Abstract textAn 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.Claims1. 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.DescriptionCROSS 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 based on physical pattern recognition models. This non-supervised learning system generates a hierarchical tree structure that is based on topological metrics, which automatically determines the final number of clusters while allowing for automated related variable detection. The methodology for detecting social trends is completely scalable, and has been successfully applied in many other areas. It is also used for the preliminary visualization engine described in more detail below. [0048] EVE is a non-linear kernel learner, which had been successfully used in many other commercial applications. This supervised learning system uses technology that has been proven to outperform statistical and neural network systems. A mathematically elegant solution which is relatively easy to customize and refine, the algorithm uses a direct strategy to capture personal Von Neumann Morgenstern utility functions. Due to their elegant and parsimonious mathematical architecture, both ADAM and EVE have been easily ported to new operating system environments, such as Symbian 60. [0049] After the system has learned a user's musical preferences, it can connect the user with music selections based on his or her likes and dislikes. The Hit Song Science (HSS) techniques are described in U.S. Pat. No. 7,081,579. The Music Intelligence Universe (MIU) techniques are herein described below. [0050] FIG. 2 depicts a schematic view of a system architecture for enabling the processing of digital music files to an automated digital music file analysis tool in order to generate an output file that serves as a descriptor of the musical characteristics of the particular musical composition that was analyzed. The output of such analysis engine may be used by the recommendation utilities described below. [0051] Once the catalogue of music has been analyzed, the technology of the present invention can give music recommendations in five different ways: [0052] MI Soundalikes ("More like this") [0053] MI Discovery [0054] MI Mood [0055] MI Room (or Music Room) [0056] MI Constellation (or Music Constellation) In all cases, results can be customized further as the user can opt to receive music matches across genres, time periods, or other customizable factors. [0057] The first kind of user interaction is related to the MI Soundalikes service, as shown in FIG. 3. This technique links a song or group of songs to a selection of music that has a similar profile. It takes the individual mathematical profile of the song or songs and compares it to all the music in the database. Given a list of songs, each can have a "Soundalikes" link to similar music. A User inputs a "seed" song or group of songs to generate a playlist of songs that have a similar analysis profile. This technique takes the individual profile of the song or songs and matches it to the whole catalogue of music in the database. The user can use the database music search engine to find songs by specifying the artist name, song title name, genre, year, etc. to select a song. An example of a user interface to select similar music is shown in FIG. 4. This service enables the user to find a list of songs musically similar to the selected one, and the key point is that no meta-tag (artist name, genre, etc) information is used to find the similarity; only the recorded song sound, only the music itself. MI Soundalikes produces a list of songs that are most similar; and MI Discovery offers a wider range of similarity allowing for further music discovery. [0058] The system can also learn from implicit information (if available), namely songs previously downloaded, or listened by the user. The system will take this list of songs as an initial music profile of the user. The system also integrates other user-based information that allows for users-groups collaboration when presenting an integrated recommendation. [0059] Similarly, the system allows for analyzing a personal music catalogue, classifying it, and determining the different clusters of the user's catalogue. The system can then recommend new songs that match the catalogue profile either as a whole, or as matching some particular cluster of the catalogue. In the same way, the system allows for music recommendation to a group of users, taking the musical "group profile" as the initial music input for the system. [0060] The second kind of user interaction is the MI Mood service, as shown in FIG. 5. The user expects a list of recommended songs from the server. The MI Mood service discovers a user's unique personal musical taste by directing them through a "music taste test". Users are presented with a number of binary choices between two short audio clips and will choose the sound clip they prefer. An example of a user interface for the music taste test is shown in FIG. 6. After a series of questions, it is possible to generate a taste profile for that user; the profile is analogous to a song profile, as measured in the analysis phase. The user profile is then matched with a song's own profile, as measured by the music analysis. In this way, songs from the database that share commonalties to the user's taste...
AbstractA 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; andcorrecting 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 INVENTION1. Field of the InventionThe 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 ArtFIG. 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 INVENTIONAn 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 range of the short time data, to a timing within the range of the short time data, in response to a determination by the determining means.In the present invention, when the timing of the music playing information is within the range of the short time data, the timing of the musical playing information is not corrected, and when the timing of the music playing information is outside the range of the short time data, the timing of the musical information is corrected and brought within the range of the short time data, which is shifted from the exact timing. Therefore, the timing of the performance of the piece of music can be intentionally shifted. Accordingly, a state which is shifted from a exact timing based on a performance of a piece of music, i.e., a "preceding shift" or a "subsequent shift", is accurately obtained, and the performance of the piece of music is brought to a high level. Moreover, a timing of a change of a predetermined content of a performance of a piece of music, for example, a timing of a change of only a snare drum sound or only a cymbals sound can be minutely shifted, and thus a more harmonious auto playing of a piece of music can be realized.Further scope of applicability of the present invention will become apparent from the detailed description given hereinafter. However, it should be understood that the detailed description and specific examples, while indicating preferred embodiments of the invention, are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description.BRIEF DESCRIPTION OF THE DRAWINGSThe present invention will become more fully understood from the detailed description given hereinbelow and the accompanying drawings which are given by way of illustration only, and thus are not limitative of the present invention, and whe...
AntimonJoined: Jan 18, 2005: 1432Lation: SwedenAudio files: 19G2 pah files: 64Trpy!Stefan_________________@myspe @virb A blog home - inyberspe no onean hear you SEGVAntimonJoined: Jan 18, 2005: 1432Lation: SwedenAudio files: 19G2 pah files: 64I like them, espially the jazzyval thingies (1st and third). They keep going, but there is variation enough to keep it interesting.Stefan_________________@myspe @virb A blog home - inyberspe no onean hear you SEGV There is no sin eept stupidity - Oar WildegsahezJoined: May 05, 2008: 134Lation: MexoAudio files: 4I agree, for the sond one most of it ...
AbstractA capo for a stringed musical instrument, comprising a substantially rigid bar member for engaging the strings of a musical instrument, a flexible strap which can be passed around the neck of the musical instrument to maintain the bar member in engagement with said strings, and securing means whereby the strap can be secured. Preferably the strap is secured at one end to one end of the bar member and the other end of the bar member has a transverse slot therein through which the other end of the strap can be passed and doubled back on itself and secured by means of a releasable touch-and-close fastener the two components of which are provided at different positions on one surface of the strap. In an embodiment at least one tuning reed is provided in a bore in the bar member to enable the capo to serve the further function of a pitch-pipe for tuning.ClaimsWe claim:1. A capo for a stringed musical instrument, the capo comprising a resilient bar member having a flat surface for engaging the strings of a said musical instrument, said bar memberhaving a depending flange at one end thereof adapted to hook over and engage a side surface of the neck of a said musical instrument to locate the bar member relative to the instrument and a transverse slot adjacent the other end thereof, a strap one endof which is connected to said one end of the bar member and the other end of which, after having been passed around the neck of a said instrument, is adapted to be passed through said transverse slot and pulled tight against the resistance of saiddepending flange en...
h4>AbstractAccording to the invention, a pedal operation is permitted independently for each group in a keyboard, and thus various ways of sounding musical tones can be realized. More specifically, a plurality of designation elements for designating the sounding of musical tones are divided into a plurality of groups, which are designated, and according to the group designation a musical control by a pedal operation is permitted independently for each group. Thus, a choice of a musical control by a pedal operation can be made for each group according to the group designation, thus permitting various ways of sounding musical tones.ClaimsI claim:1. A device for executing musical control with a pedal for an electronic musical instrument comprising:a pedal, operable for instructing the musical control;first discriminating means for discriminating an operation of said pedal;a plurality of sounding instruction means for instructing a sounding of musical tones;group designation means for designating at least one of a plurality of groups, into which said plurality of sounding instruction means are divided, and for making said pedal operative or inoperative;second discrimination means for discriminating a status of the designation by said group designation means; andmusical control means for individually executing the musical control of said pedal with respect to musical tones designated by the sounding instruction means for individual tones of said one group designated and made operative by ...
It is great to play around with the basic approaches of music making and also the FM synthesis, but there's so much more that can be done with sound synthesis. There are boundaries as to what could be easily performed with the new methods of synthesis due to the fact that they are computer based. The full concept of digital synthesis is set on trying to duplicate acoustic and also analogue sounds. There's always a few ideas and techniques being utilized, and the success is set on how accurate is the audible reproduction of the final mixIn the world of digital sound synthesis, there are four serious methods that can be applied.Method one : Frequency Domain and Spectral Modeling. Say we took the Hartmann Neuron synthesizer as an illustration, you would find it being a spectral model. Its very well used in additive synthesis and also re-synthesis. This is referring to the frequency domain.Method two : Physical Modeling.Physical sounds such as spring simulations as well as mass simulations can be really closely reproduced by physical modeling. This is because this type of modeling counts on mathematical m...
Ionfessed a long time ago eltro-musomartle.php?t=225I guess in the pture there is a zoom H2 and beyer-dynam headphoneshomepage - - myspe - virb - berkleemusQuote:Stolen? What more?? has the pre of heroin gone up in Norway rently?! ---------------------------------------------------------"At the evening sermon tonight, the sermon top will be; "What is HELL?".ome early to listen to ourhoir prte". Has the pre of heroin gone up in Norway rently?! This image has been reded to fit the page.lk on it to enlarge.Stolen? What more?? has the pre of heroin gone up in Norw...
index » News... » New ReleasesPlease support our site. If youlk through and buy from our affiliate partners, we earn a smallommission.Calendar Event: Mah 7, dewanatronommus.php?page=hedFree Tibet. Release the Pahen Lama from prison. Let the Dalai Lama return to his home. index » News... » oerts and FestivalsPlease support our site. If youlk through and buy from our affiliate partners, we earn a smallommission.hello all,new to the but have been lurking for a while and found many solutions in my seahes but this one has eluded me.i rently finished my soun...
I don't see any reason yououldn't do that. Might alsoonsider an indator on the output of the S&H - you would get an indation of output leve...
Alsohk out The SimulAnalog Projt.Among other things, you will find the paper Aomplete model of a tube amplifer stage by Thomas Serafini there (a relative of our most distinguished member Seraph, perhaps? )DJ--The mus-dsp mailing list ahives might also yield something useful.DJ-- a relative of our most distinguished member Seraph, perhaps? There is no sin eept stupidity - Oar Wilde+++ Berlin Mitte Institut für Bessere Elektronihe Musik Quky-Quk News +++Fresh Meat & Herr Kandel present: 28th April, 7pmET: New Releases Show with Visuals by JaneSnijdersom berlin-mitte-institut.de20080207280408-webtv-new-releases-show-w-visuals-by-janesnijderomMAYHARTS! May 2008: Berlin Mitte Institut`s Favourite Mjusikbe...
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