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Related Concept Videos

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single stretching vibration...
IR Frequency Region: X–H Stretching01:24

IR Frequency Region: X–H Stretching

In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of  2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in the 3500–3100 cm−1 range. Even though both O−H and N−H bonds vibrate at a similar...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
Bandpass Sampling01:17

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...
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.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.

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Targeted Training of Ultrasonic Vocalizations in Aged and Parkinsonian Rats
11:00

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Published on: August 8, 2011

[Use of narrow-band spectrogram in voice surgery].

F Núñez Batalla1, C Suárez Nieto, M Maldonado Fernández

  • 1Universidad de Oviedo, Oviedo, Asturias, España.

Acta Otorrinolaringologica Espanola
|June 27, 2000
PubMed
Summary
This summary is machine-generated.

Voice spectrography offers an objective method to assess hoarseness severity before and after surgery. This technique numerically classifies voice changes, aiding clinical evaluation.

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Area of Science:

  • Otolaryngology
  • Acoustic analysis
  • Speech science

Context:

  • Subjective voice qualities present challenges for objective measurement.
  • Voice spectrography is an established but underutilized clinical tool for voice assessment.
  • Evaluating voice changes pre- and post-surgical procedures requires objective methods.

Purpose:

  • To explore the clinical utility of voice spectrography for objective voice evaluation.
  • To apply the Yanagihara method for classifying hoarseness based on acoustic factors.
  • To numerically express the severity of hoarseness and analyze harmonic component changes.

Summary:

  • The Yanagihara method classifies hoarseness by analyzing noise and harmonic components in voice spectrographs.
  • This method provides a numerical expression of hoarseness severity, aiding clinical assessment.
  • Voice spectrographic analysis was employed to examine alterations in harmonic components.

Impact:

  • Enhances objective assessment of voice quality in clinical otolaryngology.
  • Provides a quantifiable measure for evaluating surgical voice procedure outcomes.
  • Facilitates more precise clinical decision-making in voice disorder management.