DYNAMIC DIGITAL FILTER USING NEURAL NETWORKS

The present invention provides an apparatus for decoding and classifying a digital audio input signal and for reconstructing the digital audio input signal, so that when the reconstructed signal is converted to an analog signal by a digital to analog converter ("DAC"), the analog signal can drive a preamplifier, power amplifier or speakers directly. In particular, the present invention proposes a digital filter than can be adapted to have appropriate filtering characteristics based on the signal being filtered. The invention uses a neural network to adjust coefficients of a digital filter, depending on whether the digital audio input signal is more periodic or more aperiodic. If the digital audio input signal is more periodic, the coefficients will configure the digital filter so that the filter has the characteristics of an analog brickwall filter. Whereas if the digital audio input signal is more aperiodic, the coefficients produced by the neural network will configure the digital filter to have more characteristics of an interpolation filter. The neural network is trained to recognize certain periodic and aperiodic signals and to produce digital filter parameters, preferably polynomial coefficients, correspondingly. The coefficients are selected to respond to the pure or blended periodic and aperiodic features of certain archetypal input signals.