We have developed an algorithm for accurately predicting the intelligibility of speech in noise in a reverberant environment. The algorithm is based on a development of the equalization-cancellation theory of binaural unmasking, combined with established prediction methods for monaural speech perception in noise. It has been validated against a wide range of empirical data. Acoustic measurements of rooms, known as binaural room impulse responses (BRIRs) are analysed to predict intelligibility of a nearby voice masked by any number of steadystate noise maskers in any spatial configuration within the room. This computationally efficient method can be used to generate intelligibility maps of rooms based on the design of the room.
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