Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Encoding01:19

Encoding

243
Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
243
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

147
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.
In the absence...
147
Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

419
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...
419
Neural Circuits01:25

Neural Circuits

1.5K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.5K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

A conditional diffusion-based model for high-resolution acoustic source mapping.

The Journal of the Acoustical Society of America·2026
Same author

Two-dimensional flexural wave vortices based on ring-shaped and partitioned acoustic black hole metasurface.

The Journal of the Acoustical Society of America·2026
Same author

Underwater acoustic Luneburg lens based on a square-lattice isotropic truss structure.

The Journal of the Acoustical Society of America·2025
Same author

Differential Volterra filter: A two-stage decoupling method for audible sounds generated by parametric array loudspeakers based on Westervelt equation.

The Journal of the Acoustical Society of America·2025
Same author

A lightweight speech enhancement network fusing bone- and air-conducted speech.

The Journal of the Acoustical Society of America·2024
Same author

Directional sound radiation from a rectangular panel and the high frequency limit.

JASA express letters·2024

関連する実験動画

Updated: Sep 9, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K

音源マッピングのための事前の知識の埋め込み付きの二重エンコーダーU-netアーキテクチャ

Haobo Jia1,2, Feiran Yang3, Xiaoqing Hu1

  • 1Laboratory of Noise and Audio Research, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.

The Journal of the Acoustical Society of America
|September 4, 2025
PubMed
まとめ
この要約は機械生成です。

この研究は,音源マッピングのための新しいディープラーニングフレームワークを導入し,二重ビーム形成マップを使用し,ポイントスプレッド機能の変動を考慮することで精度を改善します. この方法は,複雑な音響環境における 計算効率と局所精度を高めます

さらに関連する動画

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

633
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.8K

関連する実験動画

Last Updated: Sep 9, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

633
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.8K

科学分野:

  • 音響学
  • 信号処理
  • 機械学習

背景:

  • デコンボリューションは音源マッピングの標準ですが,計算が密集しています.
  • 現在のディープラーニング方法は機能の多様性や PSF 変数処理が欠けていて,局所化の精度が低下しています.

研究 の 目的:

  • 高解像度の音源マッピングのための監督学習フレームワークを開発する.
  • 既存の方法の限界に対処することによって,局所化の精度を向上させる.

主な方法:

  • デュアルエンコーダーのU-ネットアーキテクチャが提案され,遅延と和と機能的なビーム形成マップを処理します.
  • 対照的損失関数は,一貫した潜在的特徴の学習を保証します.
  • 周波数と位置のエンコーダーは,源の特性と空間位置の事前の知識を組み込む.

主要な成果:

  • 提案されたモデルは,シミュレーションとMIRACLEデータセットの4つのメトリックで既存の方法を上回ります.
  • 音源と周波数の異なる数の間で一般化を証明した.
  • 真の源の強度分布のより高い解像度マッピングを達成しました.

結論:

  • デュアルエンコーダーU-NETフレームワークは,音源マッピングの重要な進歩を提供します.
  • この方法はPSFの変動を効果的に処理し,計算効率を改善します.
  • このアプローチは,正確な音源の局所化のための堅固な解決策を提供します.