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To the evaluation of natural and technical signals as well as temporal courses in economy and stock exchange are increasingly employed mixed methods of the Time and Frequency Analysis [Qian 96]. They are suitable for continuous signal courses. Unlike this for the analysis of discontinuous signal courses being used procedures which the respective system states, which causally lead to the discontinuity, evaluate. It lies near, the mentioned methods and procedures summarize for the event driven Time and Frequency Analysis (edT&FA). They are suitable for discontinuous and continuous signal courses. The lecture clearly introduces himself into the application of such a procedure. The lecture shows the possibilities for the extraction of features vectors, which are used particularly for an improvement in the automatic speech recognition (ASR). Afterwards is introduced a service concept with this the Microtechnologie companies are supported to use procedures, facilities and methods of the event driven Time and Frequency Analysis.

At the interface between the continuous and discontinuous worlds the term "event" is used in a facet to Petri [Reisig 82]: An event is by Petri a point of time, which enters at the analysis of temporal courses (vibrations) under definite conditions. However it also can be a point in the space, which is reached under definite conditions at the analysis of spatial courses (waves) in the room. The event driven Time and Frequency Analysis are controlled by at least two so defined events. On the simplest case these events are directly won from the observation of the course of the signal. Every signal has a characteristic (amplitudes, periods duration, phases relationship of base vibration and harmonics as well as the dominant vibration) [Jacob 95] they can be used for this.

Followingly two examples are selected to the illustration the event driven Time and Frequency Analysis: In the first example the intensity of the dominant vibration of the signal is changeable and the short-term amplitudes shall be determined. In the second example the frequency of the dominant vibration of the signal, described as flexibility here, is changeable and the short-term periods duration shall be measured. The intensity brings a statement about the short-term energy density of the signal. Fig. 1 shows the dominant vibration of a signal with steady period duration and variable (relieving) intensity. This course could be a vowel of the human language.

Only entered events (after process of a half vibration) lead to standing up the features vector. Therefore the actual information content of a vowel can be condensed on a minimum. The features vector contains real and imaginary part for the dominant vibration and its amplitude. For harmonics of the signal can be won further features (not represented in fig. 1).

Fig. 3: Prototype of one event driven Time and Frequency Analysis, part from a state graph in accordance with UML version 1.0

To execute the event driven Frequency Analysis into fig. 1 online, in this the period duration of the dominant vibration must be ahead known. How it turns out, well it becomes the period duration "to predict", pointed in the second example: The above mentioned flexibility describes the short-term resonance of the signal source. Fig. 2 shows the dominant vibration of a signal with constant intensity however a variable (relieving) flexibility. They got borrowed the variety of the consonants of the human language. Thank for very briefly measure and analysis time an actual features vector with the measured period duration is immediately ready after the process of the dominant half vibration. It turns out, short-term frequency parts into consonants information technical to spread and his features vector driven in not equidistant steps by the event control to put. Further features in this vector are predicted periods duration of the first and second order, which are won from the first and second derivation of the period duration after the time. The predicted period duration of the n^th order are used for even counting base points for an online FFT of the same Signal.

For the design of procedures, facilities and methods of the event driven Time and Frequency Analysis is recommended a prototype. Fig. 3 shows a part from the prototype for the extraction of the features vectors, which one into the fig. 1 and 2 was used. He was note down as state graph complies with the standard Unified Modeling Language version 1.0 [UML 97]: The transitions transStartOver and transGoUnder supervise the signal cvS1 to be analyzed. In the state iniOver the measuring in the upper half vibration is prepared, in the states sumOver executed and in fixOver completed. As mirror functions for the Fourier Transform pretended a Sinus function for miS1K1re and a Cosinus function for miS1K1im. Here will also provide every other explicitly portrayable function of the JTFA [Qian 96] or also Wavelets [Daube 92]. When required, the dimension (is here only complex) of the channel K1 will refines for Wavelets. In the state iniOver an ordinal number for the channel nyK1 = ny can provide. For the allocation of further ordinal numbers nyK2, nyK3, ... , nyKm must be established still corresponding further channels K2, K3, ... , Km (not represented in Fig. 3). It is remarkable, that by the introduced concept the number of the channel in the practical application remains limited on 8 to 32. What to be analyzed in every channel (mirror function, ordinal number, equation with the method etc.) is newly assigning from event to event.

The charges to the implementation of event driven procedures into hard and software solutions are not insignificant. For this reason the integral system consideration is supported in special way of the author. In the formulation of this task are selected into dependence of the signal to be analyzed (1) suitable procedures for the period duration measuring. The (2) gradually generation of the events will then defined. Then the (3) number of the states, which the event driven Time and Frequency Analysis arises, can take. In conclusion the (4) states get agree the transformation rules and finally the (5) in and output data formats will assigned.

The event driven Time and Frequency Analysis (edT&FA) are not only used for discontinuous signals. Among other things they is usually excellently for the analysis of natural signals, which is characterized by a dominant vibration, like also the human language. Based of the modern Microtechnologie arise various applications for the structure examinations into nature, technique and economy. The event driven Time and Frequency Analysis is further preferably suitable for the Neuronal processing, as it is used into the Diagnosis of machines, vehicle and aggregates as well as for the automatic speech recognition (ASR). The development, already above recommended, of one prototype in a standardized language of design will to perform the following steps: First the suitability of the prototypes, proved with help of test vectors (mathematically generated signals), can established. After this is carried out with help of the ready prototype the machine generation of the layout for a hardware solution or the automatic generation of the program code for a software solution. Also in this and all further design phases the prototype entitled at the disposal for verifying design, measure and analysis results.

[Reisig 82] Wolfgang Reisig: Petrinetze - eine Einführung. Springer-Verlag Heidelberg 1982

[Daube 92] Daubechies, I.: Ten Lectures on Wavelets. Society for Industrial and Applied Mathematic, Philadelphia, PA (1992)

[Jacob 95] Christian Jacob: Verfahren zur Periodendauer- und Wellenlängenmessung dominanter Schwingungen und Wellen sowie zur Spektralanalyse von Schwingungen und Wellen, Offenlegungsschrift DE 195 20 836 vom 31.05.1995

[Qian 96] Qian, S.; Chen. D: Joint Time-Frequenz Analysis, Methods and Applications. Prentic Hall. 1996

[UML 97] Unified Modeling Language (UML) Version 1.0 vom 13.01.1997