Source: https://patents.google.com/patent/US9326044B2/en
Timestamp: 2019-04-22 05:48:44+00:00

Document:
Signaturing methods and apparatus as described. An example method includes applying a first function to a portion of digital data to produce a first windowed block, applying a second function different from the first function to the same portion of the digital data to produce a second windowed block, determining a first characteristic of a band of frequencies in the first windowed block, determining a second characteristic of the band of frequencies in the second windowed block, comparing the first characteristic to the second characteristic, and assigning a signature bit representative of the portion of the digital data based on the comparison of the first characteristic and the second characteristic.
This patent arises from a continuation of U.S. patent application Ser. No. 12/266,380, filed Nov. 6, 2008, (now U.S. Pat. No. 8,600,531) and claims the benefit of U.S. Provisional Patent Application 61/033,992, filed Mar. 5, 2008. U.S. Pat. No. 8,600,531 and U.S. Provisional Patent Application 61/033,992 are hereby incorporated by reference in their entireties.
The present disclosure relates generally to media monitoring, multimedia content search and retrieval and, more particularly, to methods and apparatus for generating signatures for use in identifying media information.
Identifying media information and, more specifically, audio signals (e.g., information in audio streams) using signature matching techniques is well established. Signatures are also equivalently known, and frequently referred to, as fingerprints. Signature matching techniques are often used in television and radio audience metering applications and are implemented using several methods for generating and matching signatures. For example, in television audience metering applications, signatures are generated at monitoring sites (e.g., monitored households) and reference sites. Monitoring sites typically include locations such as, for example, households where the media consumption of audience members is monitored. For example, at a monitoring site, monitored signatures may be generated based on audio streams associated with a selected channel, radio station, etc. The monitored signatures may then be sent to a central data collection facility for analysis. At a reference site, signatures, typically referred to as reference signatures, are generated based on known programs that are provided within a broadcast region. The reference signatures may be stored at the reference site and/or a central data collection facility and compared with monitored signatures generated at monitoring sites. A monitored signature may be found to match with a reference signature and the known program corresponding to the matching reference signature may be identified as the program that was presented at the monitoring site.
FIGS. 1A and 1B illustrate example audio stream identification systems for generating signatures and identifying audio streams.
FIG. 2 is a flow diagram illustrating an example signature generation process.
FIG. 3 is a time-domain representation of an example monitored audio stream.
FIG. 4 is a plot of an example of a portion of the monitored audio stream (i.e., an audio block) that is a sinusoid.
FIG. 5 is a plot of an example window that may be applied to the audio block of FIG. 4.
FIG. 6 is a plot of a windowed audio block resulting from an application of the window of FIG. 5 to the audio block of FIG. 4.
FIG. 7 is a plot of a second example window that may be applied to the audio block of FIG. 4.
FIG. 8 is a plot of a windowed audio block resulting from an application of the window of FIG. 7 to the audio block of FIG. 4.
FIG. 9 is a plot of the window of FIG. 5 showing the corresponding frequency response of the window.
FIG. 10 is a plot of the window of FIG. 7 showing the corresponding frequency response of the window.
FIG. 11A is a plot of a second alternate example window.
FIG. 11B is a plot of a corresponding frequency response of the window of FIG. 11A.
FIG. 12A is a plot of a third alternate example window.
FIG. 12B is a plot of a corresponding frequency response of the window of FIG. 12A.
FIG. 13 is a flow diagram of an example signature matching process.
FIG. 14 is a diagram showing how signatures may be compared in accordance with the flow diagram of FIG. 13.
FIG. 15 is a block diagram of an example signature generation system for generating signatures based on audio streams or audio blocks.
FIG. 16 is a block diagram of an example signature comparison system for comparing signatures.
FIG. 17 is a block diagram of an example processor system that may be used to implement the methods and apparatus described herein.
Although the following discloses example systems implemented using, among other components, software executed on hardware, it should be noted that such systems are merely illustrative and should not be considered as limiting. For example, it is contemplated that any or all of these hardware and software components could be embodied exclusively in hardware, exclusively in software, or in any combination of hardware and software. Accordingly, while the following describes example systems, persons of ordinary skill in the art will readily appreciate that the examples provided are not the only way to implement such systems.
The methods and apparatus described herein generally relate to generating digital signatures that may be used to identify media information. A digital signature, or digital fingerprint, is a signal descriptor that accurately characterizes signals for the purpose of matching, indexing, or database search and retrieval. In particular, the disclosed methods and apparatus are described with respect to generating digital audio signatures based on audio streams or audio blocks (e.g., audio information). However, the methods and apparatus described herein may also be used to generate digital signatures based on any other type of signals, time series data, and media information such as, for example, video information, web pages, still images, computer data, etc. Further, the media information may be associated with broadcast information (e.g., television information, radio information, etc.), information reproduced from any storage medium (e.g., compact discs (CD), digital versatile discs (DVD), etc.), or any other information that is associated with an audio stream, a video stream, or any other media information for which the digital signatures are generated. In one particular example, the audio streams are identified based on digital signatures including monitored digital signatures generated at a monitoring site (e.g., a monitored household) and reference digital signatures generated and/or stored at a reference site and/or a central data collection facility.
As described in detail below, the methods and apparatus described herein identify media information, including audio streams or any other media, based on digital signatures. The example techniques described herein compute a signature at a particular time using, for example, a single audio block of audio samples, but processes the audio block using two or more windowing functions to result in two or more windowed audio blocks. Further processing of the windowed audio blocks allows detection of the windowing effects on the audio spectrum of the audio block. The signature values unique or substantially unique to the audio block are derived from the effects of the two or more windowing functions on the audio block. That is, the example techniques described herein enable computation or determination of audio signatures without the use of time displaced audio blocks. Of course, the selection of the windowing functions may be adjusted, as may be the type of transformations, their parameters, and/or resolutions used to determine signatures.
As described below in detail, after application of the windowing functions to the block of audio samples, frequency components of the windowed audio blocks are generated by transforming the windowed audio blocks from the time domain to the frequency domain using, for example, a discrete Fourier transformation (DFT) or any other suitable transform (e.g., discrete cosine transform (DCT), modified discrete cosine transform (MDCT), Haar transform, Walsh transform, etc.), be it based on a Fourier Transform or not. The transform can be used to analyze the frequency components in the windowed audio blocks and identify the spectral power of each frequency component. The spectral powers may then be used to generate digital signatures.
Other techniques may be used after application of the window functions to the audio blocks. For example, the windowed audio blocks may be processed using wavelet transforms that transform audio data from the time domain to the wavelet domain. In general, wavelet transforms may be used to decompose blocks or frames of data (e.g., time domain audio samples) into multiple sub-bands, thereby allowing data sets to be analyzed at various scales and/or resolutions. By separating data into multiple sub-bands, a wavelet transform may be used to analyze each time interval of data at a desired scale or resolution.
Alternatively, rather the applying window functions in the time domain to time domain blocks of audio samples, the windowing could be done in the frequency domain, wherein a frequency response corresponding to a time domain window may be convolved with the frequency spectrum of an audio block. If frequency domain processing including a convolution is used, a conversion of the audio block to the frequency domain may be carried out using a Fourier transformation, wherein adjustments are made between audio blocks to account for discontinuity. Additionally, if the processing and application of the windows are done in the frequency domain, a time domain window having a frequency characteristic with a number of non-zero elements (e.g., 3-5 non-zero elements) may be selected.
Monitored signatures may be generated using the above techniques at a monitoring site based on audio streams associated with media information (e.g., a monitored audio stream) that is consumed by an audience or to which an audience is exposed. For example, a monitored signature may be generated based on the audio track of a television program or any other media presented at a monitoring site. The monitored signature may then be communicated to a central data collection facility for comparison to one or more reference signatures.
Reference signatures are generated at a reference site and/or a central data collection facility using the above techniques on audio streams associated with known media information. The known media information may include media that is broadcast within a region, media that is reproduced within a household, media that is received via the Internet, etc. Each reference signature is stored in a memory with media identification information such as, for example, a song title, a movie title, etc. When a monitored signature is received at the central data collection facility, the monitored signature is compared with one or more reference signatures until a match is found. This match information may then be used to identify the media information (e.g., monitored audio stream) from which the monitored signature was generated. For example, a look-up table or a database may be referenced to retrieve a media title, a program identity, an episode number, etc. that corresponds to the media information from which the monitored signature was generated.
In one example, the rates at which monitored signatures and reference signatures are generated may be different. For example, for processing and other concerns, a monitored signature may be 25% of the data rate of a reference signature. For example, a 48-bit reference signature may be generated every 0.032 seconds, which results in a reference data rate of 48 bits*31.25/seconds or 187.5 bytes/second. In such an arrangement, a 48-bit monitored signature may be generated every 0.128 seconds, which results in a monitored data rate of 48 bits*7.8125/seconds or 46.875 bytes/second. Of course, in an arrangement in which the data rates of the monitored and reference signatures differ, this difference must be accounted for when comparing monitored signatures with reference signatures. For example, if the monitoring rate is 25% of the reference rate, each consecutive monitored signature will correspond to every fourth reference signature.
The plurality of media delivery devices 110 may include, for example, set top box tuners (e.g., cable tuners, satellite tuners, etc.), PVR devices, DVD players, CD players, radios, etc. Some or all of the media delivery devices 110 such as, for example, set top box tuners may be communicatively coupled to one or more broadcast information reception devices 116, which may include a cable, a satellite dish, an antenna, and/or any other suitable device for receiving broadcast information. The media delivery devices 110 may be configured to reproduce media information (e.g., audio information, video information, web pages, still images, etc.) based on, for example, broadcast information and/or stored information. Broadcast information may be obtained from the broadcast information reception devices 116 and stored information may be obtained from any information storage medium (e.g., a DVD, a CD, a tape, etc.). The media delivery devices 110 are communicatively coupled to the media presentation devices 112 and configurable to communicate media information to the media presentation devices 112 for presentation. The media presentation devices 112 may include televisions having a display device and/or a set of speakers by which audience members consume, for example, broadcast television information, music, movies, etc.
The signature generator 114 may be used to generate monitored digital signatures based on audio information, as described in greater detail below. In particular, at the monitoring site 102, the signature generator 114 may be configured to generate monitored signatures based on monitored audio streams that are reproduced by the media delivery devices 110 and/or presented by the media presentation devices 112. The signature generator 114 may be communicatively coupled to the media delivery devices 110 and/or the media presentation devices 112 via an audio monitoring interface 118. In this manner, the signature generator 114 may obtain audio streams associated with media information that is reproduced by the media delivery devices 110 and/or presented by the media presentation devices 112. Additionally or alternatively, the signature generator 114 may be communicatively coupled to microphones (not shown) that are placed in proximity to the media presentation devices 112 to detect audio streams. The signature generator 114 may also be communicatively coupled to the central data collection facility 106 via the network 108.
The reference signature generator 122 may be configured to obtain the audio portion of all of the media information that is available in a particular broadcast region. The reference signature generator 122 may then generate a plurality of reference signatures (using, for example, the processing described in greater detail below) based on the audio information and store the reference signatures in the memory 126. Although one reference signature generator is shown in FIG. 1, a plurality of reference signature generators may be used in the reference site 104. For example, each of the plurality of signature generators may be communicatively coupled to a respective one of the broadcast information tuners 120.
The audio stream identification system 150 of FIG. 1B may be configured to monitor and identify audio streams associated with radio broadcast information, or any other media. In general, the audio stream identification system 150 is used to monitor the content that is broadcast by a plurality of radio stations in a particular broadcast region. Unlike the audio stream identification system 100 used to monitor television content consumed by an audience, the audio stream identification system 150 may be used to monitor music, songs, etc. that are broadcast within a broadcast region and the number of times that they are broadcast. This type of media tracking may be used to determine royalty payments, proper use of copyrights, etc. associated with each audio composition. The audio stream identification system 150 includes a monitoring site 152, a central data collection facility 154, and the network 108.
The reference signature generator 158 is configured to generate reference signatures based on reference audio streams. The reference audio streams may be stored on any type of machine accessible medium such as, for example, a CD, a DVD, a digital audio tape (DAT), etc. In general, artists and/or record producing companies send their audio works (i.e., music, songs, etc.) to the central data collection facility 154 to be added to a reference library. The reference signature generator 158 may read the audio data from the machine accessible medium and generate a plurality of reference signatures based on each audio work (e.g., the captured audio 300 of FIG. 3). The reference signature generator 158 may then store the reference signatures in the memory 134 for subsequent retrieval by the signature analyzer 132. Identification information (e.g., song title, artist name, track number, etc.) associated with each reference audio stream may be stored in a database and may be indexed based on the reference signatures. In this manner, the central data collection facility 154 includes a database of reference signatures and identification information corresponding to all known and available song titles.
The receiver 130 is configured to receive monitored signatures from the network 108 and store the monitored signatures in the memory 134. The monitored signatures and the reference signatures are retrieved from the memory 134 by the signature analyzer 132 for use in identifying the monitored audio streams broadcast within a broadcast region. The signature analyzer 132 may identify the monitored audio streams by first matching a monitored signature to a reference signature. The match information and/or the matching reference signature are then used to retrieve identification information (e.g., a song title, a song track, an artist, etc.) from a database stored in the memory 134.
FIG. 2 is a flow diagram representing an example signature generation process 200. As shown in FIG. 2, the signature generation process 200 first captures a block of audio that is to be characterized by a signature (block 202). An example time domain plot of audio that may be captured is shown in FIG. 3 at reference numeral 300. The audio may be captured from an audio source via, for example, a hardwired connection to an audio source or via a wireless connection, such as an audio sensor, to an audio source. If the audio source is analog, the capturing includes sampling the analog audio source using, for example, an analog-to-digital converter. In one example, the audio source may be sampled at a rate of 8 kilohertz (kHz), which is referred to as a sampling rate (Fs). This means that the analog audio is represented by digital samples thereof that are taken at the rate of eight thousand samples per second, or every 125 microseconds (us). Each of the audio samples may be represented by monoaural, 16 bits of resolution.
In one example, an audio block corresponding to 8192 samples is captured for processing. At the foregoing sampling rate of 8 kHz, this corresponds to 1.024 seconds of audio. However, this is merely one example, and the number of samples that are collected may correspond to audio segments ranging anywhere from approximately 1 second to 2 seconds in duration. Generically, herein the number of captured samples in an audio block is referred to with the variable N. Thus, in the above example, N=8192 and the time range of audio captured corresponds to t . . . t+N/Fs. A representation of an audio block is shown in FIG. 4 at reference numeral 402, in which, for example purposes, the audio block corresponds to a sinusoid.
After the audio block has been captured (block 202), the process 200 applies the first window function, referred to as W1 (block 204A), to the audio block to produce a first windowed audio block. Additionally, the process 200 windows the audio block using a second window function, referred to as W2 (block 204B) to produce a second windowed audio block. For example, the window may be a Gaussian or bell shaped function such as that shown at reference numeral 502 in FIG. 5, wherein the high and low ends of W1 502 have a zero value and the center of the window 502 has a value of one. In one example, the windowing is a sample-wise multiplication between the values of window function and respective samples of the audio block. For example, windowing the audio block 402 with the window 502, results in a windowed audio block 602, as shown in FIG. 6, wherein the amplitude of the windowed audio block 602 is zero at the extremes of the window 502 and is the same amplitude as the audio block 402 at the center of the windowed audio block 602.
Alternatively, rather the applying window functions in the time domain using sample-wise multiplication of window functions to the audio block, the windowing could be done in the frequency domain, wherein a frequency response corresponding to a time domain window may be convolved with the frequency spectrum of an audio block. As noted above, if frequency domain processing including a convolution is used, a conversion of the audio block to the frequency domain may be carried out using a Fourier transformation, wherein adjustments are made between audio blocks to account for discontinuity. Additionally, if the processing and application of the windows are done in the frequency domain, a time domain window having a frequency characteristic with a number of non-zero elements (e.g., 3-5 non-zero elements) may be selected.
The windows selected for W1 and W2 may be complimentary in nature. For example, if the window 502 shown in FIG. 5 is selected for W1, the window 702 of FIG. 7 may be selected for W2. As shown in FIG. 7, the window 702 is an inverted version of the window W1, namely W2(k)=1−W1(k), where k is a sample index in the window domain. Window W2 approaches unity value at the high and low ends of the window 702 and has a zero value in the center of the window 702. Thus, when the window 702 is applied to the audio block 402, a windowed audio block 802, as shown in FIG. 8, results. As shown in FIG. 8, the windowed audio block 802 has a zero amplitude in the center thereof, but has an amplitude that substantially matches the amplitude of the audio block 402 at the extremes of the windowed audio block 802.
As shown in FIGS. 9 and 10, the windows 502 and 702 have respective frequency responses, 902 and 1002. Thus, the application of the windows 502 and 702 to the audio block (e.g., the audio block 402), affects the spectrum of the audio block. As explained below, it is the different effects of different windows on the audio block that are examined to determine signatures representative of the audio block.
While the windows 502, 702 selected for the description above resemble a Hann window and an inverted Hann window, respectively, other window shapes may be used. For example, as shown in FIGS. 11 and 12, two asymmetrical windows 1102, 1202 may be selected, wherein a first window 1102 occupies an upper half of the windowing space and wherein a second window 1202 occupies a lower half of the windowing space. The frequency responses of asymmetrical windows 1102, 1202 are identical as shown in FIGS. 11 and 12 at reference numerals 1104 and 1204, but because the windows operate on mostly distinct portions of an audio block, the results of the windowing have different spectral characteristics for audio signals that are not sinusoidal.
While certain examples of window shapes are described herein, other window shapes may be used. For example, window shapes may be arbitrarily selected for both the first window and the second window (e.g., W1 and W2), wherein the selection is made from a set of window functions. Of course, different windows may be used at different times, provided the monitor and reference sites use the same times. Additionally, more than two windows may be used.
Returning to FIG. 2, after the windowing is complete (blocks 204A and 204B), the windowed audio blocks are respectively transformed (blocks 206A and 206B). In one example, the transformation may be a transformation from the time domain into the frequency domain. For example, the N samples of captured audio that have been windowed may be converted into an audio spectrum that is represented by N/2 complex DFT coefficients. Alternatively, any suitable transformation, such as wavelet transforms, DCT, MDCT, Haar transforms, Walsh transforms, etc., may be used.
After the transformations are completed (block 206A and 206B), the process 200 characterizes the results of each transform (block 208A and 208B). For example, the process may determine the energy in each of K+1 different bands of each of the transformation results. That is, the results of the transformation on the windowed audio block resulting from the use of window W1 (block 206A) may be divided into, for example, 16 different bands and the energy in each of the 16 different bands may be determined. This may be represented by Ej(w1), wherein j ranges from 0 to 15, and w1 indicates that the energy is associated with the spectrum resulting from the application of window W1 to the sampled audio (i.e., to the audio block). Similarly, the results of the transformation on the windowed block resulting from the use of window W2 (block 206B) may be divided into, for example, 16 different bands, the energy of which may be determined and represented as Ej(w2), wherein j ranges from 0 to 15, and w2 indicates that the energy is associated with the spectrum resulting from the application of window W2. Alternatively, different spectral characteristics other than energy may be used to characterize the results. For example, spectral flatness of energy distribution may be used.
After each set of transform results has been characterized (blocks 208A and 208B), the process 210 compares the results of the characterizations. For example, the results of the characterizations of each band may be subtracted from one another. In one example, an intermediate value may be calculated as dj=Ej(w2)−Ej(w1), wherein j ranges from 0 to K. Keeping with the specific example above in which K=15, an intermediate value dj may be calculated, wherein dj=Ej(w2)−Ej(w1), and j ranges from 0 to 15. Thus, such a comparison results in 16 different intermediate values (e.g., d0, d1, d2 . . . d15), wherein each intermediate value is the difference in characteristics in, for example, similar frequency bands of the spectra resulting from the transformations of the windowed audio blocks.
After the intermediate value has been calculated to represent the comparison of the characterizations (block 210), the process 200 determines signature bits based on the comparisons (block 212). For example, a signature bit Sj may be assigned a value of 1 if the intermediate value dj>0, and may be assigned a value of 0 otherwise, wherein j ranges from 0 to K. More specifically, as noted in the example above K=15 and, thus, there will be 16 comparisons of intermediate values to the value of 0 and, based on those comparisons, a 16 bit signature will be generated to represent the audio block, which was captured at block 202 of FIG. 2. After the signature has been determined (block 212), the process 200 iterates (block 214) and captures additional audio (block 202) to develop additional signatures.
While the foregoing describes the selection of a first window (W1) and a second window (W2) and that all signature bits for a block of captured audio are determined using the selected windows, other configurations are possible. For example, some bits of a signature representing the block of captured audio may be determined using a first pair of windows (e.g., W1 and W2), whereas other bits of the signature may be determined using a different pair of windows (e.g., W3 and W4). Additionally, a third pair of windows (e.g., W1 and W3) may be used to determine additional signature bits. In some cases, a unique pair of windows could be selected in a predetermined or arbitrary manner to determine the value of each signature bit, so long as those same window pairs were selected to operate on the same window blocks at the reference site.
The foregoing has described signaturing techniques that may be carried out to determine signatures representative of a portion of captured audio. FIG. 13 shows one example signature matching process 1300 that may be carried out to compare reference signatures (i.e., signatures determined at a reference site(s)) to monitored signatures (i.e., signatures determined at a monitoring site). The ultimate goal of signature matching is to find the closest match between a query audio signature (e.g., monitored audio) and signatures in a database (e.g., signatures taken based on reference audio). The comparison may be carried out at a reference site, a monitoring site, or any other data processing site having access to the monitored signatures and a database containing reference signatures.
Now turning in detail to the example method of FIG. 13, the example process 1300 involves obtaining a monitored signature and its associated timing (block 1302). As shown in FIG. 14, a signature collection may include a number of monitored signatures, three of which are shown in FIG. 14 at reference numerals 1402, 1404 and 1406. Each of the signatures is represented by a sigma (σ). Each of the monitored signatures 1402, 1404, 1406 may include timing information 1408, 1410, 1412, whether that timing information is implicit or explicit.
A query is then made to a database containing reference signatures (block 1304) to identify the signature in the database having the closest match. In one implementation, the measure of similarity (closeness) between signatures is taken to be a Hamming distance, namely, the number of position at which the values of query and reference bit strings differ. In FIG. 14, a database of signatures and timing information is shown at reference numeral 1416. Of course, the database 1416 may include any number of different signatures from different media presentations. An association is then made between the program associated with the matching reference signature and the unknown signature (block 1306).
The example methods, processes, and/or techniques described above may be implemented by hardware, software, and/or any combination thereof. More specifically, the example methods may be executed in hardware defined by the block diagrams of FIGS. 15 and 16. The example methods, processes, and/or techniques may also be implemented by software executed on a processor system such as, for example, the processor system 1610 of FIG. 16.
FIG. 15 is a block diagram of an example signature generation system 1500 for generating digital spectral signatures. In particular, the example signature generation system 1500 may be used to generate monitored signatures and/or reference signatures based on windowing, transforming, characterizing, and comparing, an audio block, as described above. For example, the example signature generation system 1500 may be used to implement the signature generators 114 and 122 of FIG. 1A or the signature generators 156 and 158 of FIG. 1B. Additionally, the example signature generation system 1500 may be used to implement the example methods of FIG. 2.
As shown in FIG. 15, the example signature generation system 1500 includes a sample generator 1502, a timing device 1503, a reference time generator 1504, a windower 1506, a transformer 1508, a characteristic determiner 1510, a comparator 1512, a signature determiner 1514, storage 1516, and a data communication interface 1518, all of which may be communicatively coupled as shown. The example signature generation system 1500 may be configured to obtain an example audio stream, acquire a plurality of audio samples from the example audio stream to form a block of audio and from that single block of audio, generate a signature representative thereof.
The sample generator 1502 may be configured to obtain the example audio stream, such as a stream resulting in the captured audio 300 of FIG. 3. The stream 300 may be any analog or digital audio stream. If the example audio stream is an analog audio stream, the sample generator 1502 may be implemented using an analog-to-digital converter. If the example audio stream is a digital audio stream, the sample generator 1502 may be implemented using a digital signal processor. Additionally, the sample generator 1502 may be configured to acquire and/or extract audio samples at any desired sampling frequency Fs. For example, as described above, the sample generator may be configured to acquire N samples at 8 kHz and may use 16 bits to represent each sample. In such an arrangement, N may be any number of samples such as, for example, N=8192. The sample generator 1502 may also notify the reference time generator 1504 when an audio sample acquisition process begins. The sample generator 1502 communicates samples to the windower 1506.
The reference time generator 1504 may initialize a reference time t0 when a notification is received from the sample generator 1502. The reference time t0 may be used to indicate the time within an audio stream at which a signature is generated. In particular, the reference time generator 1504 may be configured to read time data and/or a timestamp value from the timing device 1503 when notified of the beginning of a sample acquisition process by the sample generator 1502. The reference time generator 1504 may then store the timestamp value as the reference time t0.
The windower 1506 applies, for example, two windows to the audio block output from the sample generator 1502. Thus, the results of the windower 1506 are two windowed audio blocks. As described above, the windows may be any sets of windows. However, complimentary windows can be preferred because they would easily guarantee that on average the energy both values is the same, that leads to equi-probable bit distribution.
The transformer 1508 may be configured to perform an N point DFT on each of the windowed audio blocks, wherein N is the number of samples obtained by the sample generator 1502. For example, if the sample generator obtains 8192 samples, the transformer will produce a spectrum from the samples wherein the spectrum is represented by 4096 complex-valued Fourier coefficients.
The characteristic determiner 1510 may be configured to identify several frequency bands (e.g., 16 bands) within the DFT spectrum characterization generated by the transformer 1508. The selected bands may, but preferably do not, overlap with one another. The bands may be selected according to any technique. Of course, any number of suitable bands may be selected (e.g., 48). The characteristic determiner 1510 then determines a characteristic in each of the bands. For example, the characteristic determiner 1510 may determine the energy in each band. Thus, the results of the characteristic determiner 1510 are two sets of characteristics for each of, for example, 16 bands. For example, if 16 bands are selected, the characteristic determiner 1510 output would be 32 energy measures, one for each of the bands in each of the DFTs. The characteristics may be represented by Ej(w1) and Ej(w2), wherein j ranges from 0 to K (e.g., 0 to 15), and w1 and w2 represent window 1 and window 2, respectively.
The comparator 1512 compares the characteristics of respective bands to determine intermediate values. For example, the comparator 1512 may generate intermediate values according to dj=Ej(w2)−Ej(w1), such that energies in respective bands of the DFTs are subtracted from one another.
The signature determiner 1514 operates on the resulting values from the comparator 1512 to produce one signature bit for each of the intermediate values. This operation may be very similar or identical to the process 212 described above in conjunction with FIG. 2. That is, the signature bit values may be based a comparison of the intermediate value to zero. The signature bits are output to the storage 1516.
The storage may be any suitable medium for accommodating signature storage. For example, the storage 1516 may be a memory such as random access memory (RAM), flash memory, or the like. Additionally or alternatively, the storage 1516 may be a mass memory such as a hard drive, an optical storage medium, a tape drive, or the like.
The storage 1516 is coupled to the data communication interface 1518. For example, if the system 1500 is in a monitoring site (e.g., in a person's home) the signature information in the storage 1516 may be communicated to a collection facility, a reference site, or the like, using the data communication interface 1518.
FIG. 16 is a block diagram of an example signature comparison system 1600 for comparing digital spectral signatures. In particular, the example signature comparison system 1600 may be used to compare monitored signatures with reference signatures. For example, the example signature comparison system 1600 may be used to implement the signature analyzer 132 of FIG. 1A to compare monitored signatures with reference signatures. Additionally, the example signature comparison system 1600 may be used to implement the example process of FIG. 13.
The example signature comparison system 1600 includes a monitored signature receiver 1602, a reference signature receiver 1604, a comparator 1606, a Hamming distance filter 1608, a media identifier 1610, and a media identification look-up table interface 1612, all of which may be communicatively coupled as shown.
The monitored signature receiver 1602 may be configured to obtain monitored signatures via the network 108 (FIG. 1) and communicate the monitored signatures to the comparator 1606. The reference signature receiver 1604 may be configured to obtain reference signatures from the memory 134 (FIGS. 1A and 1B) and communicate the reference signatures to the comparator 1606.
The comparator 1606 and the Hamming distance filter 1608 may be configured to compare reference signatures to monitored signatures using Hamming distances. In particular, the comparator 1606 may be configured to compare descriptors of monitored signatures with descriptors from a plurality of reference signatures and to generate Hamming distance values for each comparison. The Hamming distance filter 1608 may then obtain the Hamming distance values from the comparator 1606 and filter out non-matching reference signatures based on the Hamming distance values.
After a matching reference signature is found, the media identifier 1610 may obtain the matching reference signature and in cooperation with the media identification look-up table interface 1612 may identify the media information associated with an unidentified audio stream (e.g., the example monitored audio stream 300 of FIG. 3). For example, the media identification look-up table interface 1612 may be communicatively coupled to a media identification look-up table or a database that is used to cross-reference media identification information (e.g., movie title, show title, song title, artist name, episode number, etc.) based on reference signatures. In this manner, the media identifier 1610 may retrieve media identification information from the media identification database based on the matching reference signatures.
FIG. 17 is a block diagram of an example processor system 1710 that may be used to implement the apparatus and methods described herein. As shown in FIG. 17, the processor system 1710 includes a processor 1712 that is coupled to an interconnection bus or network 1714. The processor 1712 includes a register set or register space 1716, which is depicted in FIG. 17 as being entirely on-chip, but which could alternatively be located entirely or partially off-chip and directly coupled to the processor 1712 via dedicated electrical connections and/or via the interconnection network or bus 1714. The processor 1712 may be any suitable processor, processing unit or microprocessor. Although not shown in FIG. 17, the system 1710 may be a multi-processor system and, thus, may include one or more additional processors that are identical or similar to the processor 1712 and that are communicatively coupled to the interconnection bus or network 1714.
The processor 1712 of FIG. 17 is coupled to a chipset 1718, which includes a memory controller 1720 and an input/output (I/O) controller 1722. As is well known, a chipset typically provides I/O and memory management functions as well as a plurality of general purpose and/or special purpose registers, timers, etc. that are accessible or used by one or more processors coupled to the chipset. The memory controller 1720 performs functions that enable the processor 1712 (or processors if there are multiple processors) to access a system memory 1724 and a mass storage memory 1725.
The system memory 1724 may include any desired type of volatile and/or non-volatile memory such as, for example, static random access memory (SRAM), dynamic random access memory (DRAM), flash memory, read-only memory (ROM), etc. The mass storage memory 1725 may include any desired type of mass storage device including hard disk drives, optical drives, tape storage devices, etc.
The I/O controller 1722 performs functions that enable the processor 1712 to communicate with peripheral input/output (I/O) devices 1726 and 1728 via an I/O bus 1730. The I/O devices 1726 and 1728 may be any desired type of I/O device such as, for example, a keyboard, a video display or monitor, a mouse, etc. While the memory controller 1720 and the I/O controller 1722 are depicted in FIG. 17 as separate functional blocks within the chipset 1718, the functions performed by these blocks may be integrated within a single semiconductor circuit or may be implemented using two or more separate integrated circuits.
The methods described herein may be implemented using instructions stored on a computer readable medium that are executed by the processor 1712. The computer readable medium may include any desired combination of solid state, magnetic and/or optical media implemented using any desired combination of mass storage devices (e.g., disk drive), removable storage devices (e.g., floppy disks, memory cards or sticks, etc.) and/or integrated memory devices (e.g., random access memory, flash memory, etc.).
Although certain methods, apparatus, and articles of manufacture have been described herein, the scope of coverage of this patent is not limited thereto.
assigning a signature bit representative of the portion of the digital data based on the comparison of the first characteristic and the second characteristic.
2. A method as defined in claim 1, wherein the first and second functions include complimentary window functions.
3. A method as defined in claim 1, wherein the first window function and the second window function are selected from a set of window functions.
4. A method as defined in claim 1, wherein the digital data includes at least one of time series data, video information, a web page, a still image, or computer data.
5. A method as defined in claim 1, wherein the first and second characteristics include spectral flatness of the first and second windowed blocks.
a signature determiner to assign a signature bit representative of the portion of the digital data based on the comparison of the first characteristic and the second characteristic, at least one of the windower, the characteristic determiner, the comparator, or the signature determiner being implemented using a processor.
7. An apparatus as defined in claim 6, wherein the first and second functions include complimentary window functions.
8. An apparatus as defined in claim 6, wherein the first window function and the second window function are selected from a set of window functions.
9. An apparatus as defined in claim 6, wherein the digital data includes at least one of time series data, video information, a web page, a still image, or computer data.
10. An apparatus as defined in claim 6, wherein the first and second characteristics include spectral flatness of the first and second windowed blocks.
assign a signature bit representative of the portion of the digital data based on the comparison of the first characteristic and the second characteristic.
12. A storage medium as defined in claim 11, wherein the first and second functions include complimentary window functions.
13. A storage medium as defined in claim 11, wherein the first window function and the second window function are selected from a set of window functions.
14. A storage medium as defined in claim 11, wherein the digital data includes at least one of time series data, video information, a web page, a still image, or computer data.
15. A storage medium as defined in claim 11, wherein the first and second characteristics include spectral flatness of the first and second windowed blocks.
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