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Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

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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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Perception of Sound Waves01:01

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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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Auditory Perception01:17

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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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Linear Approximation in Frequency Domain01:26

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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.
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Multi-input and Multi-variable systems01:22

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Auditory Pathway01:15

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Auditory pathways constitute the complex neural circuits responsible for transmitting and interpreting auditory information from the peripheral auditory system to the brain. Sound waves are initially captured by the outer ear, funneled through the ear canal, and reach the tympanic membrane (eardrum). These vibrations are transmitted via the middle ear's ossicles to the inner ear's cochlea.
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Related Experiment Video

Updated: Dec 13, 2025

A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds
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Polyphonic pitch tracking with deep layered learning.

Anders Elowsson1

  • 1KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science, Stockholm, Sweden.

The Journal of the Acoustical Society of America
|August 6, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel polyphonic pitch tracking system using deep learning neural networks. The system achieves state-of-the-art performance in fundamental frequency (f0) and note-based pitch estimation across multiple datasets.

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Area of Science:

  • Music Information Retrieval
  • Signal Processing
  • Machine Learning

Background:

  • Accurate polyphonic pitch tracking is crucial for music analysis and transcription.
  • Existing systems often struggle with complex polyphonic audio signals.

Purpose of the Study:

  • To develop an advanced polyphonic pitch tracking system capable of both framewise and note-based pitch estimation.
  • To leverage deep learning architectures for improved accuracy and robustness.

Main Methods:

  • Utilized a deep layered learning setup with cascading artificial neural networks.
  • Employed framewise fundamental frequency (f0) estimation using spectrogram analysis and learned sparse receptive fields.
  • Integrated pitch contour extraction, onset/offset detection, and iterative note classification for robust performance.

Main Results:

  • Achieved state-of-the-art results on four public datasets (MAPS, Bach10, TRIOS, MIREX Woodwind quintet).
  • Demonstrated high performance across all subtasks: f0 estimation, pitched onset, and pitched offset tracking.
  • The system effectively handles complex polyphonic audio, outperforming previous benchmarks.

Conclusions:

  • The proposed polyphonic pitch tracking system represents a significant advancement in the field.
  • The deep learning approach offers a robust and accurate solution for music information retrieval tasks.
  • This system provides a strong foundation for future research in automatic music transcription and analysis.