1. Field of the Invention
This invention is in the field of computerized audio signal analysis and automated music preference determination.
2. Description of the Related Art
Music is known and loved throughout the world, and many advances in modern technology have, at least in part, been inspired by a desire to record music, and distribute recorded music to various individuals and groups.
Individual tastes in music of course vary widely, and as a result, the process of determining in advance which particular musical efforts an individual or group might like has traditionally been a rather uncertain business, usually requiring artistic human judgment.
Music is commonly classified by genre, artist, album title and song title. One common method of finding new music of potential interest is to use genre and artist type classification. For example, an individual who likes a known artist or group can use this knowledge to add to his or her collection of recordings from that artist or group. This method is less useful for finding other types of music, however. Music genres, although better than nothing, are not overly helpful, because they are usually too broad. As a result, the genre classification usually includes so many different artists as to be of marginal use in finding new specific songs or other recordings that match a particular individual's preferences.
Previous efforts to provide automated methods for music preference determination include U.S. Pat. No. 5,616,876 by Cuts, and U.S. Pat. No. 6,545,209 by Flannery et. al. This earlier work was based on a human based rating system, however.
As the Internet has proliferated, various Internet commerce and ecommerce sites have come to provide various types of music recommendations. These recommendations are generally based on other user's preferences, i.e., “people who bought music A also bought music B” type logic. This user recommendation methodology, although again better than nothing, has the drawback that it perpetuates the purchase of more common titles, and overlooks more obscure music which might have actually fitted a given individual's particular musical tastes better. Another drawback of prior art “other user recommendation” type methods is that these methods also require a significant amount of purchase history. This again tends to skew results towards older, more established, or more heavily marketed musical works.
Pandora Internet Radio has attempted to create a music classification process based on the “Music Genome Project” work of Glaser et. al., U.S. Pat. No. 7,003,515. In other documents, Pandora describes their method as: “our team of fifty musician-analysts has been listening to music, one song at a time, studying and collecting literally hundreds of musical details on every song. It takes 20-30 minutes per song to capture all of the little details that give each recording its magical sound—melody, harmony, instrumentation, rhythm, vocals, lyrics . . . and more-close to 400 attributes.” Clearly this is a very labor intensive, time consuming and subjective method that at its heart is not dissimilar from other prior art human analysis methods.
Various proposals for various types of more fully computerized methods for characterizing music have also been proposed. These include U.S. Pat. No. 5,918,223 by Blum et al., and others. These patents often focus on automated methods to characterize audio files according to various acoustic signal aspects such as the mean, standard deviation, autocorrelation and first derivative of at least one of the acoustical features of loudness, pitch, brightness, bandwidth, and MFCC coefficients.
Other patents in this type of computerized music rating art include U.S. Pat. No. 6,678,680 by Woo; 7,232,948 by Zhang; 5,712,953 by Langs; and others.
Despite this prior art, there are still no satisfactory automated music suggestion methods that are presently on the market. Thus further advances in this field would be useful.
Note that these methods should be distinguished from computerized efforts to identify various forms of the same music audio file, which are often used by copyright holders to detect unwanted copying. Such file identification methods are useless for identifying entirely different audio files that the user may like, but which otherwise are in no way copies of a song that the user may have been previously familiar with.