Patent Grant
7240207
This application is related to co-pending application entitled “Audio Fingerprinting,” U.S. application Ser. No. 11/177,083, filed on Jul. 8, 2005.
The names of actual recording artist mentioned herein may be the trademarks of their respective owners. No association with any recording artist is intended or should be inferred.
The present invention relates to a system and method for creating, managing, and processing fingerprints for media data.
Classifying information that has subjectively perceived attributes or characteristics is difficult. When the information is one or more musical compositions, classification is complicated by the widely varying subjective perceptions of the musical compositions by different listeners. One listener may perceive a particular musical composition as “hauntingly beautiful” whereas another may perceive the same composition as “annoyingly twangy.”
In the classical music context, musicologists have developed names for various attributes of musical compositions. Terms such as adagio, fortissimo, or allegro broadly describe the strength with which instruments in an orchestra should be played to properly render a musical composition from sheet music. In the popular music context, there is less agreement upon proper terminology. Composers indicate how to render their musical compositions with annotations such as brightly, softly, etc., but there is no consistent, concise, agreed-upon system for such annotations.
As a result of rapid movement of musical recordings from sheet music to pre-recorded analog media to digital storage and retrieval technologies, this problem has become acute. In particular, as large libraries of digital musical recordings have become available through global computer networks, a need has developed to classify individual musical compositions in a quantitative manner based on highly subjective features, in order to facilitate rapid search and retrieval of large collections of compositions.
Musical compositions and other information are now widely available for sampling and purchase over global computer networks through online merchants such as Amazon.com, Inc., barnesandnoble.com, cdnow.com, etc. A prospective consumer can use a computer system equipped with a standard Web browser to contact an online merchant, browse an online catalog of pre-recorded music, select a song or collection of songs (“album”), and purchase the song or album for shipment direct to the consumer. In this context, online merchants and others desire to assist the consumer in making a purchase selection and desire to suggest possible selections for purchase. However, current classification systems and search and retrieval systems are inadequate for these tasks.
A variety of inadequate classification and search approaches are now used. In one approach, a consumer selects a musical composition for listening or for purchase based on past positive experience with the same artist or with similar music. This approach has a significant disadvantage in that it involves guessing because the consumer has no familiarity with the musical composition that is selected.
In another approach, a merchant classifies musical compositions into broad categories or genres. The disadvantage of this approach is that typically the genres are too broad. For example, a wide variety of qualitatively different albums and songs may be classified in the genre of “Popular Music” or “Rock and Roll.”
In still another approach, an online merchant presents a search page to a client associated with the consumer. The merchant receives selection criteria from the client for use in searching the merchant's catalog or database of available music. Normally the selection criteria are limited to song name, album title, or artist name. The merchant searches the database based on the selection criteria and returns a list of matching results to the client. The client selects one item in the list and receives further, detailed information about that item. The merchant also creates and returns one or more critics' reviews, customer reviews, or past purchase information associated with the item.
For example, the merchant may present a review by a music critic of a magazine that critiques the album selected by the client. The merchant may also present informal reviews of the album that have been previously entered into the system by other consumers. Further, the merchant may present suggestions of related music based on prior purchases of others. For example, in the approach of Amazon.com, when a client requests detailed information about a particular album or song, the system displays information stating, “People who bought this album also bought . . . ” followed by a list of other albums or songs. The list of other albums or songs is derived from actual purchase experience of the system. This is called “collaborative filtering.”
However, this approach has a significant disadvantage, namely that the suggested albums or songs are based on extrinsic similarity as indicated by purchase decisions of others, rather than based upon objective similarity of intrinsic attributes of a requested album or song and the suggested albums or songs. A decision by another consumer to purchase two albums at the same time does not indicate that the two albums are objectively similar or even that the consumer liked both. For example, the consumer might have bought one for the consumer and the second for a third party having greatly differing subjective taste than the consumer. As a result, some pundits have termed the prior approach as the “greater fools” approach because it relies on the judgment of others.
Another disadvantage of collaborative filtering is that output data is normally available only for complete albums and not for individual songs. Thus, a first album that the consumer likes may be broadly similar to second album, but the second album may contain individual songs that are strikingly dissimilar from the first album, and the consumer has no way to detect or act on such dissimilarity.
Still another disadvantage of collaborative filtering is that it requires a large mass of historical data in order to provide useful search results. The search results indicating what others bought are only useful after a large number of transactions, so that meaningful patterns and meaningful similarity emerge. Moreover, early transactions tend to over-influence later buyers, and popular titles tend to self-perpetuate.
In a related approach, the merchant may present information describing a song or an album that is prepared and distributed by the recording artist, a record label, or other entities that are commercially associated with the recording. A disadvantage of this information is that it may be biased, it may deliberately mischaracterize the recording in the hope of increasing its sales, and it is normally based on inconsistent terms and meanings.
In still another approach, digital signal processing (DSP) analysis is used to try to match characteristics from song to song, but DSP analysis alone has proven to be insufficient for classification purposes. While DSP analysis may be effective for some groups or classes of songs, it is ineffective for others, and there has so far been no technique for determining what makes the technique effective for some music and not others. Specifically, such acoustical analysis as has been implemented thus far suffers defects because 1) the effectiveness of the analysis is being questioned regarding the accuracy of the results, thus diminishing the perceived quality by the user and 2) recommendations can only be made if the user manually types in a desired artist or song title from that specific website. Accordingly, DSP analysis, by itself, is unreliable and thus insufficient for widespread commercial or other use.
With the explosion of media entity data distribution (e.g. online music content), comes an increase in the demand by media authors and publishers to authenticate the media entities to be authorized, and not illegal copies of an original work such to place the media entity outside of copyright violation inquires. Concurrent with the need to combat epidemic copyright violations, there exists a need to readily and reliably identify media entity data so that accurate metadata can be associated to media entity data to offer descriptions for the underlying media entity data. Metadata available for a given media entity can include artist, album, song, information, as well as genre, tempo, lyrics, etc. The underlying computing environment can provide additional obstacles in the creation and distribution of such accurate metadata. For example, peer-to-peer networks exasperate the problem by propagating invalid metadata along with the media entity data. The task of generating accurate and reliable metadata is made difficult by the numerous forms and compression rates that media entity data may reside and be communicated (e.g. PCM, MP3, and WMA). Media entity can be further altered by the multiple trans-coding processes that are applied to media entity data. Currently, simple hash algorithms are employed in processes to identify and distinguish media entity data. These hashing algorithms are not practical and prove to be cumbersome given the number of digitally unique ways a piece of music can be encoded.
Accordingly there is a need for improved methods of accurately recognizing media content so that content may be readily and reliably authorized to satisfy copyright regulations and also so that a trusted source of metadata can be utilized. Generally, metadata is embedded data that is employed to identify, authorize, validate, authenticate, and distinguish media entity data. The identification of media entity data can be realized by employing classification techniques described above to categorize the media entity according to its inherent characteristics (e.g. for a song to classify the song according to the song's tempo, consonance, genre, etc.). Once classified, the present invention exploits the classification attributes to generate a unique fingerprint (e.g. a unique identifier that can be calculated on the fly) for a given media entity. Further, fingerprinting media is an extremely effective tool to authenticate and identify authorized media entity copies since copying, trans-coding, or reformating media entities will riot adversely affect the fingerprint of said entity. In the context of metadata, by using the inventive concepts of fingerprinting found in the present invention, metadata can more easily, efficiently, and more reliably be associated to one or more media entities. It would be desirable to provide a system and methods as a result of which participating users are offered identifiable media entities based upon users' input. It would be still further desirable to aggregate a range of media objects of varying types and the metadata thereof, or categories using various categorization and prioritization methods in connection with media fingerprinting techniques in an effort to satisfy copyright regulations and to offer reliable metadata.
In view of the foregoing, the present invention provides a system and methods for creating, managing, and authenticating fingerprints for media used to identify, validate, distinguish, and categorize, media data. In connection with a system that convergently merges perceptual and digital signal processing analysis of media entities for purposes of classifying the media entities, the present invention provides various means to aggregate a range of media objects and meta-data thereof according to unique fingerprints that are associated with the media objects. The fingerprinting of media contemplates the use of one or more fingerprinting algorithms to quantify samples of media entities. The quantified samples are employed to authenticate and/or identify media entities in the context of media entity distribution platform.
Other features of the present invention are described below.
The system and methods for the creation, management, and authentication of media fingerprinting are further described with reference to the accompanying drawings in which:
Overview
The proliferation of media entity distribution (e.g. online music distribution) has lead to the explosion of what some have construed as rampant copyright violations. Copyright violations of media may be averted if the media object in question is readily authenticated to be deemed an authorized copy. The present invention provides systems and methods that enable the verification of the identity of an audio recording that allows for the determination of copyright verification. The present invention contemplates the use of minimal processing power to verify the identification of media entities. In an illustrative implementation, the media entity data can be created from a digital transfer of data from a compact disc recording or from an analog to digital conversion process from a CD or other analog audio medium.
The methods of the present invention is robust in determining the identity of a file that might have been compressed using one of the readily available of future developed compression formats. Unlike, conventional data identification techniques such as digital watermarking, the system and methods of the present invention do not require that a signal be embedded into the media entity data.
Exemplary Computer and Network Environments
One of ordinary skill in the art can appreciate that a computer 110 or other client device can be deployed as part of a computer network. In this regard, the present invention pertains to any computer system having any number of memory or storage units, and any number of applications and processes occurring across any number of storage units or volumes. The present invention may apply to an environment with server computers and client computers deployed in a network environment, having remote or local storage. The present invention may also apply to a standalone computing device, having access to appropriate classification data.
Classification
In accordance with one aspect of the present invention, a unique classification is implemented which combines human and machine classification techniques in a convergent manner, from which a canonical set of rules for classifying music may be developed, and from which a database, or other storage element, may be filled with classified songs. With such techniques and rules, radio stations, studios and/or anyone else with an interest in classifying music can classify new music. With such a database, music association may be implemented in real time, so that playlists or lists of related (or unrelated if the case requires) media entities may be generated. Playlists may be generated, for example, from a single song and/or a user preference profile in accordance with an appropriate analysis and matching algorithm performed on the data store of the database. Nearest neighbor and/or other matching algorithms may be utilized to locate songs that are similar to the single song and/or are suited to the user profile.
Before, after or at the same time as the human classification process, the songs from database 300 are classified according to digital signal processing (DSP) techniques at 340. Exemplary classifications for songs include, inter alia, tempo, sonic, melodic movement and musical consonance characterizations. Classifications for other types of media, such as video or software are also contemplated. The quantitative machine classifications and qualitative human classifications for a given piece of media, such as a song, are then placed into what is referred to herein as a classification chain, which may be an array or other list of vectors, wherein each vector contains the machine and human classification attributes assigned to the piece of media. Machine learning classification module 350 marries the classifications made by humans and the classifications made by machines, and in particular, creates a rule when a trend meets certain criteria. For example, if songs with heavy activity in the frequency spectrum at 3 kHz, as determined by the DSP processing, are also characterized as ‘jazzy’ by humans, a rule can be created to this effect. The rule would be, for example: songs with heavy activity at 3 kHz are jazzy. Thus, when enough data yields a rule, machine learning classification module 350 outputs a rule to rule set 360. While this example alone may be an oversimplification, since music patterns are considerably more complex, it can be appreciated that certain DSP analyses correlate well to human analyses.
However, once a rule is created, it is not considered a generalized rule. The rule is then tested against like pieces of media, such as song(s), in the database 370. If the rule works for the generalization song(s) 370, the rule is considered generalized. The rule is then subjected to groover scrutiny 380 to determine if it is an accurate rule at 385. If the rule is inaccurate according to groover scrutiny, the rule is adjusted. If the rule is considered to be accurate, then the rule is kept as a relational rule e.g., that may classify new media.
The above-described technique thus maps a pre-defined parameter space to a psychoacoustic perceptual space defined by musical experts. This mapping enables content-based searching of media, which in part enables the automatic transmission of high affinity media content, as described below.
Fingerprinting Overview
Central to the processing is the fact that every perceptually unique media entity data file, possesses a unique set of perceptually relevant attributes that humans use to distinguish between perceptually distinct media entities (e.g. different attributes for music). A representation of these attributes, referred to hereafter as the fingerprint, are extracted by the present invention from the media entity data file with the use of digital audio signal processing (DSP) techniques. These perceptually relevant attributes are then employed by the current method to distinguish between recordings. The perceptually relevant attributes may be classified and analyzed in accordance with the exemplary media entity classification and analysis system described above.
The set of attributes that constitute the fingerprint may consist of the following elements:
In operation, the average information density is taken to be the average entropy per processing frame where a processing frame is taken to be a number of media entity data file (e.g. in the example provided by
where bn is the absolute value of the nth binary of the L1 normalized spectral bands of the processing frame and where log2(.) is the log base two function. The average entropy for a given segment of the media entity data file, S can then be expressed as:
where N is the total number of processing frames.
Comparatively, the spectral bands are calculated by taking the real FFT of each processing frame, dividing the data into separate spectral bands and squaring the sum of the bins in each band. The average of the bands for a given segment of the media entity data file, {right arrow over (C)}, may be expressed as:
where {right arrow over (C)}j is a vector of values consisting of the critical band energy in each critical band.
In order to efficiently compare fingerprints it is advantageous to represent the fingerprint of a media entity as a bit sequence so as to allow efficient bit-to-bit comparisons between fingerprints. The Hamming distance, i.e., the number of bits by which two fingerprints differ, is employed as the metric of distance. In order to convert the calculated perceptual attributes described above to a format suitable for bit-to-bit comparisons, a quantization technique, as described in the preferred embodiment given below, is employed.
In operation, and as shown in
Each column of F corresponds to a chunk, which in turn, represents a slice in time. Each row in F corresponds to a single frequency band in the Mel frequency scale. F is passed to the average stage where the average of each row is calculated and stored in the vector F. In addition the average for a subset of the elements in each row is calculated and placed in the vector S. F−S is placed in the vector D.
Subsequently, each element in D is then set to 1 if that element is greater than zero and 0 if the element is equal to or less than zero in the quantization stage at block 520. For each read, forty bits of data are generated representing the quantized bits of D. Each read typically consists of a few seconds of data. A usable fingerprint is constructed from reads at several positions in the file. Further, once a large number of fingerprints have been calculated, they can be stored in a data store cooperating with an exemplary music classification and distribution system (as described above).
As shown in
In order to quantify the performance of the present invention it is useful to consider two random bit sequences. For example, consider two random bit sequences x, and y, each of length N, where the probability of each bit-value being equal to 1 is 0.5. Alternately, one can consider the generation of the bit sequences as representing the outcomes of the toss of an evenly balanced coin, with results of heads represented as a 1 and tails representing 0. With these conditions met, the probability that bit “n” in x equals bit “n” in y equals 0.5, i.e.,
P(x(n)=y(n))=0.5. (1)
The probability that x and y differ by M bits is, in the limit of large N (the results are reasonable for N>100), given approximately by the Normal distribution:
P(M)=e−(M−N/2)
where σ is the standard deviation of the distribution given by
σ=√{square root over (N1/2)}, (3)
M is known as the Hamming Distance between x and y.
The following equation (i.e. Equation 4) estimates that the probability that the hamming distance between two sequences of random bits is less than some value M′,
Stated differently, Equation 4 gives the odds that two random sequence will fall within a certain distance, M′ of each other.
In operation, Equation 4 may be used as an estimator for one aspect of the performance of the exemplary fingerprint algorithm. For example, now the two sequences x and y represent fingerprints from two separate files. Accordingly, M′ now represents the threshold below which fingerprints are considered to be from the same file. Equation (4) then gives the probability of a “false positive” result. In other words, the results of Equation (4) describes that the probability that two sequences, which do not represent the same file would have a mutual hamming distance less than M′. The above assumes that the fingerprint algorithm behaves as the ideal fingerprinting algorithm, i.e., it yields statistically uncorrelated bit sequences for two files that are not from the same original file.
Ideally, when two media entity data files are derived from the same original file, for instance, ripped from the same song on a CD then stored in two different compression formats, then the Hamming distance between the fingerprints for these two files is zero in the ideal case. This is regardless of compression format of any processing performed on the files that does not destroy or distort the perceived identity of the sound files. In this case, the probability of a false positive result is given exactly by
P(M=0)=1/2N. (5)
In reality, the exemplary fingerprinting algorithm offers a balance between the ideal properties of an ideal fingerprinting algorithm. Namely a balance is struck between the property that unrelated songs are statistically uncorrelated and that two files derived from the same master file should have a Hamming distance of zero (0). The present invention contemplates the use of an exemplary fingerprinting algorithm that offers a balance between the above named fingerprinting properties. This balance is important as it allows some flexibility in the identification of songs. For instance, both the identity as well as the quality of a media entity can be estimated by its distance from a given source media entity by measuring the distance between the two entities.
In the contemplated implementation, the fingerprinting algorithm uses a fingerprint length of 320 bytes. In addition, each fingerprint is assigned a four-byte fingerprint ID. The fingerprint data store may be indexed by fingerprint ID (e.g. a special 12 byte hash index), and by the length (e.g. in seconds), of each file assigned to a given fingerprint. This brings the total fingerprint memory requirement to 338 bytes.
Generally, access time is crucial in data store (e.g. database) applications. For that reason, the fingerprint hash index may be implemented. Specifically, each bit of the hash value corresponds to the weight of 32 bits in the fingerprint. The weight of a sequence of bits is simply the number of bits that are 1 in that sequence. When comparing two fingerprints, their hash distances are first calculated. If that distance is greater than a set value, determined by the cutoff value for the search, then it is safe to assume that the two fingerprints do not match and a further calculation of the fingerprint distance is not required. Correspondingly, if the hash distance is below a predefined limit, then it is possible that the two fingerprints could be a match so the total fingerprint distance is calculated. Using this technique, the search time for matching fingerprints is significantly reduced (e.g. by up to three orders of magnitude). For example, using the fingerprint hash index, estimates for search times on a database of one million songs for matching fingerprints are in the range of 0.2 to 0.5 seconds, depending of the degree of confidence required for the results. The higher the confidence required, the less the search time, as the search space can be more aggressively pruned. This time represents queries made directly to the fingerprint data store from an exemplary resident computer hosting the fingerprint data store. The advantages of the present invention are also realized in networked computer environments where processing times are significantly reduced.
The performance of the alternative exemplary fingerprint algorithm may be broken up into two categories: False Positive (FP) and False Negative (FN). A FP result occurs when a fingerprint is mistakenly classified as a match to another fingerprint. If a FP result occurs false metadata could be returned to the user or alternatively an unauthorized copy of a media entity may be validated to be an authorized copy. A FN result occurs when the system fails to recognize that two fingerprints match. As a result, a user might not receive the desired metadata or be precluded from obtaining desired media entities as they are deemed to stand in violation of copyright violations.
The FP performance of the exemplary fingerprint algorithm can be compared to that of the above-described ideal fingerprint algorithm. As stated, the probability of two fingerprints from the ideal fingerprint system having a distance of M or less is given by Equation 4. Equation 4 may be used as a guide for measuring the performance of the fingerprint algorithm by comparing a measured distribution of inter-fingerprint distances to the distribution for the ideal fingerprint system. The resultant measurement is the Normal distribution.
For example, and as shown by graph 700 in
The performance below a normalized hamming distance of 0.35 as demarcated by region 730 of
In the context of music media entity data files, some correlation is expected even for music media entity data files that come from completely different sources, i.e., a first music media entity data file might be from a David Bowie album and another might come from an Art Of Noise CD. However, both pieces are likely to have some common elements such as rhythm, melody, harmony, etc. A goal of the exemplary fingerprint algorithm during processing is to transition from correlated signals to decorrelated “noise” as a function of distance quickly enough to avoid a FP result, but gradually enough to still recognize two fingerprints as similar even if one fingerprint has come from a media entity data file that has undergone significant manipulation, thereby preventing a FN result. A benchmark for the exemplary fingerprint algorithm is the human ear. That is, both the exemplary fingerprint algorithm and the human ear are to recognize two files originate from the same song.
A FN occurs when two files, which originate from the same file are not recognized as the same file. To estimate the frequency of FN's transcoding effects on fingerprints are analyzed. For example, several media entity data files are encoded at multiple rates and compression formats, including wave files, which consist of raw PCM data, WMA files compressed at 128 KB/sec and MP3 files compressed at 64 KB/sec. The results of the analysis showed that the mean normalized distance for these pairs was 0.0251 with a standard deviation of 0.0225. The cutoff for identification is 0.15. Assuming a Normal distribution of transcoding distances, the odds of a false negative under this scenario are about 1 in 1 million. The similarity cutoff is at 0.2. The odds of the transcoded files not being recognized as similar are 1 in 10−12. Thus, the alternative exemplary fingerprint algorithm is robust to transcoding.
As mentioned above, the media contemplated by the present invention in all of its various embodiments is not limited to music or songs, but rather the invention applies to any media to which a classification technique may be applied that merges perceptual (human) analysis with acoustic (DSP) analysis for increased accuracy in classification and matching.
As mentioned, to determine the identity of a song, the fingerprint of an unknown song is compared to a database of previously calculated fingerprints. The comparison is performed by determining the distance between the unknown fingerprint and all of the previously calculated fingerprints. The distance between the input fingerprint and an entry in the fingerprint database can be expressed as:
d=(
where V is the unknown input fingerprint vector, D is a pre-calculated fingerprint vector in the fingerprint database, M is the scaling matrix, and t is the transpose operator. If d is below a certain threshold, typically chosen to be less than half the distance between a fingerprint database vector and its nearest neighbor, then the song is identified.
M is chosen so that the distribution of fingerprint nearest neighbors in the stored database of fingerprints is as close to a homogeneous distribution as possible. This can be accomplished by choosing M so that the standard deviation of the fingerprint nearest neighbors distribution is minimized. If this value is zero then all elements are separated from their nearest neighbor by the same amount. By minimizing the nearest neighbor standard deviation, the probability that two or more songs will have fingerprints that are so close that they will be mistaken for the same song is reduced. This can be accomplished using standard optimization techniques such as conjugate gradient, etc.
Further, the confidence in the verification or denial of the identity claim depends on the distance between the external fingerprint and the fingerprint of the media entity data file in the database to which the external file is making a claim. If the distance is significantly less than the average nearest neighbor distance between entries in the fingerprint database then the claim can be accepted with an extremely high degree of confidence.
In addition, the present invention is well suited to solving the current problem of copyright protection faced by many online media entity distribution services. For instance, an online media entity distribution service could use the technique to determine the identity of a media entity data file that it had acquired via unsecured means for distribution to users. Once the identity of the recording is made, the service could then determine if it is legal to distribute the digital audio file to its users. This process is better described by
The various techniques described herein may be implemented with hardware or software or, where appropriate, with a combination of both. Thus, the methods and apparatus of the present invention, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium, wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the invention. In the case of program code execution on programmable computers, the computer will generally include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. One or more programs are preferably implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language, and combined with hardware implementations.
The methods and apparatus of the present invention may also be embodied in the form of program code that is transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via any other form of transmission, wherein, when the program code is received and loaded into and executed by a machine, such as an EPROM, a gate array, a programmable logic device (PLD), a client computer, a video recorder or the like, the machine becomes an apparatus for practicing the invention. When implemented on a general-purpose processor, the program code combines with the processor to provide a unique apparatus that operates to perform the indexing functionality of the present invention. For example, the storage techniques used in connection with the present invention may invariably be a combination of hardware and software.
While the present invention has been described in connection with the preferred embodiments of the various figures, it is to be understood that other similar embodiments may be used or modifications and additions may be made to the described embodiment for performing the same function of the present invention without deviating there from. For example, while exemplary embodiments of the invention are described in the context of music data, one skilled in the art will recognize that the present invention is not limited to the music, and that the methods of tailoring media to a user, as described in the present application may apply to any computing device or environment, such as a gaming console, handheld computer, portable computer, etc., whether wired or wireless, and may be applied to any number of such computing devices connected via a communications network, and interacting across the network. Furthermore, it should be emphasized that a variety of computer platforms, including handheld device operating systems and other application specific operating systems are contemplated, especially as the number of wireless networked devices continues to proliferate. Therefore, the present invention should not be limited to any single embodiment, but rather construed in breadth and scope in accordance with the appended claims.
This application is a continuation of U.S. application Ser. No. 09/928,004 now U.S. Pat. No. 6,963,975, filed Aug. 10, 2001, which claims the benefit of U.S. Provisional Application No. 60/224,841, filed Aug. 11, 2000, which is hereby incorporated by reference in its entirety.
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