Dolby Laboratories Licensing Corporation

Mingqing Hu

Lie Lu

Ming Wang

Claus Bauer

Alan Jeffrey Seefeldt

Regunathan Radhakrishnan

Rhonda J Wilson

Lianwu Chen

Poppy Crum

Dirk Jeroen Breebaart

Nicolas R Tsingos

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. Corresponding system and computer program product of separating audio sources in audio content are also disclosed.

Audio source separation with source direction determination based on iterative weighting

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.

Perception based multimedia processing

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.

Generating metadata for audio object

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.

Upmixing of audio signals

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.

Projection-based audio object extraction from audio content

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.

Equalizer controller and controlling method

Embodiments of the present invention relate to audio object extraction. A method for audio object extraction from audio content of a format based on a plurality of channels is disclosed. The method comprises applying audio object extraction on individual frames of the audio content at least partially based on frequency spectral similarities among the plurality of channels. The method further comprises performing audio object composition across the frames of the audio content, based on the audio object extraction on the individual frames, to generate a track of at least one audio object. Corresponding system and computer program product are also disclosed.

Audio object extraction

Embodiments of the present invention relate to adaptive audio content generation. Specifically, a method for generating adaptive audio content is provided. The method comprises extracting at least one audio object from channel-based source audio content, and generating the adaptive audio content at least partially based on the at least one audio object. Corresponding system and computer program product are also disclosed.

Adaptive audio content generation

Embodiments for measuring content coherence and embodiments for measuring content similarity are described. Content coherence between a first audio section and a second audio section is measured. For each audio segment in the first audio section, a predetermined number of audio segments in the second audio section are determined. Content similarity between the audio segment in the first audio section and the determined audio segments is higher than that between the audio segment and all the other audio segments in the second audio section. An average of the content similarity between the audio segment in the first audio section and the determined audio segments is calculated. The content coherence is calculated as an average, the maximum or the minimum of the averages calculated for the audio segments in the first audio section. The content similarity may be calculated based on Dirichlet distribution.

Measuring content coherence and measuring similarity

Embodiments of identifying multimedia objects based on multimedia fingerprints are provided. Query fingerprints are derived from a multimedia object according to differing fingerprint algorithms. For each fingerprint algorithm, decisions are calculated through at least one classifier corresponding to the fingerprint algorithm based on the query fingerprint and reference fingerprints, the reference fingerprints being derived from reference multimedia objects according to the same fingerprint algorithm. Each of the decisions indicates a possibility that the query fingerprint and the reference fingerprint are not derived from the same multimedia content. For each of the reference multimedia objects, a distance is calculated as a weighted sum of the decisions relating to the reference fingerprints. The multimedia object is identified as matching the reference multimedia object with the smallest distance less than a threshold.

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Beijing, China

Mingqing Hu