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Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
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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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The arithmetic mean is usually skewed towards the larger values in the data set. Therefore, to avoid this inherent bias towards smaller values, the harmonic mean is used.
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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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Parseval's theorem is a fundamental principle in signal processing that enables the calculation of a signal's energy in either the time domain or the frequency domain. This theorem is pivotal in demonstrating energy conservation between these two domains, ensuring that the computed energy value remains consistent regardless of the domain of analysis.
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Related Experiment Video

Updated: Nov 27, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Bayesian Inference for Acoustic Direction of Arrival Analysis Using Spherical Harmonics.

Ning Xiang1, Christopher Landschoot1

  • 1Graduate Program in Architectural Acoustics, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

This study uses Bayesian inference to accurately determine the number and direction of arrival (DoA) for multiple sound sources. This method enhances acoustic source localization in complex environments.

Keywords:
Bayesian inferencedirection of arrivalmaximum entropymodel selectionparameter estimationspherical harmonics

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Area of Science:

  • Acoustics and Signal Processing
  • Statistical Inference
  • Array Signal Processing

Background:

  • Direction of Arrival (DoA) estimation is crucial for understanding acoustic environments.
  • Spherical microphone arrays and spherical harmonics beamforming offer advanced spatial audio capture.
  • Estimating multiple simultaneous sound sources presents significant challenges in signal processing.

Purpose of the Study:

  • To develop a robust Bayesian framework for estimating the number and DoA of simultaneous sound sources.
  • To leverage spherical harmonics beamforming for enhanced acoustic sensing.
  • To validate the proposed method through experimental testing with multiple sound sources.

Main Methods:

  • Application of two levels of Bayesian inference: Bayesian model selection for source counting and Bayesian parameter estimation for DoA.
  • Formulation of signal models using spherical harmonic beamforming incorporating prior information.
  • Incorporation of model-signal discrepancies and prior DoA information using the maximum entropy principle.

Main Results:

  • Successful estimation of the correct number of simultaneous sound sources (two and three tested) without prior knowledge.
  • Accurate estimation of the Direction of Arrival (DoA) for each individual sound source.
  • Demonstration of unambiguous source number determination and precise DoA estimation.

Conclusions:

  • The proposed model-based Bayesian inference effectively handles complex acoustic environments with multiple simultaneous sources.
  • The two-level Bayesian inference framework provides a powerful tool for accurate acoustic source localization.
  • This approach offers significant potential for advancing sound source analysis and understanding in challenging scenarios.