Noise-induced hearing loss (NIHL) is a hearing impairment resulting from exposure to loud sound
Noise-induced hearing loss (NIHL) is a hearing impairment resulting from exposure to loud sound
Noise-induced hearing loss (NIHL) occurs when individuals are exposed to loud sounds, either gradually from chronic noise or suddenly from impulse noise. This exposure overstimulates and can permanently damage delicate hearing cells, leading to irreversible hearing loss. NIHL can result from occupational exposures, loud music, or other high-intensity noises, making it a significant public health concern.
Example
A factory worker exposed to continuous loud machinery noise may gradually experience hearing loss, while a sudden blast from an airhorn can cause immediate hearing impairment.
Understanding the causes and prevention strategies for NIHL is crucial for protecting individuals' hearing health and reducing the prevalence of occupational hearing loss.
learning rate warmup does: starts small to avoid early training instability
Learning rate warmup gradually increases the learning rate from zero to a predefined value to stabilize training initially
Dropout (neural networks)
Dropout randomly sets neuron inputs/outputs to zero during training
a low-pass filter does: removes frequencies above a cutoff, keeps slow-varying signal
Low-pass filter: removes frequencies above cutoff, retains slow-varying signal
word error rate (WER) measures: edit distance between predicted and reference transcriptions
Word Error Rate (WER) measures the edit distance between predicted and reference transcriptions
log-loss / cross-entropy loss penalizes: confident wrong predictions more heavily
Log-loss penalizes confident incorrect predictions more heavily
the vocabulary size matters: larger vocab = shorter sequences but more parameters
Larger vocab reduces sequence length, increasing model complexity and parameters
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