EP1388145A1 - Vorrichtung und verfahren zum analysieren eines audiosignals hinsichtlich von rhythmusinformationen - Google Patents
Vorrichtung und verfahren zum analysieren eines audiosignals hinsichtlich von rhythmusinformationenInfo
- Publication number
- EP1388145A1 EP1388145A1 EP02745267A EP02745267A EP1388145A1 EP 1388145 A1 EP1388145 A1 EP 1388145A1 EP 02745267 A EP02745267 A EP 02745267A EP 02745267 A EP02745267 A EP 02745267A EP 1388145 A1 EP1388145 A1 EP 1388145A1
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- European Patent Office
- Prior art keywords
- rhythm information
- signal
- subband
- raw
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
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Links
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- 230000005236 sound signal Effects 0.000 title claims abstract description 53
- 238000000034 method Methods 0.000 title claims description 14
- 238000005311 autocorrelation function Methods 0.000 claims description 71
- 238000009499 grossing Methods 0.000 claims description 2
- 238000013441 quality evaluation Methods 0.000 abstract description 2
- 238000013432 robust analysis Methods 0.000 abstract 1
- 238000000605 extraction Methods 0.000 description 9
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- 238000012805 post-processing Methods 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
- 230000001020 rhythmical effect Effects 0.000 description 6
- 238000011156 evaluation Methods 0.000 description 5
- 238000001303 quality assessment method Methods 0.000 description 5
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- 230000004069 differentiation Effects 0.000 description 1
- 210000003027 ear inner Anatomy 0.000 description 1
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Classifications
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/90—Pitch determination of speech signals
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10H—ELECTROPHONIC MUSICAL INSTRUMENTS; INSTRUMENTS IN WHICH THE TONES ARE GENERATED BY ELECTROMECHANICAL MEANS OR ELECTRONIC GENERATORS, OR IN WHICH THE TONES ARE SYNTHESISED FROM A DATA STORE
- G10H1/00—Details of electrophonic musical instruments
- G10H1/36—Accompaniment arrangements
- G10H1/40—Rhythm
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10H—ELECTROPHONIC MUSICAL INSTRUMENTS; INSTRUMENTS IN WHICH THE TONES ARE GENERATED BY ELECTROMECHANICAL MEANS OR ELECTRONIC GENERATORS, OR IN WHICH THE TONES ARE SYNTHESISED FROM A DATA STORE
- G10H2210/00—Aspects or methods of musical processing having intrinsic musical character, i.e. involving musical theory or musical parameters or relying on musical knowledge, as applied in electrophonic musical tools or instruments
- G10H2210/031—Musical analysis, i.e. isolation, extraction or identification of musical elements or musical parameters from a raw acoustic signal or from an encoded audio signal
- G10H2210/076—Musical analysis, i.e. isolation, extraction or identification of musical elements or musical parameters from a raw acoustic signal or from an encoded audio signal for extraction of timing, tempo; Beat detection
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10H—ELECTROPHONIC MUSICAL INSTRUMENTS; INSTRUMENTS IN WHICH THE TONES ARE GENERATED BY ELECTROMECHANICAL MEANS OR ELECTRONIC GENERATORS, OR IN WHICH THE TONES ARE SYNTHESISED FROM A DATA STORE
- G10H2250/00—Aspects of algorithms or signal processing methods without intrinsic musical character, yet specifically adapted for or used in electrophonic musical processing
- G10H2250/131—Mathematical functions for musical analysis, processing, synthesis or composition
- G10H2250/135—Autocorrelation
Definitions
- the present invention relates to signal processing concepts and, in particular, to the analysis of audio signals with regard to rhythm information.
- semantically relevant features make it possible to model similarity relationships between pieces that come close to human perception.
- the use of features that have semantic meaning also makes it possible, for example, to automatically propose pieces that are of interest to a particular user if his preferences are known.
- the tempo is an important musical parameter that has semantic meaning.
- the tempo is usually measured in "beats per minute” (BPM).
- BPM beats per minute
- the automatic extraction of the tempo as well as the beats of the "beat” or generally speaking the automatic extraction of rhythm information is an example of obtaining a semantically important feature of a piece of music.
- beat tracking For the determination of the center of gravity and thus also the tempo, d. H.
- rhythm information For the determination of rhythm information, the term "beat tracking" has also become established in the specialist circles. It is already known from the prior art to use beat tracking on the basis of a note-like or transcribed signal representation, for example in the midi The aim, however, is not to require such a meta representation, but to carry out an analysis directly with, for example, a PCM-coded or, generally speaking, digitally available audio signal.
- the input signal is split into a series of subbands by means of a filter bank, for example into 6 subbands with crossover frequencies of 200 Hz> 400 Hz, 800 Hz, 1600 Hz and 3200 Hz.
- Low pass filtering is carried out for the first subband.
- Sub-band is high-pass filtered, band-pass filtering is described for the remaining sub-bands in between.
- Each sub-band is processed as follows.
- the sub-band signal is first rectified.
- the absolute value of the samples is determined.
- the resulting n values are then smoothed, for example with an averaging over a suitable window to egg n receive envelope signal.
- the envelope signal can be subsampled to reduce the computational complexity.
- the envelope signals are differentiated, ie Sudden changes in the signal amplitude are preferably passed on through the differentiation filter. The result is then limited to non-negative values.
- Each envelope signal is then placed in a bank of resonant filters, ie oscillators, each containing a filter for each tempo range, so that the filter that matches the musical tempo is most strongly stimulated.
- the energy of the output signal is calculated as a measure of the correspondence between the tempo of the input signal and the tempo associated with the filter.
- the energies for each tempo are finally summed up over all subbands, the largest energy sum identifying the tempo supplied as the result, ie the rhythm information.
- a major disadvantage of this method is the great computation and storage complexity, in particular for realizing the large number of parallel-oscillating “oscillators”, of which only one is ultimately selected. This makes efficient implementation, for example for real-time applications, almost impossible.
- the known algorithm is shown in Fig. 3 as a block diagram.
- the audio signal is input via an audio input 300 ner analysis filter bank 302 supplied.
- the analysis filter bank generates a number n of channels, ie individual subband signals, from the audio input. Each subband signal contains a certain range of frequencies of the audio signal.
- the filters of the analysis filter bank are selected so that they approximate the selection characteristics of the human inner ear.
- Such an analysis filter bank is also referred to as a gamma-tone filter bank.
- rhythm information of each subband signal is evaluated in the devices 304a to 304c.
- an envelope-like output signal is first calculated (corresponding to a so-called “inner hair cell” processing in the ear) and subsampled.
- An autocorrelation function is calculated from this result in order to determine the periodicity of the signal as a function of the delay, ie the " Lag ".
- An autocorrelation function which represents aspects of the rhythm information of each subband signal, is then present at the output of the devices 304a to 304c for each subband signal.
- the individual autocorrelation functions of the subband signals are then combined in a device 306 by summation in order to obtain a sum autocorrelation function (SAKF) which reproduces the rhythm information of the signal at the audio input 300.
- SAKF sum autocorrelation function
- This information can be output at a tempo output 308.
- Large values in the total autocorrelation indicate that there is a high periodicity of the beginning of notes for a delay (lag) assigned to a peak of the SAKF. Therefore, for example, the greatest value of the sum auto-correlation function is sought within the musically sensible delays.
- Musicly sensible delays include the tempo range between 60 bpm and 200 bpm.
- Means 306 may also be arranged to convert a delay time into tempo information. For example, a peak of a one second delay corresponds to a rate of 60 beats per minute. Smaller delays indicate higher speeds, while larger delays indicate lower speeds than 60 bpm.
- This method has an advantage over the first-mentioned method in that no oscillators need to be implemented with a large amount of computation and memory.
- the concept is disadvantageous in that the quality of the results depends very much on the type of audio signal. If, for example, a dominant rhythm instrument can be heard from an audio signal, the concept described in FIG. 3 will work well. If, on the other hand, the voice is dominant, which will not provide particularly clear rhythm information, the rhythm determination will be ambiguous. In the audio signal there could also be a tape that only contains rhythm information, ie.z. B.
- the sum autocorrelation function, obtained by means 306 is ambiguous.
- the sum autocorrelation function at output 306 is ambiguous in that an autocorrelation function peak is generated even when a delay is multiplied. This is understandable from the fact that a sine component with a period of tO, if it is subjected to an auto-correlation function processing, generates maxi a at multiples of the delays, ie at 2t0, 3t0, etc., in addition to the desired maximum at tO.
- the calculation model divides the signal into two channels, namely a channel below 1000 Hz and a channel above 1000 Hz. From this an autocorrelation of the lower channel and an autocorrelation of the envelope curve of the upper channel are calculated.
- ESACF Enhanced Summary-Autocorrelation Function
- the object of the present invention is to create a computing time-efficient and robust device and a computing time-efficient and robust method for analyzing an audio signal with regard to rhythm information. This object is achieved by a device for analyzing an audio signal according to claim 1 or by a method for analyzing an audio signal according to claim 11.
- the present invention is based on the knowledge that in the individual frequency bands, i. H. the sub-bands, there are often different favorable conditions for finding rhythmic periodicities. While in pop music, for example, the signal is often dominated in the middle range, for example around 1 kHz, by vocals that do not correspond to the beat, percussion sounds are often present in the higher frequency ranges. B. the hi-hat of the drums, which allow a very good extraction of rhythmic regularities. In other words, depending on the audio signal, different frequency bands contain a different amount of rhythmic information or have a different quality or significance for the rhythm information of the audio signal.
- the audio signal is therefore first broken down into subband signals. Any subband signal. is examined for its periodicity to obtain raw rhythm information for each subband signal. An evaluation of the quality of the periodicity of each subband signal is then carried out in accordance with the present invention in order to obtain a measure of significance for each subband signal. A high level of significance indicates that there is clear rhythm information in this subband signal, while a low level of significance indicates that there is less clear rhythm information in this subband signal.
- a modified envelope curve of the subband signal is first calculated and then an autocorrelation function of the envelope curve is calculated.
- the autocorrelation function of the envelope represents the raw rhythm information.
- Clear rhythm information is available if the autocorrelation function has clear maxima, while less clear rhythm information is available if the autocorrelation function of the envelope of the subband signal has fewer pronounced signal peaks or no signal peaks at all.
- An autocorrelation function that has significant signal peaks is therefore given a high level of significance, while an autocorrelation function that has a relatively flat profile is obtained a low level of significance.
- the individual raw rhythm information of the individual subband signals are not simply combined “blindly”, but are used for each subband signal, taking into account the significance measure, in order to obtain the rhythm information of the audio signal. If a subband signal has a high significance measure, it becomes so when determining the rhythmin - - Formations preferred, while a ... sub-band signal that has a low degree of significance, ie that has a low quality with regard to the rhythm information, is hardly taken into account in the determination of the rhythm information of the audio signal or, in extreme cases, is not taken into account at all.
- this weighting can result in all subband signals except the one subband signal receiving a weighting factor of 0, i.e. not at all when determining the rhythm information are taken into account, so that the rhythm information of the audio signal is determined only from a single subband signal.
- the concept according to the invention is advantageous in that it enables the rhythm information to be determined robustly, since subband signals with no clear or even deviating rhythm information, ie if the vocals have a different rhythm than the actual beat of the piece, do not contain the rhythm information of the audio signal. water down "or” falsify ". They also become very intoxicating.
- Subband signals,. wel ⁇ he__ .. provide a system autocorrelation function with a completely flat profile, do not deteriorate the signal / noise ratio when determining the rhythm information. Exactly this would occur, however, if, as in the prior art, all the autocorrelation functions of the subband signals were simply added up with the same weight.
- Another advantage of the method according to the invention is that a significance measure can be determined with a small additional computing effort, and that the evaluation of the raw rhythm information with the significance measure and the subsequent summation can be carried out efficiently without a large amount of memory and computing time, which the concept according to the invention also recommends in particular for real-time applications.
- FIG. 1 shows a block diagram of a device for analyzing an audio signal with a quality evaluation of the raw rhythm information
- FIG. 2 shows a block diagram of a device for analyzing an audio signal using weighting factors on the basis of the significance measures
- FIG. 3 shows a block diagram of a known device for analyzing an audio signal with regard to rhythm information
- FIG. 4 shows a block diagram of a device for analyzing an audio signal with regard to rhythm information using an autocorrelation function with a subband-wise postprocessing of the rhythm raw information
- FIG. 5 shows a detailed block diagram of the device for post-processing from FIG. 4.
- FIG. 1 shows a block diagram of a device for analyzing an audio signal with regard to rhythm information.
- the audio signal is transmitted via an input 100 to a device 102 for splitting the audio signal into at least two Subband signals 104a and 104b supplied.
- Each subband signal 104a, 104b is fed into means 106a and 106b for examining it for periodicities in the subband signal to obtain raw rhythm information 108a and 108b for each subband signal.
- the raw rhythm information is then fed to a device 110a or 110b for evaluating a quality of the periodicity of each of the at least two subband signals in order to obtain a significance measure 112a, 112b for each of the at least two subband signals.
- Both the raw rhythm information 108a, 108b and the significance measures 112a, 112b are fed to a device 114 for determining the rhythm information of the audio signal.
- the device 114 takes into account the significance measures 112a, 112b for the subband signals and the raw rhythm information 108a, 108b of at least one subband signal.
- the device 110a for quality assessment has determined, for example, that there is no particular periodicity in the subband signal 104a, the significance measure 112a will be very small or equal to 0.
- the device -114- for determining the rhythm information determines that the significance measure 112a is equal to zero, so that the raw rhythm information 108a of the subband signal 104a is no longer taken into account when determining the rhythm information of the audio signal Need to become.
- the rhythm information of the audio signal is then determined solely and exclusively on the basis of the raw rhythm information 108b of the subband signal 104b.
- FIG. 2 is discussed with regard to a special embodiment of the device from FIG. 1.
- a device 102 for decomposing the audio signal a conventional before analysis filter bank are used, which delivers a user-selectable number of subband signals on the output side.
- Each subband signal is then subjected to the processing of the devices 106a, 106b and 106c, whereupon the devices 110a to 110c then determine significance measures of each raw rhythm information.
- the device 114 comprises a device 114a for calculating weighting factors for each subband signal on the basis of the significance measure for this subband signal and optionally also for the other subband signals.
- a weighting of the raw rhythm information 108a to 108c then takes place in the device 114b with the weighting factor for this subband signal, whereupon, also in the device 114b, the weighted raw rhythm information is combined, e.g. B. summed up, in order to obtain the rhythm information of the audio signal at the tempo output 116.
- the inventive concept turns' thus follows. After evaluating the rhythmic information of the individual bands which .stattfinden example, envelope shaping, smoothing, differentiating, limiting to positive values and making -the autocorrelation function., Can (Einrich- .. obligations 106a to 106c), an evaluation of the value or the quality of these intermediate results takes place in the devices 110a to 110c.This is achieved with the aid of an evaluation function which evaluates the reliability of the individual results with a significance measure. The significance measures of all subband signals become a weighting factor for each band for the extraction of the rhythm information The overall result of the rhythm extraction is then achieved in the device 114b by combining the band-wise individual results taking into account their respective weighting factors. As a result, an algorithm for rhythm analysis implemented in this way shows a good ability to reliably find rhythmic information in a signal even under unfavorable conditions. The concept according to the invention is therefore characterized by a high level of robustness.
- the raw rhythm information 108a, 108b, 108c which represent the periodicity of the respective subband signal, is determined by means of an autocorrelation function.
- the significance measure by dividing a maximum of the autocorrelation function by an average of the autocorrelation function and then subtracting the value 1. It should be noted that every autocorrelation function always delivers a local maximum at a delay of 0, which represents the energy of the signal. This maximum should be disregarded so that the quality determination is not falsified.
- the autocorrelation function should only be considered in a special tempo range, ie from a maximum deceleration, .. which corresponds to the smallest, inter, e.ss., n, > tempo, to a minimum deceleration, which corresponds to the highest of interest Pace corresponds.
- a typical tempo range is between 60 bpm and 200 bpm.
- the ratio between the arithmetic mean value of the autocorrelation function in the tempo area of interest and the geometric mean value of the autocorrelation function in the tempo area of interest can be determined as a significance measure. It is known that if all values of the autocorrelation function are the same, ie if the autocorrelation function has a flat course, the geometric one The mean value of the autocorrelation function and the arithmetic mean value of the autocorrelation function are the same. In this case, the significance measure would have a value of 1, which means that the raw rhythm information is not significant.
- the ratio of the arithmetic mean to the geometric mean would be greater than 1, which means that the auto-correlation function has good rhythm information.
- the smaller the ratio between the arithmetic mean and the geometric mean the flatter the autocorrelation function and the fewer periodicities it contains, which in turn means that the rhythm information of this subband signal is less significant, i. H. have a lower quality, which will result in a low or a weighting factor of 0.
- a relative weighting is preferred, such that all weighting factors of all subband signals add up to 1, i. H. that the weighting factor of a band,. «- is determined-.-,. ,,, as ,,, the significance value. « ... this, it.,tician. band divided by the sum of all significance values.
- a relative weighting is carried out before the summation of the weighted raw rhythm information in order to obtain the rhythm information of the audio signal.
- the audio signal is fed via the audio signal input 100 into the device 102 for splitting the audio signal into subband signals 104a and 104b.
- Each subband signal is then examined in the device 106a or 106b, as has been carried out, using an autocorrelation function in order to determine the periodicity of the subband signal.
- the raw rhythm information 108a, 108b is then available at the output of the device 106a or 106b. These are fed into a device 118a or 118b in order to postprocess the raw rhythm information output by the device 116a by means of the autocorrelation function.
- the quality assessment can, however, also take place on the basis of the post-processed raw rhythm information, this latter possibility being preferred since the quality assessment on the basis of the post-processed rhythm Raw information ensures that the quality of information that is no longer ambiguous is assessed.
- the determination of the rhythm information by means 114 then takes place on the basis of postprocessed rhythm information of a channel and preferably also on the basis of the significance measure for this channel.
- a quality assessment is carried out on the basis of the raw rhythm information, i.e. the signal in front of the device 118a, this is advantageous in that if it is determined that the significance measure is equal to 0, i. H. that the autocorrelation function has a flat course, the postprocessing by means of the device 118a can be dispensed with entirely in order to save computing time resources.
- FIG. 5 is discussed in order to show a more detailed structure of a device 118a or 118b for postprocessing the rhythm raw information.
- the subband signal for example 104a
- a spread auto-correlation function can be calculated by means of a device 121, the device 121 being arranged to calculate the spread auto-correlation function so that it is spread by an integral multiple of a delay .
- Means 122 is arranged in this case to subtract the spread autocorrelation function from the original autocorrelation function, ie the raw rhythm information 108a.
- an autocorrelation function spread by a factor of 3 is calculated in the device 121 and subtracted from the result of the previous subtraction, so that all ambiguities are gradually eliminated from the raw rhythm information.
- the device 121 can be arranged to calculate an autocorrelation function compressed by an integer factor, which is then added by the device 122 to the rhythm raw information in order to also include components for delays t0 / 2, tO / 3, etc . to create.
- the spread or compressed versions of the raw rhythm information 108a can be weighted before adding or subtracting, in order to achieve flexibility in the sense of a high level of robustness.
- the periodicity "-.kan -So _a more Improvement .. he elt z. 1 J -W. are “,. ,, if. the. Properties of the autocorrelation function are included and the post-processing is carried out using the device 118a or 118b.
- a periodic sequence of note starts with a distance tO not only generates an AKF peak with a delay tO but also at 2t0, 3t0, etc. This will lead to ambiguity in the tempo detection, i.e. the search for significant maxima in the autocorrelation function.
- the ambiguities can be cleared if versions of the AKF that are spread by integer factors are subtracted from the initial value on a sub-band basis (weighted). Furthermore, there is the problem with the autocorrelation function that it does not provide any information at tO / 2, t0 / 3 ... etc., that is to say at twice, three times, etc. of the “basic tempo”, which can lead to incorrect results especially if Two instruments that are in different subbands together define the rhythm of the signal, which is taken into account by calculating versions of the auto-correlation function that are compressed by integer factors, and then adding or unweighting these to the raw rhythm information.
- the AKF postprocessing thus takes place sub-band, whereby an autocorrelation function is calculated for at least one sub-band signal and this is combined with stretched or spread versions of this function.
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- Acoustics & Sound (AREA)
- Multimedia (AREA)
- Computational Linguistics (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Audiology, Speech & Language Pathology (AREA)
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Abstract
Description
Claims
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE10123366 | 2001-05-14 | ||
DE10123366A DE10123366C1 (de) | 2001-05-14 | 2001-05-14 | Vorrichtung zum Analysieren eines Audiosignals hinsichtlich von Rhythmusinformationen |
PCT/EP2002/004618 WO2002093557A1 (de) | 2001-05-14 | 2002-04-25 | Vorrichtung und verfahren zum analysieren eines audiosignals hinsichtlich von rhythmusinformationen |
Publications (2)
Publication Number | Publication Date |
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EP1388145A1 true EP1388145A1 (de) | 2004-02-11 |
EP1388145B1 EP1388145B1 (de) | 2004-10-13 |
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Application Number | Title | Priority Date | Filing Date |
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EP02745267A Expired - Lifetime EP1388145B1 (de) | 2001-05-14 | 2002-04-25 | Vorrichtung und verfahren zum analysieren eines audiosignals hinsichtlich von rhythmusinformationen |
Country Status (7)
Country | Link |
---|---|
US (1) | US20040068401A1 (de) |
EP (1) | EP1388145B1 (de) |
JP (1) | JP3914878B2 (de) |
AT (1) | ATE279769T1 (de) |
DE (2) | DE10123366C1 (de) |
HK (1) | HK1059959A1 (de) |
WO (1) | WO2002093557A1 (de) |
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JP6759545B2 (ja) * | 2015-09-15 | 2020-09-23 | ヤマハ株式会社 | 評価装置およびプログラム |
US10666475B2 (en) * | 2018-10-29 | 2020-05-26 | Bae Systems Information And Electronic Systems Integration Inc. | Techniques for phase modulated signals having poor autocorrelation |
CN111785237B (zh) * | 2020-06-09 | 2024-04-19 | Oppo广东移动通信有限公司 | 音频节奏确定方法、装置、存储介质和电子设备 |
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GB2207027B (en) * | 1987-07-15 | 1992-01-08 | Matsushita Electric Works Ltd | Voice encoding and composing system |
JP2591430B2 (ja) * | 1993-06-30 | 1997-03-19 | 日本電気株式会社 | ベクトル量子化装置 |
JP3840684B2 (ja) * | 1996-02-01 | 2006-11-01 | ソニー株式会社 | ピッチ抽出装置及びピッチ抽出方法 |
JPH09293083A (ja) * | 1996-04-26 | 1997-11-11 | Toshiba Corp | 楽曲検索装置および検索方法 |
KR100269216B1 (ko) * | 1998-04-16 | 2000-10-16 | 윤종용 | 스펙트로-템포럴 자기상관을 사용한 피치결정시스템 및 방법 |
DE10123281C1 (de) * | 2001-05-14 | 2002-10-10 | Fraunhofer Ges Forschung | Vorrichtung und Verfahren zum Analysieren eines Audiosignals hinsichtlich von Rhythmusinformationen des Audiosignals unter Verwendung einer Autokorrelationsfunktion |
JP3859462B2 (ja) * | 2001-05-18 | 2006-12-20 | 株式会社東芝 | 予測パラメータ分析装置および予測パラメータ分析方法 |
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2001
- 2001-05-14 DE DE10123366A patent/DE10123366C1/de not_active Expired - Fee Related
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2002
- 2002-04-25 AT AT02745267T patent/ATE279769T1/de not_active IP Right Cessation
- 2002-04-25 WO PCT/EP2002/004618 patent/WO2002093557A1/de active IP Right Grant
- 2002-04-25 JP JP2002590149A patent/JP3914878B2/ja not_active Expired - Lifetime
- 2002-04-25 US US10/467,704 patent/US20040068401A1/en not_active Abandoned
- 2002-04-25 EP EP02745267A patent/EP1388145B1/de not_active Expired - Lifetime
- 2002-04-25 DE DE2002501311 patent/DE50201311D1/de not_active Expired - Lifetime
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JP2004528596A (ja) | 2004-09-16 |
JP3914878B2 (ja) | 2007-05-16 |
DE50201311D1 (de) | 2004-11-18 |
WO2002093557A1 (de) | 2002-11-21 |
DE10123366C1 (de) | 2002-08-08 |
US20040068401A1 (en) | 2004-04-08 |
ATE279769T1 (de) | 2004-10-15 |
HK1059959A1 (en) | 2004-07-23 |
EP1388145B1 (de) | 2004-10-13 |
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