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Related Concept Videos

Determination of Expected Frequency01:08

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Relative Frequency Histogram01:14

Relative Frequency Histogram

The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
Relative Frequency Distribution00:55

Relative Frequency Distribution

A relative frequency distribution is the proportion or fraction of times a value occurs in a data set. To find the relative frequencies, one can divide each frequency by the total number of data points in the sample. It is very similar to a regular frequency distribution, except that instead of reporting how many data values fall in a class, a relative frequency distribution reports the fraction of data values that fall in a class. These fractions or proportions are called relative frequencies...
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
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Relative weights for frequency glide detection using narrowband noise.

Jinyu Qian1, Virginia M Richards

  • 1Department of Psychology, 3401 Walnut Street, Suite 302C, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA. qianj@sas.upenn.edu

The Journal of the Acoustical Society of America
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Summary

This study explored how people detect frequency changes in noise. Results show listeners better detect glides when the center frequency is fixed, and pay more attention to later parts of sounds.

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Area of Science:

  • Auditory perception
  • Psychoacoustics
  • Signal detection theory

Background:

  • Understanding auditory perception is crucial for fields like audiology and human-computer interaction.
  • Previous research has investigated the detection of auditory signals in noise, but the temporal weighting of frequency glides requires further clarification.

Purpose of the Study:

  • To determine the relative temporal weights listeners assign to frequency glides within narrowband noise.
  • To compare detection performance and temporal weighting strategies under fixed versus random center frequency conditions.

Main Methods:

  • Employed a yes/no procedure using narrowband noise stimuli with linear frequency glides.
  • Investigated two stimulus conditions: glide-only and glide with preceding/following noise fringes.
  • Utilized a linear model and linear classification to derive relative temporal weights.

Main Results:

  • Detection sensitivity was higher in fixed-frequency conditions compared to random-frequency conditions.
  • Temporal weight patterns were less reliable in random-frequency conditions.
  • Listeners showed a tendency to assign larger weights to the latter half of the stimulus, indicating greater attention to later parts.
  • A linear model required relative frequency changes, not absolute, to accurately predict performance in random-frequency conditions.

Conclusions:

  • Listener performance in detecting frequency glides is influenced by the predictability of the center frequency.
  • Temporal weighting strategies in auditory signal detection are dynamic and context-dependent.
  • Future research should consider relative frequency changes when modeling auditory detection in variable frequency environments.