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:
Embodiments are directed a method of rendering object-based audio comprising determining an initial spatial position of objects having object audio data and associated metadata, determining a perceptual importance of the objects, and grouping the audio objects into a number of clusters based on the determined perceptual importance of the objects, such that a spatial error caused by moving an object from an initial spatial position to a second spatial position in a cluster is minimized for objects with a relatively high perceptual importance. The perceptual importance is based at least in part by a partial loudness of an object and content semantics of the object.
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 object clustering. A method for metadata-preserved audio object clustering is disclosed. The method comprises classifying an audio object into at least a category based rendering mode information metadata. The method further comprises assigning a predetermined number of clusters to the categories and rendering the audio object based on the rendering mode. Corresponding system and computer program product are also disclosed.
Abstract:
The present application relates to a method of extracting audio features in a dialog detector in response to an input audio signal, the method comprising dividing the input audio signal into a plurality of frames, extracting frame audio features from each frame, determining a set of context windows, each context window including a number of frames surrounding a current frame, deriving, for each context window, a relevant context audio feature for the current frame based on the frame audio features of the frames in each respective context, and concatenating each context audio feature to form a combined feature vector to represent the current frame. The context windows with the different length can improve the response speed and improve robustness.
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:
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:
Apparatus and methods for audio classifying and processing are disclosed. In one embodiment, an audio processing apparatus includes an audio classifier for classifying an audio signal into at least one audio type in real time; an audio improving device for improving experience of audience; and an adjusting unit for adjusting at least one parameter of the audio improving device in a continuous manner based on the confidence value of the at least one audio type.