Fractional linear prediction (FLP), as a generalization of conventional linear prediction (LP),\nwas recently successfully applied in different fields of research and engineering, such as biomedical\nsignal processing, speech modeling and image processing. The FLP model has a similar design as\nthe conventional LP model, i.e., it uses a linear combination of â??fractional termsâ? with different\norders of fractional derivative. Assuming only one â??fractional termâ? and using limited number of\nprevious samples for prediction, FLP model with â??restricted memoryâ? is presented in this paper\nand the closed-form expressions for calculation of FLP coefficients are derived. This FLP model\nis fully comparable with the widely used low-order LP, as it uses the same number of previous\nsamples, but less predictor coefficients, making it more efficient. Two different datasets, MIDI\nAligned Piano Sounds (MAPS) and Orchset, were used for the experiments. Triads representing\nthe chords composed of three randomly chosen notes and usual Western musical chords (both of\nthem from MAPS dataset) served as the test signals, while the piano recordings from MAPS dataset\nand orchestra recordings from the Orchset dataset served as the musical signal. The results show\nenhancement of FLP over LP in terms of model complexity, whereas the performance is comparable.
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