Abstract: Some methods may involve receiving a block of audio data, the block including N pulse code modulated (PCM) audio channels, including audio samples for each of the N channels, receiving metadata associated with the block of audio data and receiving a first set of values corresponding to reference audio samples. A second set of values, corresponding to audio samples from the block of audio data, may be determined. The first and second set of values may be compared. Based on the comparison, it may be determined whether the block of audio data is synchronized with the metadata.

Abstract: Some methods may involve receiving a block of audio data, the block including N pulse code modulated (PCM) audio channels, including audio samples for each of the N channels, receiving metadata associated with the block of audio data and receiving a first set of values corresponding to reference audio samples. A second set of values, corresponding to audio samples from the block of audio data, may be determined. The first and second set of values may be compared. Based on the comparison, it may be determined whether the block of audio data is synchronized with the metadata.

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: Features are extracted from video and audio content that have a known temporal relationship with one another. The extracted features are used to generate video and audio signatures, which are assembled with an indication of the temporal relationship into a synchronization signature construct. the construct may be used to calculate synchronization errors between video and audio content received at a remote destination. Measures of confidence are generated at the remote destination to optimize processing and to provide an indication of reliability of the calculated synchronization error.

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: 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: Features are extracted from video and audio content that have a known temporal relationship with one another. The extracted features are used to generate video and audio signatures, which are assembled with an indication of the temporal relationship into a synchronization signature construct. the construct may be used to calculate synchronization errors between video and audio content received at a remote destination. Measures of confidence are generated at the remote destination to optimize processing and to provide an indication of reliability of the calculated synchronization error.

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.