Social Behaviour ◽  
Rating Scales ◽  
Teacher Rating ◽  
One Dimension ◽  
Analysis Study ◽  
Components Analysis ◽  
Item Pools

The aim of this research was to investigate teachers' perception of the concept of impulsivity and its relation to the concept of reflectivity. Teacher rating scales for both concepts were constructed by means of the prototype method (Study 1). It appeared that impulsivity refers to social behaviour, whereas reflectivity is more cognitive in character. A Principal Components Analysis (Study 2) showed that the item pools for impulsive and reflective behaviour account for separate components. It is concluded that impulsivity and reflectivity as perceived by teachers are not two extremes of one dimension, but refer to different behavioural domains: social and cognitive.

Personality Trait Absorption: An Exploratory Study of Opportunity and Capacity in Relation to Cannabis Use

1995 ◽  
Vol 15 (1) ◽  
pp. 75-101
Author(s):  
Peter L. Nelson
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Personality Trait ◽  
Exploratory Study ◽  
Cannabis Use ◽  
Components Analysis ◽  
Relationship Of ◽  
The Relationship ◽  
Tellegen Absorption Scale ◽  
Motivation Loss

Although the Tellegen Absorption Scale has been widely employed in recent years as a measure of personality Trait Absorption, it is argued that this simple score does not sufficiently discriminate true capacity for Absorption nor does it reveal the level of opportunity made for absorptive experiencing. This study operationalizes Capacity and Opportunity as two additional subscales appended to the Tellegen scale and, by employing the technique of Principal Components Analysis, five useful sub-dimensions are generated. Following on from this Author's earlier suggestion that personality Trait Absorption may be linked to cannabis use and depression, an exploratory study was conducted into the relationship of cannabis use, gender, self-perceived motivation loss and depression to observed levels of overall Absorption as well as to levels of Capacity and Opportunity for absorptive experiencing.

scholarly journals Use of proximal sensor for soil classes separation applying Principal Component Analysis (PCA)

2020 ◽  
Vol 10 (3) ◽  
pp. 130
Author(s):  
Valéria Ramos Lourenço ◽  
David Bruno de Sousa Teixeira ◽  
Carlos Alexandre Gomes Costa ◽  
Calors Alberto Kenji Taniguchi
Keyword(s):  
Principal Components ◽  
Spectral Reflectance ◽  
Principal Component ◽  
Graphical Analysis ◽  
Soil Samples ◽  
Two Dimensional ◽  
Active Components ◽  
Components Analysis ◽  
Experimental Basin ◽  
Reflectance Data

The spectrally active components of the soil allow the realization of integrative analyzes of soil aspects such as their classification. The purpose of this study was to evaluate the separation of soil classes from spectral reflectance data using principal components analysis (PCA). The study was carried out in the Aiuaba Experimental Basin located in the municipality of Aiuaba, Ceará, Brazil. Soil samples were collected in Ustalfs, Ustults and Ustorthents profiles. The samples were submitted to spectral analysis by a spectroradiometer and, subsequently, to PCA. Principal components were used to identify which of them contribute more significantly to the separation of the soil classes analyzed, based on their relationship with the soil attributes using a two-dimensional graphical analysis. From the examination of spectral behavior data of the different soil classes, the use of PCA allowed the separation of the classes Ustorthents, Ustalfs and Ustults from each other.

Acoustic Dimensions of Hearing-Impaired Speakers’ Intelligibility

1990 ◽  
Vol 33 (3) ◽  
pp. 476-487 ◽  
Author(s):  
Dale Evan Metz ◽  
Vincent J. Samar ◽  
Nicholas Schiavetti ◽  
Ronald W. Sitler
Keyword(s):  
Principal Components ◽  
Speech Intelligibility ◽  
Spatial Differentiation ◽  
Hearing Impaired ◽  
Production Processes ◽  
Two Factors ◽  
Components Analysis ◽  
Temporal And Spatial ◽  
Predictive Relationships ◽  
The Relationship

Regression and principal components analyses were employed to study the relationship between 28 segmental and suprasegmental acoustic parameters of speech production and measures of speech intelligibility for 40 severely to profoundly hearing-impaired persons in an effort to extend the findings of Metz, Samar, Schiavetti, Sitler, and Whitehead (1985). The principal components analysis derived six factors that accounted for 59% of the variance in the original 28 parameters. Consistent with the findings of Metz et al., a subsequent regression analysis using these six factors as predictor variables revealed two factors with strong predictive relationships to speech intelligibility. One factor primarily reflected segmental production processes related to the temporal and spatial differentiation of phonemes, whereas the other primarily reflected suprasegmental production processes associated with contrastive stress. However, the predictive capability of the present factor structure was somewhat reduced relative to the findings of Metz et al. (1985). Data presented indicate that the populations sampled in the two studies may have differed on one or more dimensions of subject characteristics. Considered collectively, the present findings and the findings of Metz et al. support the tractability of employing selected acoustic variables for the estimation of speech intelligibility.

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