Recorded room impulse responses enable accurate and high-quality artificial reverberation. Used in combination with convolution, they can be computationally expensive and inflexible, providing little control to the user. On the other hand, reverberation algorithms are parametric which enable user control. However, they can lack realism and can be challenging to configure. To address these limitations, we introduce a multi-stage approach to optimize the coefficients of a Feedback Delay Network (FDN) reverberator to match a target room impulse response, thus enabling parametric control. In the first stage, we configure some FDN parameters by extracting features from the target impulse response. Then, we use a genetic algorithm to fit the remaining parameters to match the desired impulse response using a Mel-frequency cepstrum coefficients (MFCCs) cost function. We evaluate our approach across a dataset of impulse responses and conducted a subjective listening test. Our results indicate that the combination of the FDN with a short truncation of the target impulse response enables a better approximation, however, there are still differences with respect to the overall spectrum and the clarity factor in some more challenging cases.
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