Abstract: Forensic audio upmixer detection is described. Feature sets are extracted from an audio signal that has two or more individual channels. Based on the extracted feature sets, it is determined whether the audio signal was upmixed from audio content that has fewer channels.

Abstract: The present document relates to audio forensics, notably the blind detection of traces of parametric audio encoding/decoding. In particular, the present document relates to the detection of parametric frequency extension audio coding, such as spectral band replication (SBR) or spectral extension (SPX), from uncompressed waveforms such as PCM (pulse code modulation) encoded waveforms. A method for detecting frequency extension coding history in a time domain audio signal is described.

Abstract: Attributes are identified in media content. A classification value of the media content is computed based on the identified attributes. Thereafter, a fingerprint derived from the media content is stored or searched for based on the classification value of the media content.

Abstract: Techniques for re-associating dynamic metadata with media data are provided. A media processing system creates, with a first media processing stage, binding information comprising dynamic metadata and a time relationship between the dynamic metadata and media data. The binding information may be derived from the media data. While the first media processing stage delivers the media data to a second media processing stage in a first data path, the first media processing stage passes the binding information to the second media processing stage in a second data path. The media processing system re-associates, with the second media processing stage, the dynamic metadata and the media data using the binding information.

Abstract: In some embodiments, a method for embedding data (e.g., metadata for use during post-processing) in a stereo audio signal comprising frames. Each of the frames has a saturation value, and data are embedded in the stereo audio signal by modifying the signal to generate a modulated stereo audio signal comprising a sequence of modulated frames having modulated saturation values indicative of the embedded data. Typically, one data bit is embedded in each frame of an input stereo audio signal by modifying the frame to produce a modulated frame whose modulated saturation value matches a target value indicative of the data bit. In other embodiments, a method for extracting data from a stereo audio signal in which the data have been embedded in accordance with an embodiment of the inventive embedding method. Other aspects are systems (e.g., programmed processors) configured to perform any embodiment of the inventive method.

Abstract: In a class of embodiments, an audio encoding system (typically, a perceptual encoding system that is configured to generate a single (“unified”) bitstream that is compatible with (i.e., decodable by) a first decoder configured to decode audio data encoded in accordance with a first encoding protocol (e.g., the multichannel Dolby Digital Plus, or DD+, protocol) and a second decoder configured to decode audio data encoded in accordance with a second encoding protocol (e.g., the stereo AAC, HE AAC v1, or HE AAC v2 protocol). The unified bitstream can include both encoded data (e.g., bursts of data) decodable by the first decoder (and ignored by the second decoder) and encoded data (e.g., other bursts of data) decodable by the second decoder (and ignored by the first decoder).

Abstract: A portion of media content is accessed. Components from a first and each subsequent spatial regions of the media content are sampled. Each spatial region has an unsegmented area. Each subsequent spatial region includes those within its area as elements thereof or the spatial regions may partially overlap. The regions may overlap independent of a hierarchical relationship between the regions. A media fingerprint is derived from the components of each of the spatial regions, which reliably corresponds to the media content portion, e.g., over geometric attacks such as rotation.

Abstract: A media signal is accessed, which has been generated with one or more first processing operations. The media signal includes one or more sets of artifacts, which respectively result from the one or more processing operations. One or more features are extracted from the accessed media signal. The extracted features each respectively correspond to the one or more artifact sets. Based on the extracted features, a conditional probability score and/or a heuristically based score is computed, which relates to the one or more first processing operations.

Abstract: Low complexity detection of a time-wise position of a representative segment in media data is described. A subset of offset values is located in a set of offset values in media data using a first type of one or more types of features, which are extractable from (e.g., derivable from components of) the media data. The subset of offset values comprise values that are selected from the set of offset values based on one or more selection criteria. A set of candidate seed time points is identified based on the subset of offset values using a second type of the one or more types of features.

Abstract: A spread spectrum data hiding for audio signals is described. A set of pseudo-random noise sequences is added to an audio signal according to a data to be embedded. A masking curve is used to shape the added noise. A transient detection step can be used to control whether a shaped noise sequence is to be added or not. Embedded information is detected by first performing a whitening step and then performing a phase-only correlation with a same set of pseudo-random noise sequences. A detection method that is based on correlation of multiplexed noise sequences with a noise sequence embedded in the audio is also described.

Abstract: An audio data spread spectrum embedding and detection method is presented. For each audio frame, a noise sequence is chosen according to the data to be embedded. Then, a spectrum of a chosen noise sequence is shaped by a spectrum of a current audio frame and subtracted from a current frame's spectrum. During detection, a detector is used on a watermarked audio frame to first whiten the watermarked audio frame. Detection scores are then computed against two competing Adaboost learning models. A detected bit is chosen according to the model with a maximum detection score.

Abstract: Robust media fingerprints are derived from a portion of audio content. A portion of content in an audio signal is categorized. The audio content is characterized based, at least in part, on one or more of its features. The features may include a component that relates to one of several sound categories, e.g., speech and/or noise, which may be mixed with the audio signal. Upon categorizing the audio content as free of the speech or noise related components, the audio signal component is processed. Upon categorizing the audio content as including the speech related component and/or the noise related components, the speech or noise related components are separated from the audio signal. The audio signal is processed independent of the speech related component and/or the noise related component. Processing the audio signal includes computing the audio fingerprint, which reliably corresponds to the audio signal.

Abstract: The present document relates to audio forensics, notably the blind detection of traces of parametric audio encoding/decoding. In particular, the present document relates to the detection of parametric frequency extension audio coding, such as spectral band replication (SBR) or spectral extension (SPX), from uncompressed waveforms such as PCM (pulse code modulation) encoded waveforms. A method for detecting frequency extension coding history in a time domain audio signal is described.

Abstract: Signatures that can be used to identify video and audio content are generated from the content by generating measures of dissimilarity between features of corresponding groups of pixels in frames of video content and by generating low-resolution time-frequency representations of audio segments. The signatures are generated by applying a hash function to intermediate values derived from the measures of dissimilarity and to the low-resolution time-frequency representations. The generated signatures may be used in a variety of applications such as restoring synchronization between video and audio content streams and identifying copies of original video and audio content. The generated signatures can provide reliable identifications despite intentional and unintentional modifications to the content.

Abstract: Techniques for ranking representative segments in media data are provided. Media features of many different types may be extracted from the media data. A plurality of ranking scores may be assigned to a plurality of candidate representative segments. Each individual candidate representative segment in the plurality of candidate representative segments comprises at least one scene in one or more statistical patterns in media features of the media data based on one or more types of features extractable from the media data. Each individual ranking score in the plurality of ranking scores may be assigned to an individual candidate representative segment in the plurality of candidate representative segments. A representative segment to be played to an end user may be selected from the candidate representative segments, based on the plurality of ranking scores.

Abstract: Techniques for scene change detection around seed points in media data are provided. Media features of many different types may be extracted from the media data. One or more statistical patterns of media features in a plurality of time-wise intervals around a plurality of seed time points of the media data may be determined using one or more types of features extractable from the media data. At least one of the one or more types of features comprises a type of features that captures structural properties, tonality including harmony and melody, timbre, rhythm, loudness, stereo mix, or a quantity of sound sources as related to the media data. A plurality of beginning scene change points and a plurality of ending scene change points in the media data may be detected, based on the one or more statistical patterns, for the plurality of seed time points in the media data.

Abstract: Derivation of a fingerprint includes generating feature matrices based on one or more training images, generating projection matrices based on the feature matrices in a training process, and deriving a fingerprint for one or more images by, at least in part, projecting a feature matrix based on the one or more images onto the projection matrices generated in the training process.

Abstract: Techniques for repetition detection in media data are provided. Media features of many different types may be extracted from the media data. Query sequences of fingerprints may be selected time intervals that begin at query times. Matched sequences of fingerprints may be determined. A set of offset values may be determined based on the matched sequences of fingerprints. This set of offset values may be further refined into a set of significant time points using a relatively targeted search and comparison method based on the media features of a second type extracted from the media data.

Abstract: Multiple candidate feature components of media content or projection matrices (or other hash functions, e.g., non-linear projections) are identified. Each of the candidate projection matrices (or other hash functions) includes an array of coefficients that relate to the candidate features. A subgroup of the candidate features or the projection matrices (or other hash functions) are selected based at least partially on an optimized combination of at least two characteristics of the candidate features or projection matrices (or other hash functions). Media fingerprints that uniquely identify the media content are derived from the selected optimized subgroup. Optimal projection matrices (or other hash functions) may be designed. Performance or sensitivity (e.g., search time) characteristics of the fingerprints are thus balanced with robustness characteristics thereof.

Abstract: Techniques for adaptive processing of media data based on separate data specifying a state of the media data are provided. A device in a media processing chain may determine whether a type of media processing has already been performed on an input version of media data. If so, the device may adapt its processing of the media data to disable performing the type of media processing. If not, the device performs the type of media processing. The device may create a state of the media data specifying the type of media processing. The device may communicate the state of the media data and an output version of the media data to a recipient device in the media processing chain, for the purpose of supporting the recipient device's adaptive processing of the media data.