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: 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.

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: 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.

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: Audio processing method and audio processing apparatus, and training method are described. According to embodiments of the application, an accent identifier is used to identify accent frames from a plurality of audio frames, resulting in an accent sequence comprised of probability scores of accent and/or non-accent decisions with respect to the plurality of audio frames. Then a tempo estimator is used to estimate a tempo sequence of the plurality of audio frames based on the accent sequence. The embodiments can be well adaptive to the change of tempo, and can be further used to tracking beats properly.

Abstract: Embodiments of the present invention relate to audio object clustering by utilizing temporal variation of audio objects. There is provided a method of estimating temporal variation of an audio object for use in audio object clustering. The method comprises obtaining at least one segment of an audio track associated with the audio object, the at least one segment containing the audio object; estimating variation of the audio object over a time duration of the at least one segment based on at least one property of the audio object and adjusting, at least partially based on the estimated variation of the audio object, a contribution of the audio object to the determination of a centroid in the audio object clustering. Corresponding system and computer program product are disclosed.

Abstract: Example embodiments disclosed herein relate to audio object clustering. A method for metadata-preserved audio object clustering is disclosed. The method comprises classifying a plurality of audio objects into a number of categories based on information to be preserved in metadata associated with the plurality of audio objects. The method further comprises assigning a predetermined number of clusters to the categories and allocating an audio object in each of the categories to at least one of the clusters according to the assigning. 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: 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: Described is a technology by which a user hums, sings or otherwise plays a user-provided rendition of a ringtone (or ringback tone) through a mobile telephone to a ringtone search service (e.g., a WAP, interactive voice response or SMS-based search platform). The service matches features of the user's rendition against features of actual ringtones to determine one or more matching candidate ringtones for downloading. Features may include pitch contours (up or down), pitch intervals and durations of notes. Matching candidates may be ranked based on the determined similarity, possibly in conjunction with weighting criterion such as the popularity of the ringtone and/or the importance of the matched part. The candidate set may be augmented with other ringtones independent of the matching, such as the most popular ones downloaded by other users, ringtones from similar artists, and so forth.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments are directed to a method for processing an input audio signal, comprising: splitting the input audio signal into at least two components, in which the first component is characterized by fast fluctuations in the input signal envelope, and a second component that is relatively stationary over time; processing the second, stationary component by a decorrelation circuit; and constructing an output signal by combining the output of the decorrelator circuit with the input signal and/or the first component signal.

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: 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: 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: 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 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.