Music intelligence universe server2010-03-04Abstract text
An artificial intelligence song/music recommendation system and method is provided that allows music shoppers to discover new music. The system and method accomplish these tasks by analyzing a database of music in order to identify key similarities between different pieces of music, and then recommends pieces of music to a user depending upon their music preferences.Claims
1. A method of determining a user's preference of music, said method comprising the steps of: a) providing a digital database comprising a plurality of digital song files; b) mathematically analyzing each said digital song file to determine a numerical value for a plurality of selected quantifiable characteristics; c) compiling a song vector comprising a list of said numerical values for each of said plurality of selected characteristic for each said song file; d) selecting and storing a representative portion of each said song file wherein said representative portion substantially mathematically matches the song vector of said song file; f) choosing a pair of two dissimilar representative portions and enabling said user to evaluate both representative portions; g) permitting said user to indicate which of said two dissimilar representative portions said user prefers; h) based on the indication from said user of which of said two dissimilar representative portions said user prefers, finding another pair of dissimilar representative portions to maximize the probability to learn something about the user's preference; and i) repeating steps f) through h), as necessary, to establish a taste vector for said user comprising song characteristics that said user prefers.
2. The method according to claim 1, wherein said method is performed via a computer website.
3. The method according to claim 1, wherein none of said pairs of two dissimilar representative portions are repeated between consecutive steps.
4. The method according to claim 1, wherein each of said pairs of two dissimilar representative portions are selected to maximize Euclidian distance between each song represented by said representative portion.
5. The method according to claim 1, wherein each of said pairs of two dissimilar representative portions are selected to maximize orthogonality with respect to previous pairs of representative portions.
6. A method of recommending music comprising: a) establishing a digital database comprising a plurality of digital song files; b) mathematically analyzing each said digital song file to determine a numerical value for a plurality of selected quantifiable characteristics; c) compiling a song vector comprising a list of said numerical values for each of said plurality of selected characteristic for each said song file; d) establishing an affinity value for each song in the database as compared to every other song in the database; and j) displaying a representation of each song vector on a two-dimensional array wherein the distance between each song vector representation corresponds to the affinity value.
7. The method according to claim 6, wherein said representation of the song vector uses color codes or other symbols to identify different genres of songs.
8. The method according to claim 6, wherein at least some of the characteristics are based on global characteristics.
9. The method according to claim 8, wherein said global characteristics are selected from the group consisting of: happy; sad; calm; and energetic.
10. The method according to claim 6, wherein said two-dimensional array comprises a computerized graphic user interface.
11. The method according to claim 10, wherein said computerized graphic user interface is displayed in a location selected from the group consisting of: in-store retail terminals; retail internet websites; personal computers personal music player devices; and mobile phone handsets.
12. The method according to claim 6, wherein said digital database comprising a plurality of digital song files is selected from the group consisting of: private music collection; radio station music library; recording label music library; and music store song library.Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation-in-part of copending and co-owned U.S. patent application Ser. No. 11/492,395, filed with the U.S. Patent and Trademark Office on Jul. 25, 2006 entitled "Method and System for Music Recommendation", which is a continuation of co-pending and co-owned U.S. patent application Ser. No. 10/678,505, filed with the U.S. Patent and Trademark Office on Oct. 3, 2003 entitled "Method and System for Music Recommendation", now U.S. Pat. No. 7,081,579, which is based upon and claims benefit of copending and co-owned U.S. Provisional Patent Application Ser. No. 60/415,868 entitled "Method and System for Music Recommendation", filed with the U.S. Patent and Trademark Office on Oct. 3, 2002 by the inventors herein, the specifications of which are incorporated herein by reference.
[0002] This application also claims benefit of copending and co-owned U.S. Provisional Patent Application Ser. No. 60/857,627 entitled "Music Intelligence Universe Server", filed with the U.S. Patent and Trademark Office on Nov. 8, 2006 by the inventors herein, the specification of which is incorporated herein by reference.
BACKGROUND OF THE INVENTION
[0003] 1. Field of the Invention
[0004] The invention disclosed herein relates generally to methods and systems for analyzing and using digital music compositions, and more particularly to a method and system for determining the characteristics of a musical composition by analyzing its digital composition, and recommending particular musical compositions to users based upon the relative comparability of a user's desired musical characteristics and the musical characteristics of a collection of digital music.
[0005] 2. Background of the Prior Art
[0006] Historically, what is pleasing to the human ear has not changed since man began making sounds. Patterns in music that are pleasing to the human ear have not changed much, if at all, since the times of the classical composers. What has changed are styles, performances, the instruments used, and the way music is produced and recorded, but a compelling melody is still compelling and a series of random notes still sounds random. For example, the dictionary describes melody as a series of notes strung together in a meaningful sequence. Unfortunately, some sequences sound meaningful and make up a beautiful song and other sequences just sound like noise.
[0007] While the number of possible melody patterns combined with all of the other variables in recorded music allow for a seemingly infinite number of combinations, the patterns that we find pleasing have not changed. That is not to say everything has been invented, however. So far, every new style of music that has come into being: country, rock, punk, grunge etc. have all had similar mathematical patterns. The hits in those genres have all come from the same `hit` clusters that exist today and anything that has fallen outside of such `hit` clusters has rarely been successfully on the charts for its musical qualities.
SUMMARY OF THE INVENTION
[0008] It is an object of the present invention to provide a method and system for measuring the characteristics of a musical composition, and establishing a collection of digital musical compositions that may be sorted based upon such characteristics.
[0009] It is another object of the present invention to provide a method and system for determining a preferred musical characteristic profile for a music listener.
[0010] It is another object of the present invention to enable a method and system to compare digital music files to discover mathematically similar songs.
[0011] In accordance with the above objects, an artificial intelligence song/music recommendation system and method is provided that allows music shoppers to discover new music. The system and method accomplish these tasks by analyzing a database of music in order to identify key similarities between different pieces of music, and then recommends pieces of music to a user depending upon their music preferences.
[0012] The system uses a series of complex artificial intelligence algorithms to analyze a plurality of sonic characteristics in a musical composition, and is then able to sort any collection of digital music based on any combination of similar characteristics. The characteristics analyzed are those that produce the strongest reaction in terms of human perception, such as melody, tempo, rhythm, and range, and how these characteristics change over time. This approach enables the creation of "constellations" of music with similar characteristics, even from different genres and styles, enabling fast yet highly individualized music discovery. Further personalized music discovery is enabled based on a "Music Taste Test".
[0013] To provide users with music recommendations, the system employs a number of analysis functions. A "Music Taste Test" (MI Mood module) function learns a user's music preferences via a series of binary choice questions, and delivers lists and/or personalized song recommendations to the user based on this information. Recommendations are prioritized and listed in order of closest song match on a theoretical multi-dimensional grid. A "Soundalikes" function links songs having similar musical/mathematical profiles enabling for music recommendation. This function was referred to as "more like this" in U.S. Pat. No. 7,081,579 to Alcalde et al., the specification of which is incorporated herein by reference. A "Discovery" function that also links songs having similar mathematical patterns, but that allows for a wider recommendation than the "Soundalikes" function. The "Music Taste Test" function and "Soundalikes" function cooperate to establish `moods` for each song, such as happy, sad, calm, and energetic.
[0014] The various features of novelty that characterize the invention will be pointed out with particularity in the claims of this application.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Other objects, features, and advantages of the present invention will become more apparent from the following detailed description of the preferred embodiments and certain modifications thereof when taken together with the accompanying drawings, in which:
[0016] FIG. 1 is a schematic overview of a system according to the present invention.
[0017] FIG. 2 is an overview of the server configuration accessible by a user of the present invention.
[0018] FIG. 3 is a chart of the MI Soundalikes service according to the present invention.
[0019] FIG. 4 is an illustration of a graphical user interface for the MI Soundalikes service according to the present invention.
[0020] FIG. 5 is a chart of the MI Mood service according to the present invention.
[0021] FIG. 6 is an illustration of a graphical user interface for the MI Mood service according to the present invention.
[0022] FIG. 7 is an illustration of a graphical user interface for the MI Room service according to the present invention.
[0023] FIG. 8 is a schematic of the global system architecture of the present invention.
[0024] FIG. 9 shows a filtered and unfiltered image of the music universe in an example of the present invention.
[0025] FIG. 10 shows a taste vector in the filtered music universe of FIG. 6.
[0026] FIG. 11 shows alternate song pair selections in the filtered music universe of FIG. 6.
[0027] FIG. 12 shows a narrowed music universe after a first song selection.
[0028] FIG. 13 shows a further narrowed music universe after a second song selection.
[0029] FIG. 14 shows a still further narrowed music universe after a third song selection.
[0030] FIG. 15 shows a Mood state flow diagram.
[0031] FIG. 16 illustrates a visual transformation from a two-dimensional to a three-dimensional music universe according to the present invention.
[0032] FIG. 17 illustrates the evolution of the number of song pairs to be analyzed according to the present invention.
[0033] FIG. 18 illustrates a visual transformation from a two-dimensional to a three-dimensional music universe according to the present invention.
[0034] FIG. 19 shows a pre-calculated learning tree for three questions in an example of the present invention.
[0035] FIG. 20 shows the operational hierarchy and relations of the most significant classes according to the present invention.
[0036] FIG. 21 is an illustration of a graphical user interface for the Music Constellation service according to the present invention.
[0037] FIG. 22 shows Iris data projection on two dimensions using Linear Discriminant Analysis.
[0038] FIG. 23 shows Iris data projection on two dimensions using Generalized Discriminant Analysis.
[0039] FIG. 24 is an illustration of a graphical user interface for the MI Moodstellation service according to the present invention.
[0040] FIGS. 25-29 show the graphical user interface for the MI Moodstellation of FIG. 24 to illustrate additional features according to the present invention.
DESCRIPTION OF EXEMPLARY EMBODIMENTS
[0041] The invention summarized above and defined by the enumerated claims may be better understood by referring to the following description, which should be read in conjunction with the accompanying drawings. This description of an embodiment, set out below to enable one to build and use an implementation of the invention, is not intended to limit the enumerated claims, but to serve as a particular example thereof. Those skilled in the art should appreciate that they may readily use the conception and specific embodiment(s) disclosed as a basis for modifying or designing other methods and systems for carrying out the same purpose(s) of the present invention. Those skilled in the art should also realize that such equivalent assemblies do not depart from the spirit and scope of the invention in its broadest form.
[0042] Referring to FIG. 1, the method and system of the instant invention enable the analysis and processing of digital music in order to establish a description of a number of characteristics of the music, and likewise enable recommending a collection of music having particular characteristics to a user who has established a desired musical characteristic profile.
[0043] The raw materials for the system are music and songs. These are stored in a digital file, which is the main starting point for all embodiments. The first step performed by the system is to analyze an existing digital music file in order to create a descriptive profile for the musical characteristics of the song. The music analysis mimics human hearing and perception. In a first stage, the analysis portion reads a song file and extracts some data. This data can be represented as a series of numbers, which are the main input for future processing. Such processing depends on the final application, and can use algorithms such as Principal Components Analysis (PCA), KNearest Neighbors (kNN), etc.
[0044] The processes, according to the present invention, start by analyzing a large and representative sample of music. The process analyzes more than 60 characteristics of the music, such as brightness and tempo, and measures how the characteristics change over time. The selected characteristics have been identified in user testing to produce the strongest reaction. Often the characteristics are perceived unconsciously by the listener, and the correct mix of parameters is more important than any individual parameter by itself. Parameter analysis is described in U.S. Pat. No. 7,081,579 to Alcalde et al., the specification of which is included herein by reference, in its entirety.
[0045] In a preferred embodiment, the processes described herein measure innovation/prediction cycles in musical structure by using spectrum variables for power law detection. They also measure deviation analysis from the universality trend through detection of cycles from the universality trend and the detection of innovation and prediction wavelets.
[0046] Following analysis of the sonic parameters, software modules according to a preferred embodiment of the present invention learn a user's musical preferences. The software uses two modules; one called ADAM, a cluster recognition engine, and another called EVE, a music recommendation engine.
[0047] ADAM is a conceptual clustering engine that is bas...
Device for correcting timing of music playing information for use in music auto play device2010-02-24AbstractA device for correcting a timing of music playing information including a determining unit and a correcting unit, the correcting unit corrects the timing of the change of the performance of a piece of music, which is outside the range of short time data, to a timing within the range of the short time data, in response to a determination by the determining unit.ClaimsWe claim:
1. A device for correcting a timing of music playing information for use in an auto play device, comprising:
music playing information memorizing means for memorizing a timing of a change of a performance of a piece of music;
long time data memorizing means for memorizing a long time data, the timing of the change of the performance of a piece of music being corrected to an exact timing occurring at each interval of said long time data;
short time data memorizing means for memorizing a short time data, a time length of said short time data being shorter than a time length of said long time data, said short time data being set around a timing occuring at each interval of said long time data;
determining means for determining whether or not the timing of the change of the performance of a piece of music is within a range of said short time data; and
correcting means for correcting the timing of the change of the performance of a piece of music, which is outside the range of said short time data, to a timing within the range of said short time data, in response to a determination by said determining means.
2. A device for correcting a timing of music playing information for use in an auto play device according to claim wherein said correcting means corrects only a predetermined content of the performance of a piece of music.
3. A device for correcting a timing of music playing information for use in an auto playing device according to claim 1, wherein said long time data corresponds to a beat of the performance of a piece of music.
4. A device for correcting a timing of music playing information for use in an auto play device according to claim wherein the range of said short time data is set about the timing occurring at each interval of said long time data.
5. A device for correcting a timing of music playing information for use in an auto play device according to claim 1, wherein the range of said short time data is set before or after the timing occurring at each interval of said long time data.
6. A device for correcting a timing of music playing information for use in an auto play device according to claim 1, wherein said musical playing information is composed of at least one of melody information, accompaniment information, and rhythm information.
7. A device for correcting a timing of music playing information for use in an auto play device according to claim 1, wherein said timing of the change of a performance of a piece of music represents a timing at which a sound is produced.DescriptionBACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to a auto play device for playing music (hereinafter referred to as an auto play device) based on a prememorized timing of a change of a performance of a piece of music, more particularly it relates to a device for correcting a timing of the music playing information by which a timing of a change of a performance of a piece of music is corrected to a timing occurring at a predetermined interval.
2. Description of the Related Art
FIG. 1A illustrates the timing correction operation performed by a known timing correction device. In FIG. 1A, the music playing information is corrected by a time length of an 8th note, which time length corresponds, for example, to a time length of 48 clock pulses (hereinafter referred to as CK). Note, the time length of 1 CK corresponds to the time length of a unit of time which represents a standard timing for processing the music playing information. Referring to FlG. 1A, a head timing of a bar is defined as "1", a timing which is the time length of an 8th note from the timing "1" is defined as "2", a timing which is the time length of an 8th note from the timing "2" is defined as "3", a timing which is the time length of an 8th note from the timing "3" is defined as "4", and so on, and timings between the timings "1", "2", "3", "4", . . . are defined as "1-2", "2-3", "3-4", "4-5", . . . The timing of the change of the music playing information between "1" and "1-2" is corrected to "1", the timing of the change of the music playing information between "1-2" and "2-3" is corrected to "2", the timing of the change of the music playing information between "2-3" and "3-4" is corrected to "3", and the timing of the change of the music playing information between "3-4" and "4-5" is corrected to "4". Accordingly the timing of the change of the music playing information is precisely corrected to a timing of an 8th note, and thus such a timing correction device provides a convenient and more exact playing of music.
Nevertheless, in practice a player does not always play at an exact timing, but often intentionally shifts from the exact timing by, for example, a "preceding shift" or a "subsequent shift",to produce delicate musical nuances.
Accordingly, a problem arises in that a musical performance becomes mechanical if the timing is corrected too exactly.
SUMMARY OF THE INVENTION
An object of the present invention is to provide a device for correcting a timing of music playing information, which device is capable of easily carrying out a shift from an exact musical timing by, for example, a "preceding shift" or a "subsequent shift".
According to the present invention, there is provided a device for correcting a timing of music playing information for use in an auto playing device which comprises: a music playing information memorizing means for memorizing a timing of a change of a performance of a piece of music, a long time data memorizing means for memorizing long time data, the timing of the change of the performance of the piece of music being corrected to an exact timing occuring at each interval of long time data: a short time data memorizing means for memorizing short time data, a time of the short time data being shorter than a time of the long time data, the short time data being set around the timing occuring at each interval of long time data; a determining means for determining whether or not the timing of the change of the performance of the piece of music is within a range of the short time data; and correcting means for correcting the timing of the change of the performance of the piece of music, which is outside the rang...
