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Related Concept Videos

Korotkoff Sounds01:12

Korotkoff Sounds

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Korotkoff sounds are the specific sounds heard while measuring blood pressure using a sphygmomanometer, typically with a stethoscope or a Doppler device. They are named after Russian physician Nikolai Korotkov, who first described them in 1905. These sounds correspond to turbulent blood flow in the artery as the blood pressure cuff is gradually released after inflation.
During blood pressure assessment, inflating the cuff 30 millimeters of mercury above the patient's systolic blood pressure...
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Heart Sounds01:15

Heart Sounds

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Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
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Soundness of Cement01:17

Soundness of Cement

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The soundness of cement refers to the ability of cement paste to retain its volume after setting. Unsound cement can lead to expansion and structural damage due to the presence of free lime, magnesia, and calcium sulfate. Free lime hydrates very slowly, expanding and causing unsoundness, which is difficult to detect because it intercrystallizes with other compounds. Magnesia also reacts with water, forming crystals that can disrupt the cement's structure. Calcium sulfate can create...
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Sound Waves01:01

Sound Waves

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Sound waves can be thought of as fluctuations in the pressure of a medium through which they propagate. Since the pressure also makes the medium's particles vibrate along its direction of motion, the waves can be modeled as the displacement of the medium's particles from their mean position.
Sound waves are longitudinal in most fluids because fluids cannot sustain any lateral pressure. In solids, however, shear forces help in propagating the disturbance in the lateral direction as well....
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Sound Intensity00:58

Sound Intensity

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The loudness of a sound source is related to how energetically the source is vibrating, consequently making the molecules of the propagation medium vibrate. To measure the loudness of a source, the physical quantity of interest is the intensity. This is defined as the energy emitted per unit of time per unit of area perpendicular to the sound wave's propagation direction. Since the total energy is greater if the source vibrates for a longer duration and over a larger area, dividing the...
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Speed of Sound in Gases01:08

Speed of Sound in Gases

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The speed of sound in a gaseous medium depends on various factors. Since gases constitute molecules that are free to move, they are highly compressible. Hence, sound waves travel slowly through gases. Thermodynamics helps us understand the relationship between pressure, volume, and temperature of gases, thus, the speed of sound in an ideal gas can be determined using the laws of thermodynamics. At the same time, Newton's laws of motion and the continuity equation of fluid dynamics also come...
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Ultrasound Images of the Tongue: A Tutorial for Assessment and Remediation of Speech Sound Errors
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Enhanced Robot Speech Recognition Using Biomimetic Binaural Sound Source Localization.

Jorge Davila-Chacon, Jindong Liu, Stefan Wermter

    IEEE Transactions on Neural Networks and Learning Systems
    |July 12, 2018
    PubMed
    Summary

    Robots can better understand speech in noisy places using an embodied embedded cognition approach. Sound source localization (SSL) helps robots orient towards speech, significantly improving automatic speech recognition (ASR) accuracy.

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    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Acoustics

    Background:

    • Automatic speech recognition (ASR) systems struggle in noisy environments, especially with robot-generated "ego noise".
    • Human communication strategies in noisy settings offer insights for improving ASR.
    • Binaural sound source localization (SSL) can potentially enhance ASR by directing the robot's focus.

    Purpose of the Study:

    • To propose and verify an embodied embedded cognition approach using SSL to improve robot ASR in challenging acoustic conditions.
    • To investigate the impact of a humanoid robot head's orientation, guided by SSL, on ASR performance.
    • To analyze the influence of sound wave reflection from the robot's pinna on ASR accuracy.

    Main Methods:

    • Implemented an embodied embedded cognition approach integrating SSL with ASR for robots.
    • Utilized a spiking neural network (SNN) for sound signal angle calculation, inspired by the midbrain auditory system.
    • Employed a feedforward neural network (FFN) to process signals with high ego noise and reverberation.
    • Tested the SSL-ASR system on two distinct humanoid robot platforms.

    Main Results:

    • The proposed SSL approach halved the sentence error rate compared to standard channel downmixing.
    • ASR performance improved more than twofold when sound waves reflected intensely from the robot's pinna.
    • The angle of sound wave incidence significantly impacts ASR performance, with reflected waves yielding better results.

    Conclusions:

    • Embodied embedded cognition with SSL enhances robot ASR in noisy environments.
    • Robot head orientation based on SSL is crucial for maximizing speech signal quality.
    • Acoustic reflections from the robot's structure, specifically the pinna, play a vital role in improving speech recognition.