The use of audio encoding has been known in the art, and was partly pioneered by such companies as Arbitron for audience measurement research. Known techniques exploit the psychoacoustic masking effect of the human auditory system whereby certain sounds are humanly imperceptible when received along with other sounds. One such technique utilizing the psychoacoustic masking effect is described in U.S. Pat. Nos. 5,450,490 and 5,764,763 (Jensen et al.), both of which are incorporated by reference in their entirety herein, in which information is represented by a multiple-frequency code signal which is incorporated into an audio signal based upon the masking ability of the audio signal. The encoded audio signal is suitable for broadcast transmission and reception as well as for recording and reproduction. When received the audio signal is then processed to detect the presence of the multiple-frequency code signal. Sometimes, only a portion of the multiple-frequency code signal, e.g., a number of single frequency code components, inserted into the original audio signal are detected in the received audio signal. If a sufficient quantity of code components is detected, the information signal itself may be recovered.
While audio codes have proven to be effective at determining exposures to specific media, audio signature systems provide little to no semantic information regarding the media. As used herein below, the terms “semantic,” “semantic information,” “semantic audio signatures,” and “semantic characteristics” refer to information processed from time, frequency and/or amplitude components of media audio, where these components may serve to provide generalized information regarding characteristics of the media, such as genre, instruments used, style, etc., as well as emotionally-related information that may be defined by a customizable vocabulary relating to audio component features (e.g., happy, melancholy, aggressive).
Some efforts have been made to semantically classify, characterize, and match music genres and are described in U.S. Pat. No. 7,003,515, titled “Consumer Item Matching Method and System,” issued Feb. 21, 2006 and is incorporated by reference herein. However, these efforts often rely on humans to physically characterize music. Importantly, such techniques do not fully take advantage of audio signature information together with semantic information when analyzing audio content. Other efforts have been made to automatically label audio content for Music Information Retrieval Systems (MIR), such as those described in U.S. patent application Ser. No. 12/892,843, titled “Automatic labeling and Control of Audio Algorithms by Audio Recognition,” filed Sep. 28, 2010, which is incorporated by reference in its entirety herein. However such systems can be unduly complex and also do not take full advantage of audio encoding technology together with semantic processing. As such, there is a need in the art to provide semantic information based on generic templates that may be used to identify semantic characteristics of audio, and to use the semantic characteristics in conjunction with audio signature technology. Additionally, there is a need to identify such characteristics for the purposes of audience measurement. Currently advertisers target listeners by using radio ratings. These rating are gathered by using encoding or audio matching systems. As listening/radio goes to a one-to-one experience (e.g. Pandora, Spotifiy, Songza, etc.), there is a need for advertisers to be able to target listeners by the style of music they listen, along with other related information. Semantic analysis can identify this information and provide useful tools for targeted advertisement. Furthermore, semantic information may be used to provide supplemental data to matched audio signature data.