Joint Optimization of the Frequency-Domain and Time-Domain Transformations in Deriving Generalized Static and Dynamic MFCCs

Traditionally, static mel-frequency cepstral coefficients (MFCCs) are derived by discrete cosine transformation (DCT), and dynamic MFCCs are derived by linear regression. Their derivation may be generalized as a frequency-domain transformation of the log filter-bank energies (FBEs) followed by a time-domain transformation. In the past, these two transformations are usually estimated or optimized separately. In this letter, we consider sequences of log FBEs as a set of spectrogram images and investigate an image compression technique to jointly optimize the two transformations so that the reconstruction error of the spectrogram images is minimized; there is an efficient algorithm that solves the optimization problem. The framework allows extension to other optimization costs as well