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: 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: 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 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: 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: 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: 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: Methods and apparatus for detecting a repetitive pattern in a sequence of audio frames are described. Similarity values of a first similarity matrix with first resolution for the sequence are calculated. An adaptive threshold is estimated from the similarity values for classifying the similarity values into repetition or non-repetition. For each of one or more offsets of a second similarity matrix with second resolution higher that the first resolution, similarity values of the second similarity matrix corresponding to the offset are calculated. Then the calculated similarity values are binarized with the adaptive threshold to obtain binarized data. Finally, the repetitive pattern is detected from the binarized data. The requirement on memory may be reduced because less data are stored in detecting the repetitive pattern.

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: 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: Described is a technology by which a playback list comprising similar songs is automatically built based on automatically detected/generated song attributes, such as by extracting numeric features of each song. The attributes may be downloaded from a remote connection, and/or may be locally generated on the playback device. To build a playlist, a seed song's attributes may be compared against attributes of other songs to determine which other songs are similar to the seed song and thus included in the playlist. Another way to build a playlist is based on similarity of songs to a set of user provided-attributes, such as corresponding to moods or usage modes such as “resting” “reading” “jogging” or “driving” moods/modes. The playlist may be dynamically adjusted based on user interaction with the device, such as when a user skips a song, queues a song, or dequeues a song.

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 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.

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: Described is a technology by which a playback list comprising similar songs is automatically built based on automatically detected/generated song attributes, such as by extracting numeric features of each song. The attributes may be downloaded from a remote connection, and/or may be locally generated on the playback device. To build a playlist, a seed song's attributes may be compared against attributes of other songs to determine which other songs are similar to the seed song and thus included in the playlist. Another way to build a playlist is based on similarity of songs to a set of user provided-attributes, such as corresponding to moods or usage modes such as “resting” “reading” “jogging” or “driving” moods/modes. The playlist may be dynamically adjusted based on user interaction with the device, such as when a user skips a song, queues a song, or dequeues a song.

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: 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 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: 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: 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.