Frequency-domain parameter estimations for binary masked signals

We present an approach for the extraction of parameters of a damped complex exponential model from a spectrogram modified by a binary mask. The parameters are estimated by a frequency domain based methods using subspace techniques, where the core algorithm is F-ESPRIT. The sub-band defined by the binary mask provides a reduced number of DFT-samples for the parameter extractions, which results in a computational efficient scheme with high parameter estimation accuracy. The proposed synthesis system has synthesis performance comparable to the so-called LSEE-MSTFT. The estimated parameters can be used in many applications such as audio/speech coding, pitch estimation and pitch scale modification.