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Related Concept Videos

Perception of Sound Waves01:01

Perception of Sound Waves

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The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
The pitch of a sound depends on the frequency and the pressure amplitude of the source. Two sounds of the same...
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Perceiving Loudness, Pitch, and Location01:21

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The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
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Auditory Perception01:17

Auditory Perception

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The auditory system is essential for sound perception, utilizing various critical structures. When sound waves enter the outer ear, they travel through the ear canal and cause the eardrum to vibrate. These vibrations are then transmitted to the middle ear, where three tiny bones – the malleus, incus, and stapes – amplify the sound. This amplification is crucial, as it ensures that the sound vibrations are strong enough to be conveyed to the inner ear. These vibrations then reach the...
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Hearing01:31

Hearing

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When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.
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Classification of Signals01:30

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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Pervasive Sound Sensing: A Weakly Supervised Training Approach.

Daniel Kelly, Brian Caulfield

    IEEE Transactions on Cybernetics
    |February 13, 2015
    PubMed
    Summary
    This summary is machine-generated.

    Smartphone microphones can now sense human behavior with simplified training. This method enables accurate sound classification using less data, making behavior sensing more accessible.

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    Area of Science:

    • Human-computer interaction
    • Machine learning
    • Signal processing

    Background:

    • Smartphones are powerful pervasive sensing devices.
    • Microphone data offers insights into human behavior but is underutilized.
    • Current methods require extensive, detailed training data collection.

    Purpose of the Study:

    • To investigate if simplified, less detailed training data collection can enable effective microphone-based behavior sensing.
    • To develop flexible sound classification techniques for microphone data.
    • To reduce the time and expertise needed for training sound sensing models.

    Main Methods:

    • Implemented a diverse density-based multiple instance learning framework.
    • Developed a bag trimming algorithm for automatic segmentation of weakly labeled sound clips.
    • Constructed training sets using automated segmentation for classifier training.

    Main Results:

    • Validated the hypothesis that detailed training is not necessary.
    • Classifiers trained with automatically segmented data achieved high accuracy.
    • Average F-measures of 0.969 and 0.87 were obtained on two weakly supervised datasets, comparable to supervised methods.

    Conclusions:

    • Simplified data collection and automated training set construction are effective for microphone-based behavior sensing.
    • This approach enhances the utility and accessibility of smartphone microphones for human behavior analysis.
    • Future research can leverage these methods for more flexible and user-friendly sensing applications.