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: 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: 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.

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 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.

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: A method for audio signal processing is provided. The method includes acquiring a first set of metadata associated with consumption of an audio signal by a target user, acquiring a second set of metadata associated with a set of reference users and generating, at least partially based on the first and second sets of metadata, a recommended configuration of at least one parameter for the target user, the at least one parameter being for use in the consumption of the audio signal. Corresponding apparatus and computer program product 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 for audio classification are described. An audio classification system includes at least one device which executes a process of audio classification on an audio signal. The at least one device can operate in at least two modes requiring different resources. The audio classification system also includes a complexity controller which determines a combination and instructs the at least one device to operate according to the combination. For each of the at least one device, the combination specifies one of the modes of the device, and the resources requirement of the combination does not exceed maximum available resources. By controlling the modes, the audio classification system has improved scalability to an execution environment.

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: 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 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: Methods and apparatuses for performing song detection on an audio signal are described. Clips of the audio signal are classified into classes comprising music. Class boundaries of music clips are detected as candidate boundaries of a first type. Combinations including non-overlapped sections are derived. Each section meets the following conditions: 1) including at least one music segment longer than a predetermined minimum song duration, 2) shorter than a predetermined maximum song duration, 3) both starting and ending with a music clip, and 4) a proportion of the music clips in each of the sections is greater than a predetermined minimum proportion. In this way, various possible song partitions in the audio signal can be obtained for investigation.

Abstract: Automated rich presentation of a semantic topic is described. In one aspect, respective portions of multimodal information corresponding to a semantic topic are evaluated to locate events associated with the semantic topic. The probability that a document belongs to an event is determined based on document inclusion of one or more of persons, times, locations, and keywords, and document distribution along a timeline associated with the event. For each event, one or more documents objectively determined to be substantially representative of the event are identified. One or more other types of media (e.g., video, images, etc.) related to the event are then extracted from the multimodal information. The representative documents and the other media are for presentation to a user in a storyboard.

Abstract: 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.

Abstract: Embodiments for audio classification are described. An audio classification system includes at least one device which executes a process of audio classification on an audio signal. The at least one device can operate in at least two modes requiring different resources. The audio classification system also includes a complexity controller which determines a combination and instructs the at least one device to operate according to the combination. For each of the at least one device, the combination specifies one of the modes of the device, and the resources requirement of the combination does not exceed maximum available resources. By controlling the modes, the audio classification system has improved scalability to an execution environment.

Abstract: Methods and apparatuses for performing song detection on an audio signal are described. Clips of the audio signal are classified into classes comprising music. Class boundaries of music clips are detected as candidate boundaries of a first type. Combinations including non-overlapped sections are derived. Each section meets the following conditions: 1) including at least one music segment longer than a predetermined minimum song duration, 2) shorter than a predetermined maximum song duration, 3) both starting and ending with a music clip, and 4) a proportion of the music clips in each of the sections is greater than a predetermined minimum proportion. In this way, various possible song partitions in the audio signal can be obtained for investigation.

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: 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.