Patent Application
20130322645
Computer systems and related technology affect many aspects of society. Indeed, the computer system's ability to process information has transformed the way we live and work. Computer systems now commonly perform a host of tasks (e.g., word processing, scheduling, accounting, etc.) that prior to the advent of the computer system were performed manually. More recently, computer systems have been coupled to one another and to other electronic devices to form both wired and wireless computer networks over which the computer systems and other electronic devices can transfer various forms of data. The data used, manipulated, and transferred by systems and devices may be digital electronic data, may be analog data, may be a digital representation of analog data, or any number of other forms of data. Accordingly, the performance of many computing tasks are distributed across a number of different computer systems and/or a number of different computing environments.
Computer systems are now ubiquitous in music, sound, video, and other digital data applications. Digital computers are used as sound equalizers and in myriad other capacities in the digital storage and manipulation of data (which often represents underlying analog data). In using present technology, data is often lost in cleanup, processing, or manipulation of digital data which represents, for example, sound or video. Further, it is very often difficult to isolate particular parts of digital data (such as sound or video) in order to playback that particular part, removing other extraneous information, or to remove that particular part to produce a subset of the data without that particular part (such as, for example, removing a particular person's voice from an audio track or removing background freeway noise from a cell phone conversation).
Much in the audio world is focused around analyzer, equalizer, and phase technology (e.g., test, cleanup, etc.). There is a need for a mechanism for identifying patterns within an audio source and there is a need for tools which can analyze digital data to isolate particular patterns in order to apply desirable masks and/or filters to digital data.
The present invention extends to methods, systems, and computer program products for analyzing digital data. The digital data analyzed may be, for example, in the form of digital audio files, digital video files, real time audio streams, real time video streams, and the like. The present invention identifies patterns in a source of digital data and uses the identified patterns to analyze, classify, and filter the digital data.
Embodiments of the present invention extend to methods for analyzing digital data for determining related portions within the digital data. Of course, the digital data may be the digital representation of live or recorded analog data such as audio, video, etc. Such methods may be performed in a computer system which includes one or more computer processors and digital data storage. Such methods may include accessing a source of digital data where the digital data comprises at least two dimensions, a time dimension and a data value dimension.
Methods may also include determining a plurality of segments within the digital data where each segment is identified by some common characteristic of the segments. Methods may also include separating out and storing or representing each of the segments in an additional dimension, such as a third or possibly a fourth (or higher) dimension. When each of the segments (or some subset of the segments) is represented in an additional dimension, a resemblance value for each pair of the segments may be determined. Once a resemblance value is determined for each pair of segments, one or more three-dimensional (3D) fingerprints may be generated for each set of segments which share a resemblance value above some determined threshold.
Embodiments of the present invention also extend to methods for filtering and/or masking digital data. Such filtering methods may be performed within a computer system including one or more computer processors and digital data storage. Such filtering methods may include accessing a source of digital data, accessing a fingerprint of one or more segments of the digital data, and filtering and/or masking the digital data using the accessed fingerprint.
Embodiments of the present invention may also extend to computer program products for analyzing digital data. Such computer program products may be intended for executing computer-executable instructions upon computer processors in order to perform methods for analyzing digital data. Such computer program products may comprise computer-readable media which have computer-executable instructions encoded thereon wherein the computer-executable instructions, when executed upon suitable processors within suitable computer environments, perform methods of analyzing digital data as further described herein.
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The features and advantages of the invention may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the present invention will become more fully apparent from the following description and appended claims, or may be learned by the practice of the invention as set forth hereinafter.
In order to describe the manner in which the above-recited and other advantages and features of the invention can be obtained, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings in which:
The present invention extends to methods, systems, and computer program products for analyzing digital data. The digital data analyzed may be, for example, in the form of digital audio files, digital video files, real time audio streams, real time video streams, and the like. The present invention identifies patterns in a source of digital data and uses the identified patterns to analyze, classify, and filter the digital data.
Particular embodiments of the present invention relate to digital audio. Embodiments are designed to perform non-destructive audio isolation and separation from any audio source based on the premise of pattern recognition. By identifying like and not like characteristics, embodiments of the present invention can separate an audio source into simple elements such as notes, progressions, syllables, and various types of either or both desirable characteristics or undesirable characteristics (e.g., “noise”). With separated like (and not like) segments, segments may then be re-joined into an audio track for further processing or may be kept separate as distinct elements which can be independently used, manipulated, and/or reassembled.
By implementing a look back buffer, operations within embodiments of the present invention are able to operate non-destructively. Embodiments analyze digital content retroactively to identify patterns and perform operations. This allows embodiments of the invention to work backwards through a digital data sample (e.g., an audio file) from the most dominant samples to the least dominant samples. Application of embodiments of the present invention enable identifying a sample within an audio file and selectively isolating the sample to its own track.
Embodiments of the present invention extend to methods for analyzing digital data for determining related portions within the digital data. Of course, the digital data may be the digital representation of live or recorded analog data such as audio, video, etc. Such methods may be performed in a computer system which includes one or more computer processors and digital data storage. Such methods may include accessing a source of digital data where the digital data comprises at least two dimensions, a time dimension and a data value dimension.
Methods may also include determining a plurality of segments within the digital data where each segment is identified by some common characteristic of the segments. Methods may also include separating out and storing or representing each of the segments in an additional dimension, such as a third or possibly a fourth (or higher) dimension. When each of the segments (or some subset of the segments) is represented in an additional dimension, a resemblance value for each pair of the segments may be determined. Once a resemblance value is determined for each pair of segments, one or more three-dimensional (3D) fingerprints may be generated for each set of segments which share a resemblance value above some determined threshold.
Embodiments of the present invention also extend to methods for filtering and/or masking digital data. Such filtering methods may be performed within a computer system including one or more computer processors and digital data storage. Such filtering methods may include accessing a source of digital data, accessing a fingerprint of one or more segments of the digital data, and filtering and/or masking the digital data using the accessed fingerprint.
Embodiments of the present invention may also extend to computer program products for analyzing digital data. Such computer program products may be intended for executing computer-executable instructions upon computer processors in order to perform methods for analyzing digital data. Such computer program products may comprise computer-readable media which have computer-executable instructions encoded thereon wherein the computer-executable instructions, when executed upon suitable processors within suitable computer environments, perform methods of analyzing digital data as further described herein.
Embodiments of the present invention may comprise or utilize a special purpose or general-purpose computer including computer hardware, such as, for example, one or more computer processors and data storage or system memory, as discussed in greater detail below. Embodiments within the scope of the present invention also include physical and other computer-readable media for carrying or storing computer-executable instructions and/or data structures. Such computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer system. Computer-readable media that store computer-executable instructions are computer storage media. Computer-readable media that carry computer-executable instructions are transmission media. Thus, by way of example, and not limitation, embodiments of the invention can comprise at least two distinctly different kinds of computer-readable media: computer storage media and transmission media.
Computer storage media includes RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other physical medium which can be used to store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer.
A “network” is defined as one or more data links that enable the transport of electronic data between computer systems and/or modules and/or other electronic devices. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computer, the computer properly views the connection as a transmission medium. Transmission media can include a network and/or data links which can be used to carry or transmit desired program code means in the form of computer-executable instructions and/or data structures which can be received or accessed by a general purpose or special purpose computer. Combinations of the above should also be included within the scope of computer-readable media.
Further, upon reaching various computer system components, program code means in the form of computer-executable instructions or data structures can be transferred automatically from transmission media to computer storage media (or vice versa). For example, computer-executable instructions or data structures received over a network or data link can be buffered in RAM within a network interface module (e.g., a “NIC”), and then eventually transferred to computer system RAM and/or to less volatile computer storage media at a computer system. Thus, it should be understood that computer storage media can be included in computer system components that also (or possibly primarily) make use of transmission media.
Computer-executable instructions comprise, for example, instructions and data which, when executed at a processor, cause a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. The computer executable instructions may be, for example, binaries which may be executed directly upon a processor, intermediate format instructions such as assembly language, or even higher level source code which may require compilation by a compiler targeted toward a particular machine or processor. Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the described features or acts described above. Rather, the described features and acts are disclosed as example forms of implementing the claims.
Those skilled in the art will appreciate that the invention may be practiced in network computing environments with many types of computer system configurations, including, personal computers, desktop computers, laptop computers, message processors, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, pagers, routers, switches, and the like. The invention may also be practiced in distributed system environments where local and remote computer systems, which are linked (either by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links) through a network, both perform tasks. In a distributed system environment, program modules may be located in both local and remote memory storage devices.
Computing system 100 may also contain communication channels 108 that allow the computing system 100 to communicate with other computing systems, devices, or data sources over, for example, a network (such as perhaps the Internet 110). Computing system 100 may also comprise an input device, such as microphone 106, which allows a source of digital or analog data to be accessed. Such digital or analog data may, for example, be audio or video data. Digital or analog data may be in the form of real time streaming data, such as from a live microphone, or may be stored data accessed from data storage 114 which is accessible directly by the computing system 100 or may be more remotely accessed through communication channels 108 or via a network such as the Internet 110.
Communication channels 108 are examples of transmission media. Transmission media typically embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism and include any information-delivery media. By way of example, and not limitation, transmission media include wired media, such as wired networks and direct-wired connections, and wireless media such as acoustic, radio, infrared, and other wireless media. The term “computer-readable media” as used herein includes both computer storage media and transmission media.
Embodiments within the scope of the present invention also include computer-readable media for carrying or having computer-executable instructions or data structures stored thereon. Such physical computer-readable media, termed “computer storage media,” can be any available physical media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable media can comprise physical storage and/or memory media such as RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other physical medium which can be used to store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer.
Computer systems may be connected to one another over (or are part of) a network, such as, for example, a Local Area Network (“LAN”), a Wide Area Network (“WAN”), a Wireless Wide Area Network (“WWAN”), and even the Internet 110. Accordingly, each of the depicted computer systems as well as any other connected computer systems and their components, can create message related data and exchange message related data (e.g., Internet Protocol (“IP”) datagrams and other higher layer protocols that utilize IP datagrams, such as, Transmission Control Protocol (“TCP”), Hypertext Transfer Protocol (“HTTP”), Simple Mail Transfer Protocol (“SMTP”), etc.) over the network.
A method for analyzing digital data may include an act 202 of accessing a source of digital data. The digital data may comprise at least two dimensions, a time dimension and a data value dimension. For example, a source of digital data may be an audio file. Such an audio file may comprise a time dimension and a data value dimension. The time dimension may provide data which identifies at which time in the file a particular sound occurs. The data value dimension may provide the value of a frequency and value of an amplitude for the sound associated with the time of the time dimension.
The source of data may also be a streaming audio file arising from a live source, such as from an input microphone or from an archived digital source such as a .wav or .mpg, etc., file.
As those with skill in the art may recognize, a particular sound at a particular time may comprise a great many frequencies and associated amplitudes which combine in order to provide the rich and complex sounds typically associated with digital audio files. Of course, as one may appreciate, a digital file may be a previously stored file, a representation of a real time analog source, or possibly some other source. Accordingly, although sometimes described in simpler terms, the description herein should be interpreted to include multiple data values associated with particular time values and nothing within this description should be interpreted as limiting the invention to single data values associated with time values within a source of digital data.
Methods may also include determining a plurality of segments within the digital data where each segment is identified by some common characteristic of the segments. Similarities may be found within segments of a source of digital data. Window detection may be based on detecting the start and the end of a single note, a frequency progression, or a continuous data segment within the digital data. The digital data may thusly be segmented into windows where each window (or segment) comprises a time slice of the digital data where all the data within the time slice shares some common characteristic or similarity such as being part of the single note, frequency progression, or continuous data segment.
For example, a portion of an audio file may contain the sound of the strains of a particular violin or of a particular person's voice. The characteristics which identify the violin or voice may be more complex than simply frequency and/or amplitude. For example, a middle C played upon the particular violin may sound completely distinct from a middle C sung by the particular voice. For example, a middle C on a violin compared to a middle C on a clarinet will have distinct visual characteristics shown within a spectrum analyzer as well as determined within the embodiments herein. Such distinctions are distinct in terms of vibrato, hum, resonance and harmonic intensities, for example.
The portion of the audio file which contains the characteristics of the particular violin may be marked by a beginning time and by an ending time in order to identify that portion as a particular segment or window. Such similarities and/or characteristics may be found and used to identify a plurality of segments or windows within the digital data source. For example, a spectrum analyzer may be used to identify a plurality of windows within a digital audio file such that the entire digital audio file is segmented into a plurality of windows or segments, the data (e.g., sound) within each window having a common characteristic which may be used to identify the beginning time and ending time of the window. For example, the window start to window end may be identified based upon a single note or a continuous frequency progression. For example, a single note which begins at time 10 ms in a digital audio file and ends at time 15 ms may be used to identify a window beginning at 10 ms and ending at 15 ms.
Referring to
Once a window's beginning and ending are determined, then the method may include post processing 210 which plots frequencies and detects harmonics 212. At this point it may be determined 214 if multiple frequency progressions are detected. If multiple frequency progressions are detected, a new window start may be set 216. The frequency progression is then followed 218 through its falloff. At the point of the frequency progression falloff, a end to the new window may be set 220. This iteration 222 of splitting the source into windows and/or smaller windows may be repeated until each window beginning and ending has been determined and set.
Once a source of digital data (e.g., a sound file) is segmented into windows, the source (or file) may be broken into one or more (i.e., n) segments, each of which is marked by a particular beginning time and ending time.
Methods encompassed by embodiments of the present invention may also include separating out and storing or representing each of the segments in an additional dimension, such as a third or possibly a fourth (or higher) dimension. For example, the discrete windows in
In turn, the segment in separate window 620 may be analyzed to determine if there are individual characteristics within that segment which may, in turn, warrant additional separation as separate windows. Segments 630, 640, and 650 depict such segments with similar frequency progressions and harmonics of frequency progressions identified by the white line tracings. Each of segments 630, 640, and 650 depict portions of digital data which may be identified from an original data source (or subset of an original data source) as having frequency or harmonic characteristics which may warrant separation into a discrete window (or segment).
It may be noted that the length of a digital data source (e.g., an audio file) and the length of a discrete window identified within a digital data source may vary widely. The length of a data source is dependent upon the underlying data source, itself. The length of a discrete window identified within the data source may vary in size dramatically based upon the length of a particular frequency progression. An audio file which is ten (10) minutes long may be segmented into windows, each of which is no longer than ten (10) milliseconds. The length of discrete windows may, for example, vary between five (5) milliseconds and ten (10) seconds. However, each ten (10) minute segment may be treated as an audio source and further segmented into sub-segments. The length of each sub-segment may vary and, for example, may typically range between ten (10) milliseconds and ten (10) seconds. Further, the present invention allows that windows may be analyzed in a fashion analogous to how an entire file is analyzed. Accordingly, windows may be analyzed iteratively or recursively in order to break down an audio file into finer and finer sub-windows. This is, as described above, illustrated by the iteration 222 depicted in
When each of the segments or windows (or some subset of the segments) are represented in an additional dimension, a resemblance value for each pair of the segments may be determined. A matrix may be created wherein each pair of windows is represented by a resemblance value indicating the resemblance of the particular pair of windows to each other.
For example, segments {S1, S2, . . . Sn} are enumerated along the horizontal axis 510 indicated by the integers 1, 2, . . . N. Segments{S1, S2, . . . Sn} are also enumerated along the vertical axis 520. The resemblance value for two segments, (Si, Sj), is indicated by the value in the intersection of the row and column indicated by the relevant segment numbers. For example, the value corresponding to the pair (S2, S6), depicted in the intersection of row 2 and column 6 has a value of 97 (item 540). As may be noted, each segment (window) has a resemblance of 100 (on a scale of 0-100) with itself. For example, the value corresponding to the pair (S3, S3) has a value of 100. In further examples, segment 1 has resemblance value of 50 with segment 2, a resemblance value of 1 with segment 3, and a resemblance value of 95 with segment 4.
It may be noted that the resemblance values depicted in
In order to determine resemblance values for each pair of windows, a fingerprint of each window may be determined. Such a fingerprint may be determined, for example, by calculating an MD5 checksum of the window data for each window or by calculating an L-Z compression signature of each window data. Of course, as may be appreciated, such a fingerprint for each window may be generated by other means using technology available within the art or by using other, possibly proprietary, technology.
As illustrated in
Once a resemblance value is determined for each pair of segments, producing a value of how “like” each pair of segments is to each other, a three-dimensional (3D) fingerprint may be generated for each set of segments which share a resemblance value above some determined threshold. For example, if segments S2, S3, and S5 all had a resemblance value of 0.95 (on a scale from 0.0 to 1.0), then a 3D fingerprint may be calculated for the set of segments {S2, S3, S5}.
For example, in
The threshold used to determine the requisite level of similarity between individual segments in order to determine sets of segments may be set by a user or operator or may be determined programmatically within the system based upon available matching values, desired granularity, or other pertinent criteria.
Once the matrix (or table) of resemblance values has been determined and recorded, a method may iterate through the matrix and determine sets of matches wherein each set of matches are the pairs of windows which have a resemblance value equal to or greater than a particular threshold. For example, if the resemblance values range from 0 to 100, the threshold value may be 95 and a set of matches may be a set of pairs, each of which resembles the other pairs in the set with a resemblance value of 95 or greater. As may be appreciated, the threshold value may be set by a user preference or may be determined by an algorithm which calculates an appropriate threshold value for a particular application.
Once a set (or sets) of matches is determined, a 3D fingerprint may then be generated for the matching windows. Such 3D fingerprints may, for example, be calculated using time, frequency, and amplitude of the sounds within the (sound) windows. Each window is iterated through and such 3D fingerprints are generated for all windows which have been separated out into an additional dimension and/or generated or refined from previous fingerprints falling within a likeness threshold. (Of course, while useful for digital sound and often described in terms of a sound file, the embodiments described herein should not be construed to be limited to sound or sound data. The embodiments described herein are applicable to myriad forms of digital data which include, for example, sound, video, cardiac function data, stock market price data, etc.)
Once window markers are set (i.e., segments are determined), a preliminary fingerprint may be generated for each detected window to trace out frequency progressions and further refine the detected sample and reduce it down to one window per distinct sample. This process happens via attempting to overlay the tracings of previously detected samples over the new tracing of this distinct sample. An overlay is considered a match when one can be stretched in any of the three axes so that it can properly describe the contents contained within the tracing. If a match cannot be found, the tracings within a detected window are then stored and declared as their own distinct source/sample.
Now an iteration is performed 256 through the hash tables which averages 252 the data points between the 3D waveforms. New 3D waveform fingerprints may then be generated 254 using the computed averages. Each of the newly generated 3D waveform fingerprints may be stored 258 in an index.
Once such 3D fingerprints are generated for each window, overlapping data points within the fingerprints are averaged together to produce an arithmetic mean of the fingerprints. This arithmetic mean can be used to describe the capabilities of this particular source of digital data.
Embodiments of the present invention also extend to methods for filtering digital data. Such filtering methods may be performed within a computer system including one or more processors and data storage. Such filtering methods may include accessing a source of digital data, accessing a fingerprint of one or more segments of the digital data, and filtering the digital data using the accessed fingerprint.
Once the fingerprints are determined in this fashion, it may then be possible to perform bit-mask operations upon the original source of digital data. For example, a bit-mask subtraction may be performed in order to remove all instances of a particular person's voice from an audio track. In another example, a bit-mask AND may be performed during playback in order to only play back the sound of a particular violin (and remove all other sounds and/or voices).
Embodiments of the present invention may also extend to computer program products for analyzing digital data. Such computer program products may be intended for executing computer-executable instructions upon computer processors in order to perform methods for analyzing digital data. Such computer program products may comprise computer-readable media which have computer-executable instructions encoded thereon wherein the computer-executable instructions, when executed upon suitable processors within suitable computer environments, perform methods of analyzing digital data as further described herein.
The present invention may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the invention is, therefore, indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.
This application claims the benefit under 35 U.S.C. §1.20 to, and is a continuation of, U.S. Pat. No. 8,462,984, issued Jun. 11, 2013 and titled “DATA PATTERN RECOGNITION AND SEPARATION ENGINE”, which patent is expressly incorporated herein by this reference.
| Number | Date | Country | |
|---|---|---|---|
| Parent | 13039554 | Mar 2011 | US |
| Child | 13915443 | US |
