Abstract: A method for performing audio copy detection, comprising, providing a query audio data, the query audio data having a succession of frames and also providing a plurality of test audio data units, each test audio data unit including a succession of frames. For each test audio data unit the method generates a test fingerprint set. The generation of the test fingerprint test including computing similarity measurements between at least one frame of the test audio data and a plurality of frames of the query audio data. A test audio data unit is then selected as a match for the query audio data at least in part on the basis of the fingerprint sets.

Abstract: Occurrences of one or more keywords in audio data are identified using a speech recognizer employing a language model to derive a transcript of the keywords. The transcript is converted into a phoneme sequence. The phonemes of the phoneme sequence are mapped to the audio data to derive a time-aligned phoneme sequence that is searched for occurrences of keyword phoneme sequences corresponding to the phonemes of the keywords. Searching includes computing a confusion matrix. The language model used by the speech recognizer is adapted to keywords by increasing the likelihoods of the keywords in the language model. For each potential occurrences keywords detected, a corresponding subset of the audio data may be played back to an operator to confirm whether the potential occurrences correspond to actual occurrences of the keywords.

Abstract: A method for performing audio copy detection, comprising, providing a query audio data, the query audio data having a succession of frames and also providing a plurality of test audio data units, each test audio data unit including a succession of frames. For each test audio data unit the method generates a test fingerprint set. The generation of the test fingerprint test including computing similarity measurements between at least one frame of the test audio data and a plurality of frames of the query audio data. A test audio data unit is then selected as a match for the query audio data at least in part on the basis of the fingerprint sets.

Abstract: Occurrences of one or more keywords in audio data are identified using a speech recognizer employing a language model to derive a transcript of the keywords. The transcript is converted into a phoneme sequence. The phonemes of the phoneme sequence are mapped to the audio data to derive a time-aligned phoneme sequence that is searched for occurrences of keyword phoneme sequences corresponding to the phonemes of the keywords. Searching includes computing a confusion matrix. The language model used by the speech recognizer is adapted to keywords by increasing the likelihoods of the keywords in the language model. For each potential occurrences keywords detected, a corresponding subset of the audio data may be played back to an operator to confirm whether the potential occurrences correspond to actual occurrences of the keywords.

Abstract: A method and system for detecting certain types of content, such as advertisements, using acoustical means from a media stream. The method uses two matching processes to detect and identify repeated content, the starting and end boundaries of which are then found. This content is used as the basis to find non-repeated content (such as less-frequently repeated advertisements) that are typically located in proximity to repeated content and can be evaluated using Gaussian mixture models (GMMs). The system that implements this method can be used for advertisement detection and monitoring for traditional media, such as television and radio, as well as for Internet-based media, such as streaming video, streaming audio and podcasts. The system can also be used to detect and identify copyrighted material in Internet traffic.