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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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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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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.
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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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Classification of Systems-I01:26

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Classification of Systems-II01:31

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Related Experiment Video

Updated: Jul 19, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

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Online Continual Learning in Acoustic Scene Classification: An Empirical Study.

Donghee Ha1,2, Mooseop Kim1,2, Chi Yoon Jeong1,2

  • 1Artificial Intelligence Research Laboratory, Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Daejeon 34129, Republic of Korea.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
Summary
This summary is machine-generated.

This study evaluates ten continual learning (CL) methods for acoustic scene classification (ASC). SCR and GDumb performed best in different scenarios, offering guidance for future CL research in sound analysis.

Keywords:
acoustic scene classificationcatastrophic forgettingcontinual learningintransigenceonline learning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Signal Processing

Background:

  • Deep learning advances acoustic scene classification (ASC) accuracy for sound events.
  • Continual learning (CL) methods for adapting models to changing tasks in ASC are under-explored.
  • Systematic performance analysis of existing CL methods is needed to guide future research.

Purpose of the Study:

  • To systematically analyze and compare the performance of ten recent CL methods for ASC.
  • To provide guidelines for selecting appropriate CL methods based on different learning scenarios and memory buffer sizes.
  • To evaluate CL methods in realistic online class-incremental (OCI) and online domain-incremental (ODI) settings.

Main Methods:

  • Evaluated ten CL methods: two regularization-based and eight replay-based.
  • Defined and utilized OCI and ODI scenarios across three public sound datasets.
  • Assessed performance based on average accuracy, average forgetting, and training time.

Main Results:

  • In OCI scenarios, iCaRL and SCR excelled with small buffers; GDumb was best with large buffers.
  • In ODI scenarios, SCR with supervised contrastive learning consistently outperformed others, irrespective of buffer size.
  • Replay-based methods showed consistent training times and performance gains with larger memory buffers.

Conclusions:

  • GDumb and SCR are recommended as primary considerations for continual learning in ASC.
  • Method selection should depend on specific OCI or ODI scenarios and available memory buffer size.
  • Further research can build upon these findings for more robust and adaptive ASC systems.