Abstract: Example embodiments disclosed herein relate to signal processing. A method for decomposing a plurality of audio signals from at least two different channels is disclosed. The method comprises obtaining a set of components that are weakly correlated, the set of components generated based on the plurality of audio signals. The method comprises extracting a feature from the set of components, and determining a set of gains associated with the set of components at least in part based on the extracted feature, each of the gains indicating a proportion of a diffuse part in the associated component. The method further comprises decomposing the plurality of audio signals by applying the set of gains to the set of components. Corresponding system and computer program product are also disclosed.

Abstract: Example embodiments disclosed herein relate to perception based multimedia processing. There is provided a method for processing multimedia data, the method includes automatically determining user perception on a segment of the multimedia data based on a plurality of clusters, the plurality of clusters obtained in association with predefined user perceptions and processing the segment of the multimedia data at least in part based on determined user perception on the segment. Corresponding system and computer program products are disclosed as well.

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: Volume leveler controller and controlling method are disclosed. In one embodiment, A volume leveler controller includes an audio content classifier for identifying the content type of an audio signal in real time; and an adjusting unit for adjusting a volume leveler in a continuous manner based on the content type as identified. The adjusting unit may configured to positively correlate the dynamic gain of the volume leveler with informative content types of the audio signal, and negatively correlate the dynamic gain of the volume leveler with interfering content types of the audio signal.

Abstract: Example embodiments disclosed herein relate to signal processing. A method for decomposing a plurality of audio signals from at least two different channels is disclosed. The method comprises obtaining a set of components that are weakly correlated, the set of components generated based on the plurality of audio signals. The method comprises extracting a feature from the set of components, and determining a set of gains associated with the set of components at least in part based on the extracted feature, each of the gains indicating a proportion of a diffuse part in the associated component. The method further comprises decomposing the plurality of audio signals by applying the set of gains to the set of components. Corresponding system and computer program product are also disclosed.

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.

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 relate to signal processing. A method for decomposing a plurality of audio signals from at least two different channels is disclosed. The method comprises obtaining a set of components that are weakly correlated, the set of components generated based on the plurality of audio signals. The method comprises extracting a feature from the set of components, and determining a set of gains associated with the set of components at least in part based on the extracted feature, each of the gains indicating a proportion of a diffuse part in the associated component. The method further comprises decomposing the plurality of audio signals by applying the set of gains to the set of components. Corresponding system and computer program product are also disclosed.

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

Abstract: Audio objects that are present in input audio content in one or more frames are determined. Output clusters that are present in output audio content in the one or more frames are also determined. Here, the audio objects in the input audio content are converted to the output clusters in the output audio content. One or more spatial error metrics are computed based at least in part on positional metadata of the audio objects and positional metadata of the output clusters.

Abstract: Volume leveler controller and controlling method are disclosed. In one embodiment, A volume leveler controller includes an audio content classifier for identifying the content type of an audio signal in real time; and an adjusting unit for adjusting a volume leveler in a continuous manner based on the content type as identified. The adjusting unit may configured to positively correlate the dynamic gain of the volume leveler with informative content types of the audio signal, and negatively correlate the dynamic gain of the volume leveler with interfering content types of the audio signal.

Abstract: Example embodiments disclosed herein relate to perception based multimedia processing. There is provided a method for processing multimedia data, the method includes automatically determining user perception on a segment of the multimedia data based on a plurality of clusters, the plurality of clusters obtained in association with predefined user perceptions and processing the segment of the multimedia data at least in part based on determined user perception on the segment. Corresponding system and computer program products are disclosed as well.

Abstract: Example embodiments disclosed herein relate to signal processing. A method for decomposing a plurality of audio signals from at least two different channels is disclosed. The method obtains a set of components that are weakly correlated wherein the set of components generated based on the plurality of audio signals. The method extract a feature from the set of components and determining determines a set of gains associated with the set of components at least in part based on the extracted feature. Each of the gains indicate a proportion of a diffuse part in the associated component and decompose the plurality of audio signals by applying the set of gains to the set of components. Corresponding system and computer program product 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: Volume leveler controller and controlling method are disclosed. In one embodiment, A volume leveler controller includes an audio content classifier for identifying the content type of an audio signal in real time; and an adjusting unit for adjusting a volume leveler in a continuous manner based on the content type as identified. The adjusting unit may configured to positively correlate the dynamic gain of the volume leveler with informative content types of the audio signal, and negatively correlate the dynamic gain of the volume leveler with interfering content types of the audio signal.

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 object processing. A method for processing audio content, which includes at least one audio object of a multi-channel format, is disclosed. The method includes generating metadata associated with the audio object, the metadata including at least one of an estimated trajectory of the audio object and an estimated perceptual size of the audio object, the perceptual size being a perceived area of a phantom of the audio object produced by at least two transducers. 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 perception based multimedia processing. There is provided a method for processing multimedia data, the method includes automatically determining user perception on a segment of the multimedia data based on a plurality of clusters, the plurality of clusters obtained in association with predefined user perceptions and processing the segment of the multimedia data at least in part based on determined user perception on the segment. Corresponding system and computer program products are disclosed as well.