Micah Shepherd, Xi Chen, Timothy W. Leishman, Scott D.Sommerfeldt Acoustics Research Group

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Experimental Equalization of a One-Dimensional Sound Field Using Energy Density and a Parametric Equalizer. Micah Shepherd, Xi Chen, Timothy W. Leishman, Scott D.Sommerfeldt Acoustics Research Group Department of Physics and Astronomy Brigham Young University - PowerPoint PPT Presentation

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  • Experimental Equalization of a One-Dimensional Sound Field Using Energy Density and a Parametric EqualizerMicah Shepherd, Xi Chen, Timothy W. Leishman, Scott D.Sommerfeldt

    Acoustics Research GroupDepartment of Physics and AstronomyBrigham Young University

    148th Meeting of the Acoustical Society of America18 November 2004

  • Background and Traditional TechniqueBackgroundSound fields in rooms do not have ideal responsesSound field equalization compensates for room effects using filtersTraditional techniquesExcite room using pink noiseMeasure pressure response at one locationCut and boost in nth octave bands using graphic equalizer to produce desired responseBetter control using parametric equalizersVariable frequencyVariable QProblemsSpatial variance of sound fieldMicrophone at nodesLimited frequency and gain adjustmentNeed for a better approach

  • Search for an Improved TechniqueMeasure transfer function between source and receiver in 1-D sound fieldThree casesSingle point mean-squared pressureSpatially averaged mean-squared pressure (potential energy density)Single point total energy densityUse normalized inverses of the responses as equalization filters

  • Experimental Setup

  • Energy DensityPressure gradient method Estimate particle velocity using pressure gradient

    Energy Density is then

    2 microphone transfer function method Developed by Chung and Blaser

    Solve for incident and reflected pressure and reflection coefficients at microphone positions

    Derive particle velocity and energy density from result

  • Difference in ED Estimations

  • Unequalized FieldMean-Squared Pressure FieldEnergy Density Field

  • Ideal Inverse Filters From Measured Field

  • Ideal Equalized Pressure FieldsMean-Squared Pressure EQEnergy Density EQ

  • Comparison of Ideal Energy DensityFilter and Parametric EQ Filter

  • Equalized Pressure FieldsParametric ED EQIdeal ED EQ

  • Spatially Averaged Pressure Responses: Ideal and Parametric

  • ConclusionsEnergy density equalization approximates spatially averaged pressure equalization in a 1-D sound fieldA discrete ED measurement can be used to equalize a 1-D sound field better than a discrete pressure measurementParametric equalizers can be used to approximate ideal ED filters, but with notable errors

  • Future workConduct more general tests in a 1-D field with variable side-branch source positionsTest energy density equalization methods in 3-D sound fieldsTest energy density equalization methods using multiple sourcesDevelop adaptive filtering techniques

  • Thank you

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