Abstract: Example embodiments disclosed herein relate to audio object clustering based on renderer-aware perceptual difference. A method of processing audio objects is provided. The method includes obtaining renderer-related information indicating a configuration of a renderer. The method also includes determining, based on the obtained renderer-related information, a rendering difference between a first audio object and a second audio object among the audio objects with respect to the renderer. The method further includes clustering the audio objects at least in part based on the rendering difference. Corresponding system, device, and computer program product are also disclosed.

Abstract: Example embodiments disclosed herein relate to content-adaptive surround sound virtualization. A method of virtualizing surround sound is disclosed. The method includes receiving a set of input audio signals, each of the input audio signals being indicative of sound from one of different sound sources, and determining a probability of the set of input audio signals belonging to a predefined audio content category. The method also includes determining a virtualization amount based on the determined probability, the virtualization amount indicating to which extent the set of input audio signals is virtualized as surround sound. The method further includes performing surround sound virtualization on two or more input audio signals in the set based on the determined virtualization amount and generating output audio signals based on the virtualized input audio signals and other input audio signals in the set. Corresponding system and computer program product for virtualizing surround sound are also disclosed.

Abstract: Example embodiments disclosed herein relate to audio object clustering with single channel quality preservation. A method of clustering audio objects is disclosed. The method includes determining cluster positions based on object positions of the audio objects and a reference speaker layout, the reference speaker layout indicating speakers located at different speaker positions. The method also includes determining object-to-cluster gains based on the determined cluster positions, the object positions and the reference speaker layout, an object-to-cluster gain defining a proportion of the respective audio object that is assigned to a cluster associated with one of the determined cluster positions. The method further includes clustering the audio objects based on the object-to-cluster gains and the cluster positions for generating cluster signals. Corresponding system, computer program product and device for clustering audio objects are also disclosed.

Abstract: Example embodiments disclosed herein relate to audio signal processing. The audio signal has multiple audio objects. A method of processing an audio signal is disclosed. The method includes obtaining an object position for each of the audio objects; and determining cluster positions for grouping the audio objects into clusters based on the object positions, a plurality of object-to-cluster gains, and a set of metrics. The metrics indicate a quality of the cluster positions and a quality of the object-to-cluster gains, each of the cluster positions is a centroid of a respective one of the clusters, and one of the object-to-cluster gains defines a ratio of the respective audio object in one of the clusters. The method also includes determining the object-to-cluster gains based on the object positions, the cluster positions and the set of metrics; and generating a cluster signal based on the determined cluster positions and object-to-cluster gains.

Abstract: A method is disclosed for audio object extraction from an audio content which includes identifying a first set of projection spaces including a first subset for a first channel and a second subset for a second channel of the plurality of channels. The method may further include determining a first set of correlations between the first and second channels, each of the first set of correlations corresponding to one of the first subset of projection spaces and one of the second subset of projection spaces. Still further, the method may include extracting an audio object from an audio signal of the first channel at least in part based on a first correlation among the first set of correlations and the projection space from the first subset corresponding to the first correlation, the first correlation being greater than a first predefined threshold. Corresponding system and computer program products are also disclosed.

Abstract: The present document describes a method (100) for extracting audio sources (301) from audio channels (302). The method (100) includes updating (102) a Wiener filter matrix based on a mixing matrix from a source matrix and based on a power matrix of the audio sources (301). Furthermore, the method (100) includes updating (103) a cross-covariance matrix of the audio channels (302) and of the audio sources (301) and an auto-covariance matrix of the audio sources (301), based on the updated Wiener filter matrix and based on an auto-covariance matrix of the audio channels (302). In addition, the method (100) includes updating (104) the mixing matrix and the power matrix based on the updated cross-covariance matrix of the audio channels (302) and of the audio sources (301), and/or based on the updated auto-covariance matrix of the audio sources (301).

Abstract: An audio processing system and method which calculates, based on spatial metadata of the audio object, a panning coefficient for each of the audio objects in relation to each of a plurality of predefined channel coverage zones. Converts the audio signal into submixes in relation to the predefined channel coverage zones based on the calculated panning coefficients and the audio objects. Each of the submixes indicating a sum of components of the plurality of the audio objects in relation to one of the predefined channel coverage zones. Generating a submix gain by applying an audio processing to each of the submix and controls an object gain applied to each of the audio objects. The object gain being as a function of the panning coefficients for each of the audio objects and the submix gains in relation to each of the predefined channel coverage zones.

Abstract: Example embodiments disclosed herein relates to upmixing of audio signals. A method of upmixing an audio signal is described. The method includes decomposing the audio signal into a diffuse signal and a direct signal, generating an audio bed at least in part based on the diffuse signal, the audio bed including a height channel, extracting an audio object from the direct signal, estimating metadata of the audio object, the metadata including height information of the audio object; and rendering the audio bed and the audio object as an upmixed audio signal, wherein the audio bed is rendered to a predefined position and the audio object is rendered according to the metadata. Corresponding system and computer program product are described as well.

Abstract: Embodiments of the present invention relate to video content assisted audio object extraction. A method of audio object extraction from channel-based audio content is disclosed. The method comprises extracting at least one video object from video content associated with the channel-based audio content, and determining information about the at least one video object. The method further comprises extracting from the channel-based audio content an audio object to be rendered as an upmixed audio signal based on the determined information. Corresponding system and computer program product are also disclosed.

Abstract: An audio processing system and method which calculates, based on spatial metadata of the audio object, a panning coefficient for each of the audio objects in relation to each of a plurality of predefined channel coverage zones. Converts the audio signal into submixes in relation to the predefined channel coverage zones based on the calculated panning coefficients and the audio objects. Each of the submixes indicating a sum of components of the plurality of the audio objects in relation to one of the predefined channel coverage zones. Generating a submix gain by applying an audio processing to each of the submix and controls an object gain applied to each of the audio objects. The object gain being as a function of the panning coefficients for each of the audio objects and the submix gains in relation to each of the predefined channel coverage zones.

Abstract: Example embodiments disclosed herein relate to content-adaptive surround sound virtualization. A method of virtualizing surround sound is disclosed. The method includes receiving a set of input audio signals, each of the input audio signals being indicative of sound from one of different sound sources, and determining a probability of the set of input audio signals belonging to a predefined audio content category. The method also includes determining a virtualization amount based on the determined probability, the virtualization amount indicating to which extent the set of input audio signals is virtualized as surround sound. The method further includes performing surround sound virtualization on two or more input audio signals in the set based on the determined virtualization amount and generating output audio signals based on the virtualized input audio signals and other input audio signals in the set. Corresponding system and computer program product for virtualizing surround sound are also disclosed.

Abstract: An audio processing system and method which calculates, based on spatial metadata of the audio object, a panning coefficient for each of the audio objects in relation to each of a plurality of predefined channel coverage zones. Converts the audio signal into submixes in relation to the predefined channel coverage zones based on the calculated panning coefficients and the audio objects. Each of the submixes indicating a sum of components of the plurality of the audio objects in relation to one of the predefined channel coverage zones. Generating a submix gain by applying an audio processing to each of the submix and controls an object gain applied to each of the audio objects. The object gain being as a function of the panning coefficients for each of the audio objects and the submix gains in relation to each of the predefined channel coverage zones.

Abstract: Diffuse or spatially large audio objects may be identified for special processing. A decorrelation process may be performed on audio signals corresponding to the large audio objects to produce decorrelated large audio object audio signals. These decorrelated large audio object audio signals may be associated with object locations, which may be stationary or time-varying locations. For example, the decorrelated large audio object audio signals may be rendered to virtual or actual speaker locations. The output of such a rendering process may be input to a scene simplification process. The decorrelation, associating and/or scene simplification processes may be performed prior to a process of encoding the audio data.

Abstract: Example embodiments disclosed herein relates to upmixing of audio signals. A method of upmixing an audio signal is described. The method includes decomposing the audio signal into a diffuse signal and a direct signal, generating an audio bed at least in part based on the diffuse signal, the audio bed including a height channel, extracting an audio object from the direct signal, estimating metadata of the audio object, the metadata including height information of the audio object; and rendering the audio bed and the audio object as an upmixed audio signal, wherein the audio bed is rendered to a predefined position and the audio object is rendered according to the metadata. Corresponding system and computer program product are described as well.

Abstract: Example embodiments disclosed herein relate to audio signal processing. The audio signal has multiple audio objects. A method of processing an audio signal is disclosed. The method includes obtaining an object position for each of the audio objects; and determining cluster positions for grouping the audio objects into clusters based on the object positions, a plurality of object-to-cluster gains, and a set of metrics. The metrics indicate a quality of the cluster positions and a quality of the object-to-cluster gains, each of the cluster positions is a centroid of a respective one of the clusters, and one of the object-to-cluster gains defines a ratio of the respective audio object in one of the clusters. The method also includes determining the object-to-cluster gains based on the object positions, the cluster positions and the set of metrics; and generating a cluster signal based on the determined cluster positions and object-to-cluster gains.

Abstract: Equalizer controller and controlling method are disclosed. In one embodiment, an equalizer controller includes an audio classifier for identifying the audio type of an audio signal in real time; and an adjusting unit for adjusting an equalizer in a continuous manner based on the confidence value of the audio type as identified.

Abstract: Diffuse or spatially large audio objects may be identified for special processing. A decorrelation process may be performed on audio signals corresponding to the large audio objects to produce decorrelated large audio object audio signals. These decorrelated large audio object audio signals may be associated with object locations, which may be stationary or time-varying locations. For example, the decorrelated large audio object audio signals may be rendered to virtual or actual speaker locations. The output of such a rendering process may be input to a scene simplification process. The decorrelation, associating and/or scene simplification processes may be performed prior to a process of encoding the audio data.

Abstract: An audio processing system and method which calculates, based on spatial metadata of the audio object, a panning coefficient for each of the audio objects in relation to each of a plurality of predefined channel coverage zones. Converts the audio signal into submixes in relation to the predefined channel coverage zones based on the calculated panning coefficients and the audio objects. Each of the submixes indicating a sum of components of the plurality of the audio objects in relation to one of the predefined channel coverage zones. Generating a submix gain by applying an audio processing to each of the submix and controls an object gain applied to each of the audio objects. The object gain being as a function of the panning coefficients for each of the audio objects and the submix gains in relation to each of the predefined channel coverage zones.

Abstract: Example embodiments disclosed herein relate to audio source separation with source direction determined based on iterative weighted component analysis. A method of separating audio sources in audio content is disclosed. The audio content includes a plurality of channels. The method includes obtaining multiple data samples from multiple time-frequency tiles of the audio content. The method also includes analyzing the data samples to generate multiple components in a plurality of iterations, wherein each of the components indicates a direction with a variance of the data samples, and wherein in each of the plurality of iterations, each of the data samples is weighted with a weight that is determined based on a selected component from the multiple components. The method further includes determining a source direction of the audio content based on the selected component for separating an audio source from the audio content.

Abstract: Embodiments of the example embodiment relate to audio object extraction. A method for audio object extraction from audio content is disclosed. The method comprises determining a sub-band object probability for a sub-band of the audio signal in a frame of the audio content, the sub-band object probability indicating a probability of the sub-band of the audio signal containing an audio object. The method further comprises splitting the sub-band of the audio signal into an audio object portion and a residual audio portion based on the determined sub-band object probability. Corresponding system and computer program product are also disclosed.