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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.
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Related Experiment Video

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Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention
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Learning to Localize Sound Sources in Visual Scenes: Analysis and Applications.

Arda Senocak, Tae-Hyun Oh, Junsik Kim

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |November 15, 2019
    PubMed
    Summary

    Machines can now learn to localize sound sources in visual scenes using a novel unsupervised algorithm. However, human prior knowledge is needed to correct errors, which can be fixed with semi-supervised learning.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Audio Processing

    Background:

    • Real-world events involve synchronized visual and auditory information.
    • Machines struggle to correlate visual scenes with sounds and localize sound sources autonomously.

    Purpose of the Study:

    • To develop an unsupervised algorithm for sound source localization in visual scenes.
    • To investigate the empirical learnability of correlating audio-visual data.
    • To address limitations of unsupervised methods and explore supervised/semi-supervised alternatives.

    Main Methods:

    • A novel two-stream neural network with attention mechanisms for sound source localization.
    • Development of a new dataset for evaluating sound source localization performance.
    • Extension of the network to supervised and semi-supervised settings.

    Main Results:

    • The unsupervised algorithm can localize sound sources but produces errors due to correlation-causality mismatch.
    • Supervised and semi-supervised approaches effectively correct errors, even with limited labeled data.
    • Learned audio-visual embeddings demonstrate versatility in cross-modal alignment and video panning.

    Conclusions:

    • Unsupervised sound source localization is challenging due to inherent data ambiguities.
    • Semi-supervised learning offers a robust solution for accurate audio-visual sound localization.
    • The developed framework has potential applications in cross-modal content alignment and automated video production.