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Method and apparatus for automatic detection and identification of unidentified broadcast audio or video signals

Method and apparatus for automatic detection and identification of unidentified broadcast audio or video signals description/claims

This application claims priority to PCT/US05/04802, filed on Feb. 16, 2004, and 60/736,348, filed on Nov. 15, 2005, both of which are incorporated herein by reference.

The present invention relates to a method of detecting and tracking unknown broadcast content items that are periodically encountered by automatic detection and tracking systems. It is known in the art that detection of broadcast content, for example, music broadcast over radio, includes the sampling the of the identified content to compute numerical representations of features of the content, sometimes referred to in the art as a fingerprint, or in the related patent application PCT/US05/04802, filed on Feb. 16, 2004, which is incorporated herein by reference, a pattern vector. These known pattern vectors are stored in a database and while the broadcast signals are received, the same computation is applied to the incoming signal. Then, the detection process entails searching for matches between the incoming computed pattern vectors and the vast database of pre-created pattern vectors associated with the identity of known content.

This system runs into problems when content that has not been registered in the database yet and is being broadcast anyway. In the prior art, these unknown or unmatched programming items would be ignored. This invention is directed to address this shortcoming by determining when a likely piece of programming content has been detected, tracking such detections, and then submitting the piece for human identification in order that proper publishing or other indicia of identity be associated with the content. The system automatically determines which portions of the broadcast signal are previously un-registered content.

FIG. 1 is a schematic of a Radio Monitoring System.

FIG. 2 is an Illustration of a repetition of the same program along the time-axis.

FIG. 3 is a schematic of how the invention is operated

FIG. 4 is the workflow of exercising the first grouping and the second grouping

Short Coming of a Radio Monitoring System The core of a modern radio monitoring system (see FIG. 1) is a detection system consisting of a detection algorithm and a database. The database, referred to as the detection database, is populated with identification information of the programs to be detected, e.g. songs and commercial ads. The identification information includes the fingerprint of the program (in this document, the term “pattern” or “pattern vector” will be used instead of “fingerprint”). Signals received from an electronic media broadcast, for example, a radio broadcast are processed to extract patterns at regular intervals in time. Patterns from broadcast are compared with patterns registered in the detection database. A detection is made if a program registered in the database matched with that of the broadcast. Matching includes more than an identical match, but also close matches and a series of matches over time that are determined to be sufficiently consistent with a specific program. The short coming of the system is that detection result is determined by what is being stored in the database. Clearly, if a particular song is not registered in the detection database, the song will never be detected.

A Semi-Automatic Solution. It is a well-known fact that certain programs, particularly songs and commercial ads, are broadcast repeatedly. This repetition provides the opportunity for a detection algorithm to detect programs that have been repeated, even if they are not registered in the detection database. An important principle being employed to design the detection algorithm on repetitive programs is the self-similarity principle. If a program is broadcast at time T1 and is repeated at T2, T2>T1. Programs located along the time-axis at T1 and T2 are said identical.

To detect if a program, which was located at T1, is repeated elsewhere, we can cut out the content located at T1, and run an identification exercise to identify along the time-axis when this piece of content is repeated. We will then identify that program located at T1 if it is repeated at T2. The invention is directed to exercise the self-similarity principle in order to detect unregistered programs that have been repeated. The invention is designed to bring in all programs that have at least one repetition within a pre-determined period of time. The invention can generate results autonomously while other matching and identification processes are running. However, the actual identity of the content of each of the results is not known until it has been listen by human operators. Clearly, listening process is a manual process. Alternatively, the harvested results can be converted to registered content if it turns out that a piece of content is registered whose pattern vectors sufficiently match those associated with content only identified as a unique piece of content as a result of the operation of the invention. In other words, as content is registered in the database, it can be checked to see if it has already been detected by the invention, and the database of detections updated accordingly.

If the invention outputs a identifiable piece of content, referred to herein as a “harvest”, such as a new release of a song not actually registered in the database, the pattern vectors of this song that have been recovered by the invention can be extracted from one of the corresponding broadcast signal clips and registered into the detection database of the media monitoring system. By definition, each of the harvests has at least two copies because at a minimum, self-similarity between to instances in time is necessary to determine the repetition that indicates the presence of an individually identifiable piece of content. Selecting which repetition should be registered into the detection database is determined by the audio quality. The invention also includes a step called Audio-Selector, which selects from all the repetitions of the harvested content, the one that has the best audio quality. The pattern vectors computed from this repetition is registered in the database, typically with a unique identification number that is used in lieu of an actual title, because the title is not known yet.

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