An audio encoder for encoding an audio signal includes: a first encoding processor for encoding a first audio signal portion in a frequency domain, wherein the first encoding processor includes: a time frequency converter for converting the first audio signal portion into a frequency domain representation having spectral lines up to a maximum frequency of the first audio signal portion; a spectral encoder for encoding the frequency domain representation; a second encoding processor for encoding a second different audio signal portion in the time domain; a cross-processor for calculating, from the encoded spectral representation of the first audio signal portion, initialization data of the second encoding processor, so that the second encoding processing is initialized to encode the second audio signal portion immediately following the first audio signal portion in time in the audio signal; a controller configured for analyzing the audio signal and for determining, which portion of the audio signal is the first audio signal portion encoded in the frequency domain and which portion of the audio signal is the second audio signal portion encoded in the time domain; and an encoded signal former for forming an encoded audio signal including a first encoded signal portion for the first audio signal portion and a second encoded signal portion for the second audio signal portion.

A method, computer program product, and computing system for generating a plurality of acoustic relative transfer functions for a plurality of audio acquisition devices of an audio recording system deployed in an acoustic environment. The plurality of acoustic relative transfer functions may be encoded into a first embedding of acoustic relative transfer functions and at least a second embedding of acoustic relative transfer functions. Information may be extracted from at least the first embedding of acoustic relative transfer functions.

A method for partitioning of input vectors for coding is presented. The method comprises obtaining of an input vector. The input vector is segmented, in a non-recursive manner, into an integer number, N.sup.SEG, of input vector segments. A representation of a respective relative energy difference between parts of the input vector on each side of each boundary between the input vector segments is determined, in a recursive manner. The input vector segments and the representations of the relative energy differences are provided for individual coding. Partitioning units and computer programs for partitioning of input vectors for coding, as well as positional encoders, are presented.

A method and an apparatus for calculating a downmixed signal and a residual signal are provided. According to the method, if a first target frame (a current frame or a previous frame of the current frame) is a switching frame, a to-be-encoded downmixed signal and a to-be-encoded residual signal of the subband corresponding to the preset frequency band in the current frame is calculated based on a switch fade-in/fade-out factor of a second target frame, an initial downmixed signal and an initial residual signal of the preset frequency band.

In general, disclosed is a device that includes one or more processors, coupled to the memory, configured to perform an energy analysis with respect to one or more audio objects, in the ambisonics domain, in the first time segment. The one or more processors are also configured to perform a similarity measure between the one or more audio objects, in the ambisonics domain, in the first time segment, and the one or more audio objects, in the ambisonics domain, in the second time segment. In addition, the one or more processors are configured to perform a reorder of the one or more audio objects, in the ambisonics domain, in the first time segment with the one or more audio objects, in the ambisonics domain, in the second time segment, to generate one or more reordered audio objects in the first time segment.

A method for partitioning of input vectors for coding is presented. The method comprises obtaining of an input vector. The input vector is segmented, in a non-recursive manner, into an integer number, N.sup.SEG, of input vector segments. A representation of a respective relative energy difference between parts of the input vector on each side of each boundary between the input vector segments is determined, in a recursive manner. The input vector segments and the representations of the relative energy differences are provided for individual coding. Partitioning units and computer programs for partitioning of input vectors for coding, as well as positional encoders, are presented.

An audio encoder for encoding an audio signal includes: a first encoding processor for encoding a first audio signal portion in a frequency domain, wherein the first encoding processor includes: a time frequency converter for converting the first audio signal portion into a frequency domain representation having spectral lines up to a maximum frequency of the first audio signal portion; a spectral encoder for encoding the frequency domain representation; a second encoding processor for encoding a second different audio signal portion in the time domain; a cross-processor for calculating, from the encoded spectral representation of the first audio signal portion, initialization data of the second encoding processor, so that the second encoding processing is initialized to encode the second audio signal portion immediately following the first audio signal portion in time in the audio signal; a controller configured for analyzing the audio signal and for determining, which portion of the audio signal is the first audio signal portion encoded in the frequency domain and which portion of the audio signal is the second audio signal portion encoded in the time domain; and an encoded signal former for forming an encoded audio signal including a first encoded signal portion for the first audio signal portion and a second encoded signal portion for the second audio signal portion.

A method, computer program product, and computing system for encoding audio encounter information of a reference audio acquisition device of a plurality of audio acquisition devices of an audio recording system, thus defining encoded reference audio encounter information. Location information may be estimated, via a machine vision system, for an acoustic source within an acoustic environment. One or more acoustic relative transfer functions may be selected from a plurality of acoustic relative transfer functions for the plurality of audio acquisition devices of the audio recording system based upon, at least in part, the location information. The encoded reference audio encounter information and a representation of the selected one or more acoustic relative transfer function may be transmitted.

There is disclosed inter alia an apparatus for spatial audio signal encoding comprising means for receiving for each time frequency block of a sub band of an audio frame a spatial audio parameter comprising an azimuth and an elevation; determining a first distortion measure for the audio frame by determining a first distance measure for each time frequency block and summing the first distance measure for each time frequency block; determining a second distortion measure for the audio frame by determining a second distance measure for each time frequency block and summing the second distance measure for each time frequency block, and selecting either the first quantization scheme or the second quantization scheme for quantising the elevation and the azimuth for all time frequency blocks of the sub band of the audio frame, wherein the selecting is dependent on the first and second distortion measures.

At an artificial intelligence system, training iterations of a first machine learning model are implemented. In a particular iteration, a group of data items are selected from an item collection using active learning, and respective labels selected from a set of tags are obtained for at least some of the items of the group. Using feature processing elements of a different machine learning model, a respective feature set corresponding to individual labeled items is generated in the iteration, and the feature sets are included in a training set used to train the first machine learning model. A trained version of the first machine learning model is stored after a training completion criterion is met.