Abstract: The disclosure relates to an audio processing apparatus, comprising: a plurality of audio sensors, each audio sensor configured to receive a respective plurality of audio frames of an audio signal from an audio source, wherein the respective plurality of audio frames defines an audio channel of the audio signal; and a processing circuitry configured to: determine a respective feature set having at least one feature for each audio frame of each of the plurality of audio frames, wherein the plurality of features define a three-dimensional feature array; process the three-dimensional feature array using a neural network, wherein the neural network comprises a self-attention layer configured to process a plurality of two-dimensional sub-arrays of the three-dimensional feature array; and generate an output signal on the basis of the plurality of processed two-dimensional sub-arrays. Moreover, the disclosure relates to a corresponding audio processing method.

Abstract: Communication system and related methods, in particular a method of operating a communication system is disclosed. The method comprises obtaining audio data representative of one or more voices, the audio data including first audio data of a first voice; obtaining first voice data based on the first audio data; wherein obtaining first voice data comprises applying a voice model on the first audio data; wherein the first voice data includes first speaker metric data; outputting a first voice representation indicative of the first voice data; obtaining first voice validation data, based on the first voice representation, from a first validator; obtaining second voice validation data, based on the first voice representation, from a second validator; determining an agreement metric based on the first voice validation data and the second voice validation data; determining a first validation score based on the agreement metric; and outputting the first validation score.

Abstract: An apparatus and a method for acoustic scene classification of a block of audio samples are provided. The block is partitioned into frames in the time domain. For each respective frame of a plurality of frames of the block, a change measure between the respective frame and a preceding frame of the block is calculated. The respective frame is assigned, based on the calculated change measure, to one of a set of short-event frames, a set of long-event frames, and a set of background frames. The feature vector is determined based on a feature computed from one or more of the set of short-event frames, the set of long-event frames, and the set of background frames.

Abstract: A speech analyser and related methods are disclosed, the speech analyser comprising an input module for provision of speech data based on a speech signal; a primary feature extractor for provision of primary feature metrics of the speech data; a secondary feature extractor for provision of secondary feature metrics associated with the speech data; and a speech model module comprising a neural network with model layers including an input layer, one or more intermediate layers including a first intermediate layer, and an output layer for provision of a speaker metric, wherein the speech model module is configured to condition an intermediate layer based on the secondary feature metrics for provision of output from the intermediate layer as input to the model layer after the intermediate layer in the neural network.

Abstract: The disclosure relates to an audio processing apparatus, comprising: a plurality of audio sensors, each audio sensor configured to receive a respective plurality of audio frames of an audio signal from an audio source, wherein the respective plurality of audio frames defines an audio channel of the audio signal; and a processing circuitry configured to: determine a respective feature set having at least one feature for each audio frame of each of the plurality of audio frames, wherein the plurality of features define a three-dimensional feature array; process the three-dimensional feature array using a neural network, wherein the neural network comprises a self-attention layer configured to process a plurality of two-dimensional sub-arrays of the three-dimensional feature array; and generate an output signal on the basis of the plurality of processed two-dimensional sub-arrays. Moreover, the disclosure relates to a corresponding audio processing method.

Abstract: A method for reconstructing at least one target signal comprises determining a first set of feature vectors from the input signal, the first set of feature vectors forming a non-negative input matrix; determining a second set of feature vectors, the second set of feature vectors forming a non-negative noise matrix; decomposing the input matrix into a sum of a first matrix and a second matrix, the first matrix representing a product of a non-negative bases matrix and a non-negative weight matrix, and the second matrix representing a combination of the noise matrix and a noise weight vector; and reconstructing the at least one target signal based on the non-negative bases matrix and the non-negative weight matrix.

Abstract: The present invention relates to an apparatus for improving a perception of a sound signal, the apparatus comprising: a separation unit configured to separate the sound signal into at least one speech component and at least one noise component; and a spatial rendering unit configured to generate an auditory impression of the at least one speech component at a first virtual position with respect to a user, when output via a transducer unit, and of the at least one noise component at a second virtual position with respect to the user, when output via the transducer unit.

Abstract: A method for reconstructing at least one target signal comprises determining a first set of feature vectors from the input signal, the first set of feature vectors forming a non-negative input matrix; determining a second set of feature vectors, the second set of feature vectors forming a non-negative noise matrix; decomposing the input matrix into a sum of a first matrix and a second matrix, the first matrix representing a product of a non-negative bases matrix and a non-negative weight matrix, and the second matrix representing a combination of the noise matrix and a noise weight vector; and reconstructing the at least one target signal based on the non-negative bases matrix and the non-negative weight matrix.