A method for room calibration, comprising: measuring a plurality of impulse responses at a plurality of measurement points in a room for each speaker of a plurality of speakers, determining a plurality of transfer functions at the plurality of measurement points for each speaker based on the plurality of impulse responses; and weighting and summing the transfer functions to obtain a weighted and summed sound curve for each speaker, wherein the weighting and summing further comprises: obtaining magnitude components and phase components of the transfer functions for each speaker, constructing Gaussian distributions with the magnitude components and the phase components for each speaker, generating weights for the distributions of the magnitude components and the phase components for each speaker based on each cluster distance, wherein each cluster distance is a distance of the measurement of each measurement point to a center of a cluster that is formed from distributions of the magnitude components and the phase components for a speaker, wherein the weights are obtained by performing a k-nearest neighbors algorithm for each distribution, and weighting and summing the magnitude components and the phase components for each speaker based on the weights, to obtain the weighted and summed sound curve for each speaker. measuring a plurality of impulse responses at a plurality of measurement points in a room for each speaker of a plurality of speakers, determining a plurality of transfer functions at the plurality of measurement points for each speaker based on the plurality of impulse responses; and weighting and summing the transfer functions to obtain a weighted and summed sound curve for each speaker, wherein the weighting and summing further comprises: obtaining magnitude components and phase components of the transfer functions for each speaker, constructing Gaussian distributions with the magnitude components and the phase components for each speaker, generating weights for the distributions of the magnitude components and the phase components for each speaker based on each cluster distance, wherein each cluster distance is a distance of the measurement of each measurement point to a center of a cluster that is formed from distributions of the magnitude components and the phase components for a speaker, wherein the weights are obtained by performing a k-nearest neighbors algorithm for each distribution, and weighting and summing the magnitude components and the phase components for each speaker based on the weights, to obtain the weighted and summed sound curve for each speaker. obtaining magnitude components and phase components of the transfer functions for each speaker, constructing Gaussian distributions with the magnitude components and the phase components for each speaker, generating weights for the distributions of the magnitude components and the phase components for each speaker based on each cluster distance, wherein each cluster distance is a distance of the measurement of each measurement point to a center of a cluster that is formed from distributions of the magnitude components and the phase components for a speaker, wherein the weights are obtained by performing a k-nearest neighbors algorithm for each distribution, and weighting and summing the magnitude components and the phase components for each speaker based on the weights, to obtain the weighted and summed sound curve for each speaker.