The exemplary embodiments provide a parallel implementation of filters with recursive transfer functions. This can enable a filter to act as a frame filter that may process a frame of multiple samples of data in parallel rather than being limited to processing a single sample of data at a time. Each frame contains plural input samples of data values. The input samples are from a common source and have a time dependency. The exemplary embodiments are suitable for implementing various types of filters in parallel, such as cascaded integrator comb filters, biquad filters and other types of infinite impulse response (IIR) filters. The exemplary embodiments may use polyphase decomposition to decompose a filter with a recursive transfer function into multiple polyphase component filters. The polyphase component filters may be applied to respective samples of data in a parallel pipelined configuration to produce filtered output for the samples of data in parallel.

Prism signal processing is a new FIR filtering technique that can offer a fully recursive calculation and elegant filter design. Its low design and computational cost may be particularly suited to the autonomous signal processing requirements for the Internet of Things. Arbitrarily narrow band-pass filters may be designed and implemented using a chain of Prisms and a simple yet powerful procedure. Using the described method and system, an ultra-narrowband filter can be evaluated in fractions of a microsecond per sample on a desktop computer. To achieve this update rate using a conventional non-recursive FIR calculation would require supercomputer resources. FPGA embodiments of the system demonstrate computation efficiency and broad applications of the technique.

Prism signal processing is a new FIR filtering technique that can offer a fully recursive calculation and elegant filter design. Its low design and computational cost may be particularly suited to the autonomous signal processing requirements for the Internet of Things. Arbitrarily narrow band-pass filters may be designed and implemented using a chain of Prisms and a simple yet powerful procedure. Using the described method and system, an ultra-narrowband filter can be evaluated in fractions of a microsecond per sample on a desktop computer. To achieve this update rate using a conventional non-recursive FIR calculation would require supercomputer resources. FPGA embodiments of the system demonstrate computation efficiency and broad applications of the technique.

The present document relates to the design of anti-aliasing and/or anti-imaging filters for resamplers using rational resampling factors. In particular, the present document relates to a method for implementing such anti-aliasing and/or anti-imaging filters with reduced computational complexity. In addition, the present document relates to further aspects of an audio encoding and decoding system, such as the phase relation between the channels of a multi-channel audio signal and/or the structure of the bitstream of an encoded audio signal.

Methods and apparatuses are provided for channel equalization in a communication system. The method includes initializing, using processing circuitry, filter coefficients of an adaptive decision feedback equalizer randomly in a predetermined search space. Further, the method includes updating, using the processing circuitry, the filter coefficients. The filter coefficients are updated using a least mean square recursion when the filter coefficients are stagnant. The filter coefficients are updated using a particle swarm optimization procedure when the filter coefficients are not stagnant. Further, the updating step is repeated until a predetermined stopping criteria is met. Further, the method includes, filtering, using the processing circuitry, a received signal using the filter coefficients.

Methods and apparatuses are provided for channel equalization in a communication system. The method includes initializing, using processing circuitry, filter coefficients of an adaptive decision feedback equalizer randomly in a predetermined search space. Further, the method includes updating, using the processing circuitry, the filter coefficients. The filter coefficients are updated using a least mean square recursion when the filter coefficients are stagnant. The filter coefficients are updated using a particle swarm optimization procedure when the filter coefficients are not stagnant. Further, the updating step is repeated until a predetermined stopping criteria is met. Further, the method includes, filtering, using the processing circuitry, a received signal using the filter coefficients.