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:
Systems and methods for detecting a synchronization code word embedded in a plurality of frames of a signal are described. In one example embodiment, the synchronization code word contains “s” bits, embedded one bit per frame in “s” frames of an input signal. The method of detecting this synchronization code word includes: initiating a first segmentation procedure wherein “n” segments are defined in each signal frame of the input signal. A first correlation threshold value, which is based on the synchronization code word, is used to identify in the “n” segments, a first segment having the highest likelihood of containing at least a portion of the synchronization code word. The first segment is used to initiate a recursive detection procedure incorporating one or more additional segmentation procedures and one or more additional correlation threshold values, to detect the synchronization code word in a sub-divided portion of the first segment.
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:
A media fingerprint archive system generates and archives media fingerprints from second media content portions such as commercials. A downstream media measurement system can extract/derive query fingerprints from an incoming signal, and query the media fingerprint archive system whether any of the query fingerprints matches any archived fingerprints. If so, the media measurement system can perform media measurements on a specific secondary media content portion from which the matched query fingerprint is derived. If not, the media measurement system can analyze media characteristics of a media content portion to determine whether the media content portion is a secondary media content portion and perform media measurement if needed to. The media measurement system may send fingerprints from an identified secondary media content portion to the media fingerprint archive system for storage.
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:
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:
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. The method may comprise transforming the time domain audio signal into a frequency domain, thereby generating a plurality of subband signals in a corresponding plurality of subbands comprising low and high frequency subbands; determining a degree of relationship between subband signals in the low frequency subbands and subband signals in the high frequency subbands; wherein the degree of relationship is determined based on the plurality of subband signals; and determining frequency extension coding history if the degree of relationship is greater than a relationship threshold.