Abstract:
An apparatus, a circuit and a method are given, to realize very effective noise suppression for speech signals. Using thereby novel calculation methods allow for a real-time operation without any remarkable delay. Also a significant reduction of the overall processing power demands in conjunction with reduced memory requirements is achieved. Using the intrinsic advantages of that solution the circuit of the invention is manufactured with standard CMOS technology and/or standard Digital Signal Processors at low cost.

Description:
BACKGROUND OF THE INVENTION 
   (1) Field of the Invention 
   The invention relates generally to electronic circuits for telecommunications and to methods used therewith and more particularly, to a circuit for transmission of sound signals and to a method for speech transmission with noise suppression. The invention also concerns an apparatus for implementing the method and use thereof. 
   (2) Description of the Prior Art 
   In telecommunications and recording techniques of sound signals a major problem is the degradation of the transmitted or recorded sound signals by ambient noise. When it comes to speech transmission or recording the intelligibility of the transmitted or recorded speech signal in the presence of audible noise is most important. This is especially very obvious and significant in the case where car drivers are communicating on telephone during ride with the aid of hands-free phone equipment. In order to generally suppress or reduce audible ambient noise of such sound signals a multitude of techniques and methods has been specified in the past. 
   The main problem hereby is due to the fact, that in most cases the unwanted noise signal and the wanted sound signal are most likely to appear within the same frequency range. Such they have to be discriminated by other characteristics than their frequency range. Albeit filtering techniques in the frequency domain have been vastly used in prior art, yet with unsatisfactory results. Other discrimination characteristics, both in the frequency and in the time domain have been under scrutiny in many different prior art approaches and have proved to deliver more satisfying results. Modern digital integrated signal processing circuits either built up with discrete computational units or in the form of monolithic digital signal processors allow for an extensive use of advanced calculation algorithms such as the Fast/Discrete Fourier Transformation (FFT/DFT) or Correlation Analysis (CA) methods. The computational demands hereby are however very high and are often not suitable for real-time applications. In case where real-time requirements have to be met, practical realizations lead to very costly solutions. 
     FIG. 1A  prior art depicts the normally used method for the processing in such digital integrated signal processing circuits, whereby in block  15  the Fast Fourier Transformation (FFT) processing is taking place, namely for all the M samples of an incoming noisy signal x(n) during one sampling period, giving M FFT values X(n,k), whereby n may be called a ‘discrete time variable’ for x(n) and k named as a ‘normalized frequency number or index’ in case of X(n,k). These M results X(n,k)  35  are then carried altogether in parallel into the Noise Reduction Processing Unit  55  for their further processing to achieve the desired “noise free” resulting signal s(n), whereby the calculations for all frequency numbers are done all at once, which is very time consuming and thus causing considerable delay for the processing of a whole data set due to the many calculations needed. As can also be seen substantial computing power in blocks  15  and  55  is needed for all these necessary calculations. 
   It is therefore a challenge for the designer of such methods and circuits to achieve a high-quality and low-cost solution. Several prior art inventions referring to such solutions describe related technologies, methods and circuits. 
   U.S. Pat. No. 6,208,951 (to Kumar et al.) describes a method and an apparatus for the identification and/or separation of complex composite signals into its deterministic and noisy components with a given process for the identification and/or separation of composite signal into its deterministic and noisy components wherein the process uses recursive wavelet transformations to separate the deterministic and noisy components of signals and uses the difference in the properties with regard to degree of correlation and dimensionality of these constituent components as a basis for separation, the said process of identification and/or separation has application in a variety of situations where digitized data is made available via an apparatus which converts the monitored signals. 
   U.S. Pat. No. 6,502,067 (to Hegger et al.) discloses a method and apparatus for processing noisy sound signals including a method for processing a sound signal y in which redundancy, consisting mainly of almost repetitions of signal profiles, is detected and correlations between the signal profiles are determined within segments of the sound signal. Correlated signal components are allocated to a power component and uncorrelated signal components to a noise component of the sound signal. The correlations between the signal profiles are determined by methods of nonlinear noise reduction in deterministic systems in reconstructed vector spaces based on the time domain. 
   Canadian Patent CA 02319995 (to Ruwisch) discloses a method and apparatus for suppressing audible noise in speech transmission by means of a multi-layer self-organizing fed-back neural network. This method involves using a multi-layer self-organising neural network with feedback. A minima detection layer, a reaction layer, a diffusion layer and an integration layer define a filter function (F(f,T)) for noise filtering. The filter function is used to convert a spectrum B(f, T) free of noise, into a noise-free speech signal (y(t)) by inverse Fourier transformation. The signal delay caused by processing the signal is so short that the filter can operate in real-time for telecommunication. All neurons are supplied with an externally set parameter K, the size of which defines the degree of noise suppression of the whole filter. An Independent claim is included for an apparatus for noise suppression during speech transmission. 
   The Ph.D. thesis of Hyoung-Gook Kim, “Background Noise Reduction Based on Diffusive Gain Factors and 1.2 kbit/s Low Bit Rate Speech Coding Using Spectral Vector Quantization of Differential Features, Technische Universität Berlin, Fachbereich Elektrotechnik und Informatik, Berlin 2002, D83” describes a novel method which uses a background noise reduction with the help of a minimum detection stage, a stage for the estimation of the noise and a computation stage based on Diffusive Gain Factors (DGF). The circuit developed for this method has however a rather high demand for processing power. 
   Although these papers describe methods close to the field of the invention they differ in essential features from the method and especially the circuit introduced here. 
   SUMMARY OF THE INVENTION 
   A principal object of the present invention is to provide an effective method implementable with the help of very manufacturable integrated circuits for a noise suppressing system for sound signals. 
   An object of the present invention is thereby to establish an especially adapted method for sound signals containing human speech. 
   Also an object of the present invention is thereby to include into said adapted method for speech signals a means to avoid unwanted artifacts, e.g. “musical tones”. 
   A further object of the present invention is to allow for an implementation with modern digital signal processors by use of the appropriate design features of said method. 
   Also an object of this invention is to reduce the necessary processing time by use of sophisticated algorithms for the noise suppression, thus rendering the circuit capable for real-time operations. 
   Equally an object of this invention is to reduce the necessary processing time to such an extent, that the operation of the circuit can be called delay-free under real-time conditions. 
   Another important object of the present invention is to reduce the overall processing power demands in conjunction with reduced memory requirements by exploiting the inherent design features relating to a set of M incoming data samples x(n), their according spectra X(k) calculated with the help of a Discrete Fourier Transform (DFT) algorithm and the use of Noise Gain Factors (NGF), whereby only one NGF out of a set of M NGFs is processed, selected via an ‘n modulo M’ rule where M is a power of 2 (as required by the DFT algorithm) and relating to selecting each frequency number k at least once within said set of M incoming data samples x(n) and thus allowing to economically process a noise free set of M output signal values s(n) without any significant delay. 
   A still further object of the present invention is to reduce the power consumption of the circuit by realizing inherent appropriate design features. 
   Another further object of the present invention is to reduce the cost of manufacturing by implementing the circuit as a monolithic integrated circuit in low cost CMOS technology. 
   Another still further object of the present invention is to reduce cost by effectively minimizing the number of expensive components. 
   In accordance with the objects of this invention, a method is achieved, describing in detailed steps an algorithm and its implementation units for a ‘Delay Free Noise Suppression’, capable of generating a noise reduced—‘noise free’—output sound signal out of a noise polluted input sound signal, where said method steps are dealing with signals, both time signals and sampled signals, their corresponding spectrum data words and the essential Noise Gain Factor (NGF) values, and are further dealing with the respective output spectrum data, as provided by the algorithm of said method. Said method is then delivering the desired noise canceled output signal. Said method therefore comprises steps for preparing the processing of received noisy speech input signals—from an A/D converter—representing a series of digitized words of sound sample data in form of an input data stream; receiving a data stream of sound samples for an according, consecutively described “Sample-Wise Discrete Fourier Transformation” calculation step; further a step for calculating the spectrum of said sound samples, exemplified for a single sample and performed in a “Sample-Wise Discrete Cosine Transformation” unit, resulting in parallel data words, describing the spectrum of said sound sample and therein optionally performing a Hann windowing in the frequency domain i.e. on the data words of the spectra; also further steps for delivering said spectrum data words via a “Multiplexer” unit in parallel into “Multipliers”, part of a “Noise Canceling Multiplier” unit, and clocking serially in a data stream of said spectra into a “Minimum Detection” unit and processing said serial spectrum data words in order to evaluate the minimum value for that signal sample; then feeding said minimum spectrum value into a “Noise Gain Factor Calculation” unit and receiving said input values in said “Noise Gain Factor Calculation” unit, which possesses a total of four inputs: input #1 for said minimum spectrum value, input #2 for a Filter Strength value, separately evaluated and furnished, input #3 for an average Noise Gain Factor (NGF) value furnished from an “Average Calculation” unit, and input #4 for a series of previous NGF values, clocked in from a “Noise Canceling Multiplier Table” unit, part of said “Noise Canceling Multiplier” unit. Also included are steps for calculating in said “Noise Gain Factor Calculation” unit out of the four input signals a new series of NGF values and feeding said new series of NGF values via a “Synchronous Signal Detection” unit into said “Noise Canceling Multiplier Table” unit of said “Noise Canceling Multiplier” unit as well as feeding said new series of NGF values into said “Average Calculation” unit as input values, and switching said new series of NGF values through to said “Noise Canceling Multiplier Table” unit as multiplication factors into said “Multipliers” of said “Noise Canceling Multiplier” unit. Comprised is further the multiplication of said new series of NGF values with said according spectrum data words of said noisy speech input signal and thus generating with said multiplication process of said spectrum data words with said NGF values a new set of noise canceled data values, which are then reversely transformed within said “Inverse Sample-Wise Discrete Cosine Transformation” unit into a noise canceled speech signal. Also included is the transmission of said noise free speech output signals, represented as a series of digitized words of sound sample data into a D/A converter for the final conversion into the desired noise free speech signal. 
   Also in accordance with the objects of this invention, an apparatus, implementing a new method is achieved, realizing a modern digital system for a ‘Delay Free Noise Suppression’ operating on analog input signals and delivering analog output signals, hereby digitally processing sound signals or—more specific—speech signals, thereby using a means specialized to realize a noise suppression method essentially based upon “Sample-Wise Discrete Cosine Transformation (DCT) and Spectral Minimum Detection (SMD) with Noise Gain Factors (NGF)” algorithms. An apparatus comprising a first circuit block, wherein the analog input signal—representing the noise polluted speech signals continuously converted into a digital input data stream of noisy sound samples; a second circuit block containing a digital signal processing system processing said digital data stream x(n) of noisy sound samples using said method for the ‘delay free’ noise suppression or cancelation for speech signals with “Sample-Wise Discrete Cosine Transformation (DCT) and Spectral Minimum Detection (SMD) with Noise Gain Factors (NGF)” algorithms essentially consisting of three parts: first a “Sample-Wise Discrete Cosine Transformation” part and second the “Spectral Minimum Detection (SMD) with Noise Gain Factors (NGF)” part and third an “Inverse Sample-Wise Discrete Cosine Transformation” part; and a third circuit block reconverting said processed digital output data stream s(n) of noise free sound samples—representing the noise canceled sound signal—back into said analog output signal, which is the desired noise canceled speech signal. 
   Furthermore in accordance with the objects of this invention, a circuit is achieved, implementing the new method of the invention, a circuit realizing within a modern digital system for a ‘Delay Free Noise Suppression’ a noise suppression method essentially based upon “Sample-Wise Discrete Cosine Transformation (DCT) and Spectral Minimum Detection (SMD) with Noise Gain Factors (NGF)” algorithms, hereby digitally processing sound signals or more specific, speech signals—where the noisy speech input signal is represented as a series of continuously digitized words of sound sample data, a data stream ready for being processed by said circuit. Said circuit comprises a circuit block, named “Sample-Wise Discrete Cosine Transformation” unit, possessing one serial data input line and a set of parallel data output lines, receiving said data stream of sound samples—on said serial data input line—for the according “Sample-Wise Discrete Cosine Transformation (DCT)” calculation step of said algorithm, resulting in data words, describing the spectrum of that sound sample; it comprises also a circuit block, named “Digital Signal Processing (DSP) System for Noise Suppression with Spectral Minimum Detection (SMD) with Noise Gain Factors (NGF)” consisting of a digital signal processor system implementing a noise suppression algorithm, whereby said incoming data stream of spectrum data words is transformed into the desired noise canceled outgoing data stream of output data words for the ‘noise free’ spectra, whereto Noise Gain Factors (NGF) are calculated according to an estimation rule for the noise floor, as evaluated with the help of said Spectral Minimum Detection (SMD) algorithm and an added Filter Strength factor with values between 0.0 (no filtering at all) and 1.0 (maximum filter strength) accounts for deviations from a standard rule i.e. sudden changes of said noise floor e.g. and where said Filter Strength value can be chosen as a constant or can be dynamically varied by using a nonlinear function between the filter strength and the averaged Noise Gain Factors. Furthermore comprised is a circuit block, named “Inverse Sample-Wise Discrete Cosine Transformation” unit, which reversely transforms said noise canceled output spectrum data values back into the noise canceled speech signal, possessing a set of parallel data input lines and a serial data output line for delivering said noise canceled speech signal. 
   Finally in accordance with the objects of this invention, a circuit is achieved, also implementing the new method of the invention, a circuit realizing within a modern digital system for a ‘Delay Free Noise Suppression’ a noise suppression method essentially based upon “Sample-Wise Discrete Cosine Transformation (DCT) and Spectral Minimum Detection (SMD) with Noise Gain Factors (NGF)” algorithms, hereby digitally processing sound signals or more specific, speech signals—where the noisy speech input signal is represented as a series of continuously digitized words of sound sample data, a data stream ready for being processed by said circuit. Said circuit comprises a first circuit block, named “Sample-Wise Discrete Cosine Transformation” unit, possessing one serial data input line and a set of M parallel data output lines, receiving said data stream of sound samples—on said serial data input line—for the according “Sample-Wise Discrete Cosine Transformation (DCT)” calculation step of said algorithm, resulting in a set of data words, describing the spectrum of that sound sample. Said circuit also comprises several other circuit blocks, one named “Multiplexer” unit, possessing a set of parallel data input lines and another set of parallel data output lines and also one serial data output line, whereby said set of parallel data input lines connects to said “Sample-Wise Discrete Cosine Transformation” unit, and said set of parallel data output lines connects to a consecutively defined set of “Multipliers”, and said serial data output line connects to a consecutively defined “Minimum Detection” unit; another circuit block named “Noise Canceling Multiplier” unit, consisting of a set of “Multipliers” and a “Noise Canceling Multiplier (NCM) Table” and serving as the central processing block for said algorithm thereby calculating the desired noise canceled output spectrum data values with the help of said consecutively evaluated Noise Gain Factor (NGF) values, possessing a set of parallel data input lines and a set of parallel data output lines as well as one serial data input line and one serial data output line, whereby said set of parallel data input lines connects to said “Multiplexer” unit and said set of parallel data output lines connects to a consecutively defined “Inverse Sample-Wise Discrete Cosine Transformation” unit, and whereby said one serial data input line connects to a consecutively defined “Synchronous Signal Detection” unit and said serial data output line connects to a consecutively defined “Noise Gain Factor Calculation” unit; one more circuit block, named “Minimum Detection” unit, possessing a serial data input line and a serial data output line, whereby said serial data input line connects to said “Multiplexer” unit and said serial data output line connects to said “Noise Gain Factor Calculation” unit; and another circuit block, named “Noise Gain Factor Calculation” unit, essentially responsible for the calculations for said Noise Gain Factor (NGF) values, possessing a total of four serial data input lines and one serial data output line, whereby the first serial data input line connects to said “Noise Canceling Multiplier” unit, and the second serial data input line connects to said “Minimum Detection” unit, and the third serial data input line connects to a consecutively defined “Average Calculation” unit, and the fourth serial data input line connects to an optional and separately furnished Filter Strength value signal and whereby said serial data output line connects to said “Synchronous Signal Detection” unit; furthermore a circuit block, named “Average Calculation” unit, possessing a serial data input line and a serial data output line whereby said serial data input line connects to said “Synchronous Signal Detection” unit and said serial data output line connects to said “Noise Gain Factor Calculation” unit; also another circuit block, named “Synchronous Signal Detection” unit, possessing a serial data input line and a serial data output line whereby said serial data input line connects to said “Noise Gain Factor Calculation” unit and said serial data output line connects to said “Average Calculation” unit as well as said “Noise Canceling Multiplier” unit; and finally a circuit block, named “Inverse Sample-Wise Discrete Cosine Transformation” unit, which reversely transforms said noise canceled output spectrum data values back into the noise canceled speech signal, possessing a set of parallel data input lines and a serial data output line for delivering said noise canceled speech signal. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     In the accompanying drawings forming a material part of this description, the details of the invention are shown: 
       FIG. 1A  prior art and  FIG. 1B  illustrate the significant difference in the operation of the essential circuit blocks between prior art realizations and an embodiment for the present invention. 
       FIG. 2  shows the building blocks for the preferred embodiment of the present invention i.e. the block diagram of the noise suppression system. The block diagram shows the essential circuit blocks realizable with a variety of modern monolithic integrated-circuit technologies. 
       FIGS. 3A and 3B  depicts in form of a frequency diagram the influence of a suddenly appearing noisy disturbance, called a siren, on the values of the Noise Gain Factor (NGF) compared to the normal white noise behavior. 
       FIG. 4  and  FIGS. 5A-5C  show in form of a block diagram and a flow diagram an apparatus for the implementation of the invention. 
       FIG. 6A-6F  show in form of a flow diagram the method implemented with the electrical circuit as shown in  FIG. 2 . 
   

   DESCRIPTION OF THE PREFERRED EMBODIMENTS 
   The preferred embodiments disclose a novel method for an implementation of a real-time noise-suppressing algorithm using modern integrated digital circuits and an exemplary circuit thereto. 
   The description of the preferred embodiments of the invention is subdivided into two steps; first an overall description of the whole implementation and its constitutive method is given and second a more detailed description of the underlying theory is presented, whereon said method is based upon. 
   The approach followed here is to some extent already known as a method based on spectral subtraction and described elsewhere in the pertinent literature. It is a simple but very effective psychoacoustically motivated real-time approach essentially based upon a one-channel noise reduction with spectral subtraction technique and as such apt to achieve a well-balanced trade-off between noise reduction and speech distortion. The new method is derived from a concept called more precisely “Spectral Minimum Detection (SMD) with Diffusive Gain Factors (DGF)”. The invention claimed here consists in a simpler and more effective algorithm, using a sample-wise applied Discrete Fourier Transformation (DFT) with simplified calculation formulas and thus making possible a real-time implementation with virtually no delays. Furthermore the method for calculating the DGF is varied and optimized as a new calculation method for Noise Gain Factors (NGF), perfectly fitting into the sample-wise DFT processing scheme. 
   As a comprehension aid the following list is compiled and presented here, and so consequently showing the variables in order of their logical appearance within the various descriptions. An introductory remark shall be made concerning sampled digital signals S d  (n), where n is the current running index or counter of the sample and also concerning its assigned frequency spectrum S d  (n, k), obtained by applying a Discrete FOURIER Transformation or DFT-algorithm, thus giving k discrete resulting frequency lines; the subscript d alluding formally to the application of a discrete Fourier transform algorithm with frequency number k used as its current summation index and the number M defining the number of samples necessary for the DFT calculation, and required to be a power of 2. 
   LIST OF REFERENCES FOR THE VARIABLES USED WITH EXPLANATIONS 
   
       
       
         
           x(t) continuous and analog noisy input signal 
           t time 
           x(n) continuous stream of sound samples with current index n 
           n discrete time variable as running or counting index in case of x(n) 
           k (normalized) frequency number as running index in case of X(n) which physically spoken is not a frequency itself, but a number representing a frequency 
           X(n) complex DFT-spectrum represented simplified as frequency band for data sample x(n) with current index n 
           X(n,k) complex DFT-spectrum represented as frequency band for data sample x(n) with current discrete time variable as index n and frequency number k as index k 
           X real  real part of amplitude value for X(n) 
           X imag  imaginary part of amplitude value for X(n) 
           M number of frequency bands in data set, necessarily a power of 2-by reason of FFT/DFT algorithm—and its value depending on frequency range, time frame, sampling rate and desired resolution 
           X(0) to X(M-1) set of M frequency bands, named as single data items 
           X(n&amp;(M-1)) selected data sample out of the data set of M frequency bands, assigned to sample x(n) via an ‘n modulo M’ rule, M being a power of 2 
           X min (n) absolute minimum of amplitude values for data set X(M) 
           X min (n&amp;(M-1)) selected data sample out of the data set representation thereof 
           N(0) . . . N(M-1) Noise Gain Factors 
           N(n&amp;(M-1)) selected data sample out of the data set representation thereof 
           S(0) . . . S(M-1) noise reduced complex DFT-spectrum values 
           s(n) sample of desired noise free output signal, assigned to index n 
           S(f) according continuous complex DFT-spectrum, assigned to frequency f 
           f natural frequency 
           s(t) continuous and analog noise free output signal 
         
       
     
  
   The denomination n&amp;(M-1) thereby signifying a selection process, generating data associated to a ‘logical and’ combination of the discrete time variable as running or counting index n with the M FFT calculated data corresponding to said DFT-spectrum values, observing an ‘n modulo M’ rule, which guarantees that there is only one non-ambiguous and permitted choice possible and valid. Mathematically is ‘n modulo M’ defined as the integer remainder, resulting of a divisional operation of integer n by integer M, e.g. n=9 and M=4 leading to a division result=2 with division remainder=1 thus ‘9 modulo 4’=1. 
   Looking at  FIG. 1B  the most significant difference in the operation of the essential processing stages between prior art realizations and an embodiment for the present invention is demonstrated. Within block  10  the Sample-Wise Fast Fourier Transformation (FFT) processing is recursively performed; namely at each sample n all the M spectrum values for an incoming noisy signal x(n) during one sampling period are calculated using the recursion formulas (1.3a&amp;b) given later, producing M FFT values X(n,k), whereby n is a ‘discrete time variable’ in x(n) and k the ‘normalized frequency number or index’ in X(n,k). Out of these M results X(n,k)  30  fed together in parallel into the Multiplexer block  30  one value X(n,k) is selected by said multiplexer  30  and put into the Noise Reduction Processing Unit  50  for its further processing to achieve the desired “noise free” resulting signal s(n), whereby the calculations for only one frequency number k has to be done at the same time, which is very time economic and thus leading to real-time results with virtually no significant delay. However it has to be guaranteed, that every frequency number k is selected at least once within a time frame or data set of M incoming samples. Compared to prior art solutions (as already described with the help of  FIG. 1A  prior art) the recursive FFT calculation algorithms and the selection schemes introduced by the multiplexer are the new key points of this invention. 
   Referring now to the elements in  FIG. 2 , the preferred embodiment of the circuit implementing the method of the present invention is illustrated. The essential functional components, so called processing units of the digital circuit together with two symbols, representing the input (item  100 ) and output (item  500 ) speech signals, are shown as items  150  to  550 , which are explained below in more detail in the section entitled “Description of the Processing Units for the Delay Free Noise Cancelation System”. Subsequently explained is the cooperation of these processing units, in order to realize said new method of the invention for the noise suppression or even noise cancelation described in the section “Spectral Minimum Detection (SMD) with Noise Gain Factors (NGF)”, see the flow diagrams in  FIGS. 5A-5C  and  FIGS. 6A-6F . Said method is derived from a pertinent theoretical background, the relevant formulas thereof are also given and explained in the following mathematical insertion, explaining the algorithms used, with formulas (1.0), (1.1), (1.2a&amp;b), (1.3a&amp;b), (1.4), (1.5) and (1.6a&amp;b) and all contained in the last section about the underlying theory named “Theory of the Sample-Wise Discrete Cosine Transformation (DCT)”. 
   Reverting now to  FIG. 2 , in symbol  100  the noisy speech input signal is represented, namely as a series of already digitized words of sound sample data—a so called data stream x(n), ready for being processed according to the method of the invention in the following sample-wise calculation. Unit  150 , named “Sample-Wise Discrete Cosine Transformation” receives this data stream of sound samples x(n) for the according sample-wise Discrete Fourier Transformation calculation step, resulting in M data words X(0) to X(M-1), describing the spectrum of that sound sample x(n). As an option here, Hann windowing in the frequency domain can be additionally performed. These M spectrum data words X(0) to X(M-1) are then delivered via a “Multiplexer”  210  in parallel into M multipliers  230 , part of the “Noise Canceling Multiplier” unit  225  and serially clocked into a “Minimum Detection” unit  260  selected as per X(n&amp;(M-1)). These serial spectrum data words X(n&amp;(M-1)) are therein processed to evaluate the minimum value X min (n&amp;(M-1)) for that signal sample, which is thus fed into the “Noise Gain Factor Calculation” unit  250 . This “Noise Gain Factor Calculation” unit  250  possesses a total of four inputs, receiving as input values besides X min (n&amp;(M-1)), a Filter Strength value (item  300 ), which is separately evaluated, an average Noise Gain Factor (NGF) value furnished from an “Average Calculation” unit  270 , and a series of previous NGF values selected as per N(n&amp;(M-1)), clocked in from the “Noise Canceling Multiplier Table” unit  220 , part of the “Noise Canceling Multiplier” unit  225 . Out of these four input signals a new series of NGF values N(n&amp;(M-1)) is then calculated and fed via a “Synchronous Signal Detection” unit  240  into the “Noise Canceling Multiplier Table” unit  220  of the “Noise Canceling Multiplier” unit  225 . These new series of NGF values still selected as per N(n&amp;(M-1)) is fed also into the “Average Calculation” unit  270  as input values. The new series of NGF values N(n&amp;(M-1)) is then switched through the “Noise Canceling Multiplier Table” unit  220  as multiplication factors N(0) to N(M-1) into the M “Multipliers” of the “Noise Canceling Multiplier” unit  225 , where the recent spectrum data words X(0) to X(M-1)—of the noisy speech input signal—are awaiting processing. The multiplication process of the spectrum data words X(0) to X(M-1) with the NGF values N(0) to N(M-1) then generates new, noise canceled data values S(0) to S(M-1), which are then reversely transformed in the “Inverse Sample-Wise Discrete Cosine Transformation” unit  550 , back into the noise canceled speech signal s(n), represented by symbol  500 . Summarizing some essentials we find, that the incoming samples of data stream x(n) are counted or enumerated using said discrete time variable n as counting index thus n appearing as a counter, and that all the noise reduction processing happens within a time frame defined by a set of M incoming samples x(n) using M Noise Gain Factors (NGF) determined by the new method of the invention. This method selects one NGF out of said set of M NGFs via said ‘n modulo M’ rule or if M is a power of 2 (as required by the DFT algorithm) the notion ‘n&amp;(M-1)’ selecting said respective NGF item, denoted N(n&amp;(M-1)). Within a complete cycle processing all M values X(n) by multiplying them with said NGF values N(n&amp;(M-1)) furnishes said set of M respective results S(n). The main problem solved hereby is to select each frequency number k at least once within said set of M incoming samples x(n) and thus delivering a noise free set of M output signal values s(n) without any significant delay. 
   Now delving again into  FIG. 2 , the following section describes the purpose and function of every unit in greater detail: 
   Description of the Processing Units for the Delay Free Noise Cancelation System. 
   “Sample-Wise Discrete Cosine Transformation” Unit: Item  150   
   According to the “Theory of the Sample-Wise Discrete Cosine Transformation (DCT)” the stream of sound samples x(n) is transformed into the Fourier spectrum at every sample. Formulas (1.3a) and (1.3b) are used for the transformation of x(n) into X(0) . . . X(M-1), where the X are split into their real and their imaginary parts X real  and X imag . The mathematical expressions of equations (1.3a) and (1.3b)—see below—are essentially new as derived later; the variables s &amp; S—generic for signal—solely being replaced by x &amp; X as used here. 
   “Multiplexer” Unit: Item  210 . 
   The “Multiplexer” unit  210  selects one (or more) of M frequency bands for each incoming sample and sends these selected values X(n&amp;(M-1)) to the “Minimum Detection” unit  260 . The succession of these selections is not important, but every frequency has to be selected at least once within each set of M incoming samples. Said M frequency bands are FFT values X(n,k) or simply X(n), whereby n is a ‘discrete time variable’ in x(n) and k the ‘normalized frequency number or index’ in X(n,k). Out of these M results X(n) fed together in parallel into the Multiplexer block  210  one value X(n) is selected by said multiplexer  210  according to X(n&amp;(M-1)) and put into the “Minimum Detection” unit  260 , whereby (n&amp;(M-1)) describes the above defined ‘n modulo M’ selection of frequency numbers k and which is why all the following calculations have to be done for only one frequency number k at the same time, therefore being very time economic and thus leading to real-time results with virtually no significant delay. However, as already stated, it has to be guaranteed, that every frequency number k is selected at least once within a time frame or data set of M incoming samples. 
   “Minimum Detection” Unit: Item  260 . 
   The “Minimum Detection” unit  260  detects the absolute minimum of the amplitude value of X(n) for each frequency band for a period of a few hundred milliseconds in the past. Therefore a history buffer with at least two values for each frequency band has to be used. Each value contains the minimum for a certain section of time and the absolute minimum for the whole period is the absolute minimum of all values for each frequency. The length of the whole period depends on the application, but normally values between 100 (better  300 ) ms and 1000 (better  800 ) ms are used. For a better performance the value sets coming from the “Multiplexer” unit  210  are to be averaged for a short time (˜80 ms). The absolute minimum X min (n) is sent to the “Noise Gain Factor Calculation” unit  250 . 
   “Noise Gain Factor Calculation” Unit: Item  250   
   The X min (n) value can be defined as the energy of the noise floor and has to be subtracted from the noisy speech signal. For a better quality of the noise reduction it is possible to calculate a Noise Gain factor N(n), which can be multiplied to the Fourier components instead of subtracting X min (n) from X(n). So if S(n) is the desired noise free spectrum
 
 S ( n )= X ( n )− X   min ( n )= N ( n )* X ( n ), then
 
 N ( n )=1.0 −X   min ( n )/ X ( n ) for all  X ( n )!=0
 
is the resulting Noise Gain factor. Because X min  is only an estimation of the noise floor, it is useful to add a Filter Strength factor with values between 0.0 (no filtering at all) and 1.0 (maximum filter strength) to the formula, so that
 
 N ( n )=1.0−X min ( n )/ X ( n )*Filter Strength for all  X ( n )!=0.
 
   This Filter Strength value can be chosen as a constant or can be dynamically varied by using a nonlinear function between the filter strength and the averaged Noise Gain Factors N(0) . . . N(M-1) coming from the “Average Calculation” unit  270 . At least the Noise Gain Factor N(n) should be averaged for a better performance and is sent to the “Synchronous Signal Detection” unit  240 . 
   “Synchronous Signal Detection” Unit: Item  240   
   The “Noise Gain Factor” method has the property, that if the neighbor frequencies reduce the speech signal, it is impossible that the actual observed and treated frequency is not reduced by the noisy speech signal. The multiplication factors of the Noise Canceling Multipliers are 1 if the signal is mainly speech in the corresponding frequency band, smaller than 1 if there is speech and noise in the corresponding frequency band and 0 if there is only noise in the corresponding frequency band. 
   With the help of  FIGS. 3A and 3B  an important phenomenon with regard to noise reduction will now be described in greater detail. There are two different classes of noise: white noise and sirens. Most background noises behave like noise out of one of these classes. “White noise”: all frequency bands have similar signal to noise ratio and therefore the multiplication factors of the Noise Canceling Multipliers in the neighborhood are very similar (and lower than 1). “Siren signals”: One frequency band has the whole noise energy; the neighbor frequencies have much smaller energy. The multiplication factor of the Noise Canceling Multiplier of this frequency band is much lower than the multiplication factors at the neighbor frequencies.  FIG. 3A  and  FIG. 3B  illustrate the results achieved with an apparatus, which puts the noise suppression method of the invention into practice with an exemplary realization. The significance of the Noise Gain Factor (NGF) can be clearly observed. What never happens in the real world is that the multiplication factor of the Noise Canceling Multiplier of one frequency band is much higher than the neighbor multiplication factor, because that would signify, that there is a noise floor everywhere else, except in one frequency band. But this effect happens if the algorithm detects in a noise floor (unwanted) modulation frequencies of speech, which leads to so-called “musical tones”. 
   The “Synchronous Signal Detection” unit  240  takes care of it and reduces the multiplication factor of the Noise Canceling Multiplier to make sure, that no musical tones appear. In the case of an estimation failure it is possible, that this situation may occur and these so-called “musical tones” can be heard, which are fundamentally unwanted artifacts. The “Synchronous Signal Detection” unit  240  detects such situations by comparing the neighbor frequencies and reduces this effect, as described above. The newly calculated Noise Gain Factor replaces the old value in the buffer of the “Noise Canceling Multiplier” unit  225  and the value is sent additionally to the “Average Calculation” unit  270 . 
   “Average Calculation” Unit: Item  270 . 
   The “Average Calculation” unit  270  calculates the average about all Noise Gain Factors N(0) . . . N(M-1). This value can then be used for a dynamic adjustment of the Filter Strength value. 
   “Noise Canceling Multiplier” Unit: Item  225 . 
   The “Noise Canceling Multiplier” unit  225  contains a buffer for all Noise Gain Factors additionally to its internal serial/parallel converter, thus forming a “Noise Canceling Multiplier Table” unit (item  220 ). The “Noise Canceling Multiplier” unit  225  is responsible for the subtraction of the noise by multiplying each Noise Gain Factor N(n) with the corresponding X(n), using e.g. M multipliers (items  230 ). The result is the wanted noise reduced speech signal S(n). It is further possible to integrate an amplification of the speech signal to compensate for the energy loss resulting from the subtraction of the noise energy. Such a virtually noise canceled speech signal output can be reached. 
   “Noise Canceling Multiplier Table” Unit: Item  220 . 
   The “Noise Canceling Multiplier Table” unit  220  contains some sort of registers or memory cells organized in form of a table for all processed Noise Gain Factors delivered from the “Synchronous Signal Detection” unit  240  as an intermediate storage area for the “Noise Gain Factor Calculation” unit  250  and the serial/parallel converter handles the allocation of the sequentially provided Noise Gain Factors to the appropriate multipliers  230  of the “Noise Canceling Multiplier” unit  225 . At each incoming sample one (or more) Noise Gain Factors are recalculated and stored back into the table. 
   “Inverse Sample-Wise Discrete Cosine Transformation” Unit: Item  550 . 
   The last step in the calculation is the inverse Fourier transformation that is done in the “Inverse Sample-Wise Discrete Cosine Transformation” unit  550 . According to the “Theory of the Sample-Wise Discrete Cosine Transformation” the noise reduced spectrum S(0) . . . S(M-1) coming from the “Noise Canceling Multiplier” unit  225  will be transformed into the next sample s(n) of the output signal. The new and important equation (1.6a)—see below—is used for this calculation. It is further possible to integrate a definable delay into the output by changing the phases of each frequency value. Therefore it is possible to get the same processing delay for every sampling rate. 
   Regarding the two diagrams in  FIG. 4  and in  FIGS. 5A-5C  and in order to clarify the function and the cooperation of the above described units the following section describes the new and governing method of the invention in more detail: first a block diagram for a standard implementation is given in  FIG. 4  and second a flow diagram for the essential methodic steps of the noise suppression algorithm implemented therein is presented with  FIGS. 5A-5C . 
   Referring now to the overall block diagram of  FIG. 4  the general principle for an apparatus realizing a modern digital system operating on analog input signals and delivering analog output signals is shown. Hereby digitally processing sound signals or even more specific speech signals and using a means specialized to realize the delay free noise suppression method of the invention. 
   It is understood and common knowledge to any skilled artisan in this field, thus only inserted here for clarity and definition of terms, that each electronical communication system dealing with sound transmission such as phones, sound transceivers or recorders has to make use of a physical sound transformation into analog electric signals by the help of microphones or acoustical oscillation receivers summarized as sound sensors and used as physical input device, whereas on the output side of that electronical communication system it is retransforming its analog electric output signals again into physical sound by general sound actors such as loudspeakers or acoustical earpieces used as physical output device. 
   In the starting block  620  the analog input signal  622 —representing the noise polluted speech signal—is converted to a digital data stream using well-known sampling and Analog/Digital (A/D) conversion techniques. Block  600  contains as a whole the digital signal processing system wherein the new method for the delay free noise suppression or cancelation for speech signals—represented as digital data streams—is implemented. This new method essentially consists of three parts: first a “Sample-Wise Discrete Cosine Transformation” part and second the “Spectral Minimum Detection (SMD) with Noise Gain Factors (NGF)” part and third an “Inverse Sample-Wise Discrete Cosine Transformation” part. The final block  630  then reconverts the processed digital data stream—representing the noise free speech signal—back into the analog output signal  633 , which is the desired noise free speech signal, using well-known Digital/Analog (D/A) conversion techniques. 
   Referring now to  FIGS. 5A-5C , the contents from within block  600  is described with the help of a flow diagram, detailing said noise suppression method and their implementation units. Said method implemented in the apparatus of the invention is explained in single steps, referring to the units shown in and explained with the help of  FIG. 2  and in the explanations given above. These method steps are dealing with signals, both time signals x(t) and sampled signals x(n), their corresponding spectrum data X(0) to X(M-1), and essentially the Noise Gain Factor (NGF) values N(n&amp;(M-1)), key values for the whole algorithm of said method; where the symbolic argument n&amp;(M- 1 ) signifies the particular value, associated to a ‘logical and’ combination of said running or counting index n of said input signal stream and the respective spectrum data of said M spectral data words, as provided by the already introduced multiplexer. 
   A first step  601  in said method prepares for the processing of received noisy speech input signals x(t)—from an A/D converter—represented as a series of digitized words of sound sample data—data stream x(n) represented by symbol  100 —according to the method of the invention in the following sample-wise calculation, exemplified for a single sample x(n), the second step  602  then receives data stream sample x(n) of sound samples x(n) for the according sample-wise Discrete Fourier Transformation calculation step, performed in the “Sample-Wise Discrete Cosine Transformation” unit  150 , resulting in M parallel data words X(0) to X(M-1), describing the spectrum of sound sample x(n). The next operational steps ( 603 - 607 ) of said method optionally perform a Hann windowing in the frequency domain i.e. on the M data words X(0) to X(M-1), deliver said M spectrum data words X(0) to X(M-1) via “Multiplexer” unit  210  in parallel into the M multipliers  230 , part of the “Noise Canceling Multiplier” unit  225 , are serially clocking in the data stream of selected values X(n&amp;(M-1)) into said “Minimum Detection” unit  260  and process said M serial spectrum data words X(n&amp;(M-1)) to evaluate the minimum value X min (n&amp;(M-1)) for that signal sample x(n). The following step of method  608  feeds said minimum spectrum value X min (n&amp;(M-1)) into the “Noise Gain Factor Calculation” unit  250 . Another following step of method  609  then receives the input values in the “Noise Gain Factor Calculation” unit  250 , possessing a total of four inputs: input 1 for minimum spectrum value X min (n&amp;(M-1)), input 2 for a Filter Strength value (item  300 )—separately evaluated—, input 3 for an average Noise Gain Factor (NGF) value furnished from “Average Calculation” unit  270 , and input 4 for a series of previous NGF values N(n&amp;(M-1)), clocked in from the “Noise Canceling Multiplier Table” unit  220 , part of the “Noise Canceling Multiplier” unit  225 . Calculating in said “Noise Gain Factor Calculation” unit  250  out of the four input signals a new series of NGF values N(n&amp;(M-1)) is accomplished in this step  610 . The now two following steps ( 611  &amp;  612 ) feed the new series of NGF values N(n&amp;(M-1)) via “Synchronous Signal Detection” unit  240  into the “Noise Canceling Multiplier Table” unit  220  of the “Noise Canceling Multiplier” unit  225  and feed this new series of NGF values N(n&amp;(M-1)) also into “Average Calculation” unit  270  as input values. The next two steps of the method ( 613  &amp;  614 ) are switching through the new series of NGF values N(n&amp;(M-1)) to the “Noise Canceling Multiplier Table” unit  220  as multiplication factors N(0) to N(M-1) into the M multipliers of the “Noise Canceling Multiplier” unit  225 , and multiply the new series of NGF values N(n&amp;(M-1)) with the according spectrum data words X(0) to X(M-1) of the noisy speech input signal and generate with this multiplication process of the spectrum data words X(0) to X(M-1) with the NGF values N(0) to N(M-1) the new, noise canceled data values S(0) to S(M-1). A separate step  615  reversely transforms in the “Inverse Sample-Wise Discrete Cosine Transformation” unit  550  out of the new, noise canceled data values S(0) to S(M-1) the noise canceled speech signal s(n), represented by symbol  500 . Preparing for the transmission of noise free speech output signals, represented as a series of digitized words of sound sample data—data stream s(n)—into a D/A converter for the final conversion into the noise free speech signal s(t) is the final step  616  of the method, as implemented by said apparatus of the invention. 
   Delving deeper now into the  FIGS. 6A-6F , an exceedingly detailed description of said method for noise suppression is presented somewhat more generally, however following the above introduced division into three parts: A “Sample-Wise Discrete Cosine Transformation” part (items  710  . . .  717 ), a “Spectral Minimum Detection (SMD) with Noise Gain Factors (NGF)” part (items  810  . . .  869 ), and an “Inverse Sample-Wise Discrete Cosine Transformation” part (items  910  . . .  999 ). 
   Said new method is starting off for part one with the first three steps  710 ,  715  &amp;  717 , which provide in step  710  a means for a “Sample-Wise Discrete Cosine Transformation”, wherein according to the “Theory of the Sample-Wise Discrete Cosine Transformation (DCT)” a continuous stream of sound samples x(n) is transformed all along into its Fourier spectrum X, represented by M frequency bands X(0) . . . X(M-1), and evaluated for every sample and wherein the Formulas (Re) and (Im)—as given and defined in the following two steps for the real and imaginary parts correspondingly—are used for the transformation of x(n) into X(0) . . . X(M-1); the X thereby split into their real and their imaginary parts, X real  and X imag ; n thereby being the running counter index of said continuous input stream of sound samples and M the number of frequency bands observed in said time frame, and which transform (step  715 ) within said means for a “Sample-Wise Discrete Cosine Transformation” sound sample x(n) into the real parts of the Fourier spectra X(0) . . . X(M-1) using as Formula (Re) for the transformation the following recursive Equation (1.3a)—as derived and explained later—
 
 Re: S   dreal,n ( k )= S   dreal,n-1 ( k )+( s   dreal ( n )− s   dreal ( n−M ))cos(2 πnk/M )  (1.3a)
 
where, in the mathematical expression—the variables s &amp; S—generic for signal—have to be replaced by x &amp; X as used here and already defined above, whereby d denotes the application of a discrete Fourier transform algorithm with k as its frequency number or index representing the discrete resulting frequency lines for the frequency band observed and also transform (step  717 ) within said means for a “Sample-Wise Discrete Cosine Transformation” sound sample x(n) into the imaginary parts of the Fourier spectra X(0) . . . X(M-1) using as Formula (Im) for the transformation the following recursive Equation (1.3b)—as derived and explained later—
 
 Re: S   dreal,n ( k )= S   dreal,n-1 ( k )+( s   dreal ( n−M )− s   dreal ( n ))sin(2 πnk/M )  (1.3b)
 
where, in the mathematical expression—the variables s &amp; S—generic for signal—have to be replaced by x &amp; X as used here and already defined above, whereby d denotes the application of a discrete Fourier transform algorithm with k as its frequency number or index representing the discrete resulting frequency lines for the frequency band observed.
 
   The now following twenty steps (items  810  . . .  869 ) for part two of said method are itemized as follows: step  810  provides a means for a “Multiplexer” unit, where the multiplexer selects one (or more) of said M frequency bands X(0) . . . X(M-1) for each of said incoming sound samples x(n) and provide this as part of a means for a “Spectral Minimum Detection (SMD) with Noise Gain Factors (NGF)”; step  820  provides a means for a “Minimum Detection” unit, detecting the absolute minimum of the amplitude value of X(n&amp;(M-1)) for each frequency band for a period of a few hundred milliseconds in the past; also as part of said means for “Spectral Minimum Detection (SMD) with Noise Gain Factors (NGF)”; step  815  compares within said “Minimum Detection” unit at least two values for each frequency band using a history buffer, where each value of said history buffer contains the minimum for a certain section of time and where the absolute minimum for the whole past period is the absolute minimum of all values for each frequency; step  817  detects for said past period within said “Minimum Detection” unit said absolute minimum of said amplitude values using for the length of the whole period values between 100 and 1000 ms, depending on the application; step  819  sends the values X(n&amp;(M-1)) from said “Multiplexer” unit to said “Minimum Detection” unit, whereby the order of which is not important, but every frequency has to be selected at least once within each set of M incoming samples; step  825  forms the average X min (n&amp;(M-1)) in said “Minimum Detection” unit for a short time (˜80 ms) and for each value X(n&amp;(M-1)) coming from said “Multiplexer” unit, in order to reach a better processing performance; step  830  provides a means for a “Noise Gain Factor Calculation” unit for processing the noise reduction algorithm, which defines an X min (n) value as the energy of the noise floor and which, as a matter of principle, has to be subtracted from the noisy speech signal; this also as part of said means for “Spectral Minimum Detection (SMD) with Noise Gain Factors (NGF)”; step  833  sends from said “Minimum Detection” unit the detected absolute minimum value X min (n&amp;(M-1)) to said “Noise Gain Factor Calculation” unit; step  835  calculates within said “Noise Gain Factor Calculation” unit a Noise Gain factor N(n) according to N(n)=10-X min (n)/X(n) for all X(n)!=0, which can be multiplied—for a better quality of the noise reduction—to the Fourier components X(0) . . . X(M-1) instead of X min (n) being subtracted from X(n); step  837  adds within said “Noise Gain Factor Calculation” unit an optional Filter Strength factor with values between 0.0 (no filtering at all) and 1.0 (maximum filter strength) to the N(n) calculation formula, so that N(n)=1.0-X min (n)/X(n)*Filter Strength for all X(n)!=0, where Xmin is an estimation of the noise floor; step  840  provides a means for an “Average Calculation” unit, wherein the average about all of said M Noise Gain Factors N(n)=N(0) . . . N(M-1) is calculated; this also as part of said means for “Spectral Minimum Detection (SMD) with Noise Gain Factors (NGF)”; step  843  forms an average for said Noise Gain Factor N(n) within said “Average Calculation” unit, again in order to reach a better processing performance; step  845  adjusts dynamically said optional Filter Strength value within said “Noise Gain Factor Calculation” unit using the average value N(n) as calculated by said “Average Calculation” unit; step  847  chooses said optional Filter Strength value e.g. as a constant or a dynamically varied variable by using a nonlinear function between the filter strength and the averaged Noise Gain Factors N(0) . . . N(M-1) coming from said “Average Calculation” unit; step  850  provides a means for a “Noise Canceling Multiplier” unit, wherein a “Noise Canceling Multiplier Table” means is contained, buffering all Noise Gain Factors calculated during one period additionally to according internal serial/parallel converters and where said “Noise Canceling Multiplier” unit is responsible for the subtraction of the noise by multiplying each Noise Gain Factor N(n) with the corresponding X(n), using e.g. M internal multipliers, delivering as result the M wanted noise reduced speech signal spectrum bands S(n)=S(0) . . . S(M-1) and this also as part of said means for “Spectral Minimum Detection (SMD) with Noise Gain Factors (NGF)”; step  860  provides a means for a “Synchronous Signal Detection” unit as part of said means for “Spectral Minimum Detection (SMD) with Noise Gain Factors (NGF)”, because the Noise Gain Factors N(0) . . . N(M-1) have the property, that if the neighbor frequencies reduce the speech signal, it is impossible, that the actual observed and treated frequency is not reduced by the noisy speech signal. The multiplication factors of said “Noise Canceling Multipliers” are 1 if the signal is mainly speech in the corresponding frequency band, smaller than 1 if there is speech and noise in the corresponding frequency band and 0 if there is only noise in the corresponding frequency band; step  863  detects irregular situations within said “Synchronous Signal Detection” unit by comparing the neighbor frequencies and reduce the effect of such situations, where the algorithm detects in a noise floor (unwanted) modulation frequencies of speech, which could lead to so called irregular ‘musical tones’, by reducing the multiplication factor of the corresponding ‘noise canceling’ multiplier to make sure that no ‘musical tones’ appear; step  865  sends said averaged Noise Gain Factor N(n), delivered by said “Noise Gain Factor Calculation” unit to said “Synchronous Signal Detection” unit and calculate a new Noise Gain Factor N(n&amp;(M-1)), which replaces the old value in the buffer of said “Noise Canceling Multiplier” unit and ensure, that said new value is sent additionally to the “Average Calculation” unit; step  867  stores intermediately said Noise Gain Factor (NGF) values within said “Noise Canceling Multiplier” unit in said means for a “Noise Canceling Multiplier Table”, which contains some sort of registers for all processed NGF values delivered from said “Synchronous Signal Detection” unit, and which is used as an intermediate storage area for said “Noise Gain Factor Calculation” unit and where the serial/parallel converter handles the allocation of the sequentially provided NGF values to the appropriate multipliers of said “Noise Canceling Multiplier” unit; and step  869  amplifies within or in conjunction with said means for a “Noise Canceling Multiplier” the speech signal to compensate for the energy loss resulting from the subtraction of the noise energy in order to reach a virtually noise canceled speech signal output. 
   Within the last five steps ( 910  . . .  999 ) for part three of said method, step  910  provides a means for an “Inverse Sample-Wise Discrete Cosine Transformation” unit, wherein the last step of the calculation, an inverse Fourier transformation is done according to the “Theory of the Sample-Wise Discrete Cosine Transformation”. Step  925  changes within or in conjunction with said unit for an “Inverse Sample-Wise Discrete Cosine Transformation” the phases of each frequency value in order to reach a definable delay in the output signal and therefore making it possible to get the same processing delay for every sampling rate and step  935  transforms within said “Inverse Sample-Wise Discrete Cosine Transformation” unit the M noise reduced spectrum bands S(0) . . . S(M-1) coming from the “Noise Canceling Multiplier” unit into the next sample s(n) of the wanted, noise free speech signal sample as output, obeying for this calculation to the Formula of Equation (Inv), which is given and defined in the following step  955 , which processes within said “Inverse Sample-Wise Discrete Cosine Transformation” unit the transformation of the entity of all M noise reduced spectrum bands S(0) . . . S(M-1) into a sample s(n) of said noise free output signal, using as Formula (Inv) for the transformation, whereby only the real signal part s dreal (n) is needed, the following Equation (1.6b)—as derived and explained later— 
                   Inv   ⁢     :       ⁢     
     ⁢         s   dreal     ⁡     (   n   )       =         2   M     ⁢       ∑     k   =   0         M   /   2     -   1       ⁢         S   dreal     ⁡     (   k   )       ⁢     cos   ⁡     (     2   ⁢   π   ⁢           ⁢   n   ⁢           ⁢     k   /   M       )             -         S   dimag     ⁡     (   k   )       ⁢     sin   ⁡     (     2   ⁢   π   ⁢           ⁢   n   ⁢           ⁢     k   /   M       )                     (     1.6   ⁢   b     )               
thus summing up all the spectral frequency lines designated by k running from 0 to (M/2)−1, considering said discretely calculated real and imaginary components S dreal  and S dimag  of the complex spectrum bands S. Step  999  finally supplies said continous stream of noise free digital output signal samples s(n) ready for its conversion into the desired noise free analog speech signal s(t) as a function in time t by recurring the appropriate processing loop for the complete algorithm from its beginning.
 
   For a better understanding of the invention the underlying theory is now summarized and briefly explained in the following section: 
   Theory of the Sample-Wise Discrete Cosine Transformation (DCT) 
   A short introduction for the mathematical background of the DFT method of the invention is given here, emphasizing on the newly derived equations (1.3a) and (1.3b), for the evaluation of the signal spectrum out of the noisy speech signal input, in the form of a sample-wise DCT. And further emphasizing on the new equation (1.6a) for the iDCT, as used for the retransformation of the noise canceled signal spectrum back into the clean speech signal output. 
   Based on the fact, that for continuous and analog signals s(t), i.e. functions of time t, like sound or especially speech signals the associated continuous spectrum S(f) over the frequency f can be calculated using the well known Fourier transformation, the application of modern digital integrated circuits and digital processing techniques leads to the use of sampled digital signals s d (n), where n is the index of the sample in a period of time. Calculating the according frequency spectrum S d (n) with the hereby applicable Discrete Fourier Transformation (DFT) gives discrete resulting frequency lines, which are defined through their index k. The number M defines the number of samples necessary for the DFT calculation and chosen corresponding to the observed signal&#39;s sample rate under consideration of Shannon&#39;s sampling theorem for signal fidelity, thus defines a resultant frequency range or frequency band for every signal sample. 
   Fourier Transformation for continuous analog signals: 
                   S   ⁡     (   f   )       =       ∫     -   ∞       +   ∞       ⁢       s   ⁡     (   t   )       ⁢     ⅇ       -   ⅈ2π     ⁢           ⁢   f   ⁢           ⁢   t       ⁢     ⅆ   t                 (   1.0   )               
The DFT form for sampled digital signals:
 
                     S   d     ⁡     (   k   )       =       ∑     n   =   0       M   -   1       ⁢         s   d     ⁡     (   n   )       ⁢     ⅇ       -   ⅈ2π     ⁢           ⁢   n   ⁢           ⁢     k   /   M                     (   1.1   )               
The DFT form in Euler&#39;s representation:
 
               S   d     ⁡     (   k   )       =         ∑     n   =   0       M   -   1       ⁢         s   d     ⁡     (   n   )       ⁢     (       cos   ⁡     (     2   ⁢   π   ⁢           ⁢   n   ⁢           ⁢     k   /   M       )       -     ⅈsin   ⁡     (     2   ⁢   π   ⁢           ⁢   n   ⁢           ⁢     k   /   M       )         )     ⁢           ⁢   0       ≤   k   &lt;     M   /   2             
or split into real and imaginary parts of the Discrete Cosine Transformation (DCT):
 
                     S   dreal     ⁡     (   k   )       =         ∑     n   =   0       M   -   1       ⁢         s   dreal     ⁡     (   n   )       ⁢     cos   ⁡     (     2   ⁢   π   ⁢           ⁢   n   ⁢           ⁢     k   /   M       )           +           s   dimag     ⁡     (   n   )       ⁢     sin   ⁡     (     2   ⁢   π   ⁢           ⁢   n   ⁢           ⁢     k   /   M       )           ︸     =   0                   (     1.2   ⁢   a     )                   S   dimag     ⁡     (   k   )       =         ∑     n   =   0       M   -   1       ⁢           s   dimag     ⁡     (   n   )       ⁢     cos   ⁡     (     2   ⁢   π   ⁢           ⁢   n   ⁢           ⁢     k   /   M       )           ︸     =   0           -         s   dreal     ⁡     (   n   )       ⁢     sin   ⁡     (     2   ⁢   π   ⁢           ⁢   n   ⁢           ⁢     k   /   M       )                   (     1.2   ⁢   b     )               
where S dimag (n) is 0 for all n. The Fourier transform, as used here, is only applied to one dimensional signals in the time domain s(t), which have no imaginary part, in other words: also the imaginary parts of all sampled s d  are zero. As however the Fourier transform is defined for imaginary values too and the formulas show the complete version, this is notedly mentioned here. (In the frequency domain, S d  has a real and an imaginary part, S dreal  and S dimag  as shown in equations 1.2a and 1.2b.)
 
   Is S dreal (k) and S dimag (k) available for n-1 to n-M, the DFT can be calculated with the next sample s(n) for the range n to n-(M-1) as follows:
 
 S   dreal ( k )= S   dreal,n-1 ( k )+ s   dreal ( n )cos(2π nk/M )− s   dreal ( n−M )cos(2π( n−M ) k/M )
 
or simplified:
 
 s   dreal,n ( k )= s   dreal,n-1 ( k )+( s   dreal ( n )− s   dreal ( n−M ))cos(2 nk/M )  (1.3a)
 
and
 
 S   dimag,n ( k )= S   dimag,n-1 ( k )− s   dreal ( n )sin(2 πnk/M )+ s   dreal ( n−M )sin(2π( n−M ) n/M )
 
or simplified:
 
 S   dimag,n ( k )= S   dimag,n-1 ( k )+( s   dreal ( n−M )− s   dreal ( n ))sin(2 πnk/M )  (1.3b)
 
   The inverse Fourier transformation is the reversing operation to the Fourier transformation and thus very similar. 
   Inverse Fourier transformation for continuous analog signals: 
                   s   ⁡     (   t   )       =       ∫     -   ∞       +   ∞       ⁢       S   ⁡     (   f   )       ⁢     ⅇ     ⅈ2π   ⁢           ⁢   f   ⁢           ⁢   t       ⁢     ⅆ   f                 (   1.4   )               
The DFT form for sampled digital signals:
 
                     s   d     ⁡     (   n   )       =       2   M     ⁢       ∑     k   =   0         M   /   2     -   1       ⁢         S   d     ⁡     (   k   )       ⁢     ⅇ     ⅈ2π   ⁢           ⁢   n   ⁢           ⁢     k   /   M                       (   1.5   )               
The DFT form in Euler&#39;s representation:
 
               s   d     ⁡     (   n   )       =         2   M     ⁢       ∑     k   =   0         M   /   2     -   1       ⁢         S   d     ⁡     (   k   )       ⁢     (       cos   ⁡     (     2   ⁢   π   ⁢           ⁢   n   ⁢           ⁢     k   /   M       )       +     ⅈsin   ⁡     (     2   ⁢   π   ⁢           ⁢   n   ⁢           ⁢     k   /   M       )         )     ⁢           ⁢   0         ≤   n   &lt;   M           
or split into real and imaginary parts of the inverse Discrete Cosine Transformation (iDCT):
 
                     s   dreal     ⁡     (   n   )       =         2   M     ⁢       ∑     k   =   0         M   /   2     -   1       ⁢         S   dreal     ⁡     (   k   )       ⁢     cos   ⁡     (     2   ⁢   π   ⁢           ⁢   n   ⁢           ⁢     k   /   M       )             -         S   dimag     ⁡     (   k   )       ⁢     sin   ⁡     (     2   ⁢   π   ⁢           ⁢   n   ⁢           ⁢     k   /   M       )                   (     1.6   ⁢   a     )                   s   dimag     ⁡     (   n   )       =         2   M     ⁢       ∑     k   =   0         M   /   2     -   1       ⁢         S   dimag     ⁡     (   k   )       ⁢     cos   ⁡     (     2   ⁢   π   ⁢           ⁢   n   ⁢           ⁢     k   /   M       )             +         S   dreal     ⁡     (   k   )       ⁢     sin   ⁡     (     2   ⁢   π   ⁢           ⁢   n   ⁢           ⁢     k   /   M       )                   (     1.6   ⁢   b     )               
where S dimag (n) is 0 for all n and therefore not important, as already described earlier.
 
   With the new equations (1.3a) and (1.3b) it is possible to get at every signal sample the complete Fourier spectrum, which can then be inversely transformed by equation (1.6a) without any significant (or at least with a well defined) delay. 
   As shown in the preferred embodiments and evaluated by circuit analysis, the novel circuits and methods provide an effective and manufacturable alternative to the prior art. 
   While the invention has been particularly shown and described with reference to the preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made without departing from the spirit and scope of the invention.