Abstract:
The disclosure relates to an apparatus for determining a quality score (MOS) for an audio signal sample, the apparatus comprising: an extractor configured to extract a feature vector from the audio signal sample, wherein the feature vector comprises a plurality of feature values and wherein each feature value is associated to a different feature of the feature vector; a pre-processor configured to pre-process a feature value of the feature vector based on a cumulative distribution function associated to the feature represented by the feature value to obtain a pre-processed feature value; and a processor configured to implement a neural network and to determine the quality score (MOS) for the audio signal sample based on the pre-processed feature value and a set of neural network parameters for the neural network associated to the cumulative distribution function.
Abstract:
A sound processing node for an arrangement of sound processing nodes is disclosed. The sound processing nodes being configured to receive a plurality of sound signals, wherein the sound processing node comprises a processor configured to determine a beamforming signal on the basis of the plurality of sound signals weighted by a plurality of weights, wherein the processor is configured to determine the plurality of weights using a transformed version of a linearly constrained minimum variance approach, the transformed version of the linearly constrained minimum variance approach being obtained by applying a convex relaxation to the linearly constrained minimum variance approach.
Abstract:
The invention relates to a sound processing node for an arrangement of sound processing nodes, the sound processing nodes being configured to receive a plurality of sound signals, wherein the sound processing node comprises a processor configured to generate an output signal on the basis of the plurality of sound signals weighted by a plurality of beamforming weights, wherein the processor is configured to adaptively determine the plurality of beamforming weights on the basis of an adaptive linearly constrained minimum variance beamformer using a transformed version of a least mean squares formulation of a constrained gradient descent approach, wherein the transformed version of the least mean squares formulation of the constrained gradient descent approach is based on a transformation of the least mean squares formulation of the constrained gradient descent approach to the dual domain.
Abstract:
A method and apparatus for performing an adaptive down-mixing of a multichannel audio signal comprising a number of input channels, wherein a signal adaptive transformation of said input channels is performed by multiplying the input channels with a downmix block matrix comprising a fixed block for providing a set of backward compatible primary channels and a signal adaptive block for providing a set of secondary channels