Abstract:
The invention relates to an arrangement for measuring and assessing properties of a system ( 28 ) which transfers an electrical, mechanical or acoustical signal or converts an excitation signal x into another signal y. An error system ( 30 ) models the transfer behavior of the system, estimates a desired output signal y′, and generates an error signal e which reveals the excess distortion and disturbances of the output signal y at any time instant t, and can reveal peak values of transient distortion having low power which might otherwise be masked by noise and regular distortion. The error signal is supplied to an assessment system ( 44 ), where convenient distortion measures are calculated and the distortion is displayed versus properties of the signal (e.g., instantaneous frequency and amplitude). The assessment system may also generate a control output ( 42 ) to modify signal x to ensure an optimal excitation of the system.

Description:
BACKGROUND OF THE INVENTION 
   1. Field of Invention 
   The invention relates generally to an arrangement and a method for measuring and assessing properties of a system which transfers an electrical, mechanical or acoustical signal, or converts such a signal into another signal. The system is characterized to have at least one signal input and at least one signal output. Examples of such systems include an electro-acoustical transducer (loudspeaker, actuator, head phones), a converter between the analog and digital domain, storage media for audio data (CD, mini-disc), and wired and wireless communication systems (fiber optics, high frequency transmission). 
   2. Description of Related Art 
   A signal which is converted, transferred or stored in a system can be subject to distortion caused by properties of the system (e.g. inherent nonlinearities) and their interaction with the transferred signal. Additional stochastic disturbances may be caused by noise, ambient sound, or loose connections, which are not directly related to the transferred signal. Traditional techniques developed for assessing the signal distortion typically require providing a special excitation signal (single tone, multi-tone complex), measuring the output signal of the system, and transforming the time signal into the frequency domain to search for additional components in the output spectrum which are not part of the excitation signal. This technique makes it possible to identify harmonic and intermodulation components at multiples of the excitation frequencies, and at any combinations of the difference and sum frequencies. Distortion measures standardized by national and international organizations assess the amplitude of the distortion components, whereas the phase of the distortion component is neglected. The second-order and third-order distortion, total harmonic distortion and other simple measures are sufficient in most cases. For example, these measures are commonly used to assess the effects of regular loudspeaker nonlinearities (motor and suspension nonlinearity, nonlinear radiation) which are directly related to their principle of operation. 
   A conventional signal distortion measurement system of this type is shown schematically in  FIG. 1 , which provides a traditional measurement of signal distortion generated by a system under test  1  by using a spectral analysis (FFT). This technique may be applied if the stimulus contains a limited number of tones and each distortion component may be separated from the fundamental tones and identified as a harmonic or intermodulation component. Typically, this method uses a signal generator  2  which generates a single tone, a sensor or measurement input  4 , an analog/digital converter (ADC)  6 , a FFT analyzer  8  and a block  10  for calculating the relative distortion d t  in percent. 
   However, there other types of signal loudspeaker distortion which are quite audible, but which can not be reliably detected by traditional measures. These kinds of distortion are mainly caused by anomalies and defects caused by problems in design or manufacturing. For example, loudspeakers may have defects such as a loose glue joint producing a buzzing sound, a voice coil rubbing on the pole tips, or any obstacle hitting the moving assembly and generating a small click. This class of signal distortion is called “triggered distortion” because it is deterministic; i.e., it depends on the input signal and is initiated under special conditions of the state variables (e.g. voice coil displacement). This triggered distortion may produce significant peak values for a short time in rare instances. However, the power in the mean is much smaller than that found with regular distortion caused by motor and suspension which has a steady-state characteristic. Performing a spectral analysis (FFT transform) is not a reliable way to detect triggered distortion because the energy of the triggered distortion is distributed over a large number of higher-order harmonics (&gt;40), and the signal to noise ratio of each component is very low. 
   U.S. Pat. No. 5,884,260 discloses an invention which addresses this problem by measuring the envelope of the time signal using a filter bank; this approach is illustrated in  FIG. 2 . A signal generator  12  generates the stimulus for the system under test  14 . A sensor or measurement input  16  provides its output to a filter bank  18 , which contains multiple branches connected in parallel. Each branch comprises a band-pass filter  20 , a rectifier  22  and a low-pass filter  24  connected in series. The pass bands of the band-pass filters and the time constants of the low-pass filters correspond with properties of the human auditory system. The band-pass filters have sufficient damping outside their pass-bands to separate the fundamental components from the harmonics. The amplitude and phase response of band-pass filters  20  and the time constants of the low-pass filters  24  changes the waveform of the analyzed signal and limits so as to detect signal distortion which is short in duration but high in amplitude. This method provides a pattern of the distortion which is relevant to human hearing, but is not comparable with other measurements and is hardly interpretable from an objective point of view. 
   The techniques known in the prior art fail if the triggered distortion or the symptoms of a malfunction or defect have less power than the measurement noise or the regular distortion caused by normal nonlinearities inherent in the system without any defects. 
   OBJECTS OF THE INVENTION 
   It is an objective of the invention to develop an arrangement and a method for measuring the signal distortion of a system more precisely, and for assessing the distortion quantitatively. The invention shall also reveal the relationship between signal distortion and the properties of the transferred signal and of the system. Excessive distortion having a small amplitude shall be detected in the presence of noise and regular distortion. The invention shall be realized by simple means and should be robust. The results shall be interpretable and comparable with other known methods. The invention shall be a basis for detecting irregular behavior, malfunctions and defects of a system automatically. Stochastic disturbances such as a loose connection or ambient noise shall be separated from deterministic distortion. 
   SUMMARY OF THE INVENTION 
   The objectives are reached by assessing the structure of the output signal&#39;s waveform in the time domain, and exploiting both amplitude and phase information. A signal source is required that provides an artificial test stimulus, music, or any other excitation signal x, to the input of the system. A signal y at the output of the system under test is monitored directly or by using special sensors. Both the excitation signal x and the measured system output y are supplied to an error system. The error system produces an error signal e that describes the instantaneous distortion in the full temporal resolution. The signal e is supplied to an assessment system, where it is transformed into convenient distortion measures and its dependency on properties of the input or output signal or any other state variable of the system under test is investigated. These properties may be known by using a deterministic excitation signal x, or are provided by a signal analyzer supplied with the input signal x or the output signal y. The assessment system may have an assessment output where the quality of the system or defects may be indicated. The assessment system may induce the signal generator to change the properties of the stimulus to ensure an optimal excitation of the system and to increase the reliability of the assessment. 
   The signal e is generated in the error system by modeling the transfer properties of the system under test. There are two embodiments of the invention:
         In one embodiment, there is a model system that estimates the undesired or disturbing properties of the system under test and generates the error signal e directly.   In the alternative embodiment, the model system generates a desired output signal y′ that considers all desired properties of the system under test. The difference between the measured system output y and the desired output y′ provides the error signal e. In both embodiments, the properties of the model system depend on parameters which are estimated from the input signal x and the output signal y. The parameters of the model systems may be stored and averaged over multiple measurements.       

   This technique makes it possible to separate excessive distortion caused by a defect or a malfunction of the system from regular distortion caused by nonlinearities inherent in the normal system or any other desired properties of system. The error signal e preserves all of the phase and amplitude information of the distortion in the output y′ of the system under test. No FFT, filtering, or any other transformation need be applied to separate the distortion. Small peaks or other transient distortion will be measured in their full temporal resolution and may be detected even if the energy is small. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The following figures illustrate the objectives, advantages and embodiments of the invention: 
       FIG. 1  is a block diagram of a known signal distortion measurement system. 
       FIG. 2  is a block diagram of another known signal distortion measurement system. 
       FIG. 3  is a block diagram of a signal distortion measurement and assessment system in accordance with the present invention. 
       FIG. 4  is a block diagram of an error system as might be used in a signal distortion measurement and assessment system per the present invention. 
       FIG. 5  is a block diagram of an assessment system as might be used in a signal distortion measurement and assessment system per the present invention. 
       FIG. 6  is a plot of a sinusoidal excitation signal as might be used in a signal distortion measurement and assessment system per the present invention. 
       FIG. 7  is a plot of an exemplary output signal from a system under test. 
       FIG. 8  depicts linear and nonlinear parameters as might be calculated by an estimator system per the present invention. 
       FIG. 9  is a plot of the desired output signal which should result from the sinusoidal excitation signal shown in  FIG. 6 . 
       FIG. 10  is a plot showing distortion V(f) and total harmonic distortion d t (f) as a function of the instantaneous frequency f. 
       FIG. 11  is a plot showing instantaneous distortion V(y) as a function of the instantaneous signal amplitude y(t). 
   

   DETAILED DESCRIPTION 
     FIG. 3  is a block diagram which illustrates the principles and signal flow of a signal distortion measurement and assessment system in accordance with the present invention. The arrangement includes a signal source  26 , generating a stimulus x(t) supplied to the input of a system under test  28 . The stimulus may be a stochastic or a deterministic signal. Noise, music, speech or any other natural audio signal are examples of a stochastic stimulus. A deterministic stimulus is usually an artificial test signal (sweep, tone, multi-tone complex) generated by a signal source. 
   System under test  28  produces an output signal y(t), which is using a sensor (not shown) and supplied to a first input of an error system  29 . The error system has a second input which is provided with stimulus x(t) from signal source  26 . The error system produces an error signal e(t) as an output. 
   The present system also includes an assessment system  44  having an input  48  connected to receive error signal e(t). The assessment system  44  transforms error signal e(t) into a distortion response V(f) at an output  45 , or into any other distortion measure. This measure reveals the dependency of the distortion on instantaneous frequency f (“V(f)”), on the amplitude of the output signal y(t) (“V(y)”), or any other state variable related to the nonlinearity (e.g. instantaneous voice coil displacement). The assessment system  44  also generates a control signal S at a control output  42 . Control signal S is dependent on the signal properties of y(t), and is supplied to a control input  46  of signal source  26 . This control signal S may be used to change the properties (frequency, amplitude) of the stimulus to provide optimum excitation of the system under test. 
     FIG. 4  shows one possible embodiment of error system  29 . The error system contains a model system  30 , a subtraction circuit  32  and an estimator  34 . The model system  30  receives stimulus x(t) at one input, and provides a desired output signal y(t)′ to the first input of subtraction circuit  32 . The second input of the subtraction circuit receives signal y(t) as measured at the output of system under test  28 . The subtraction circuit  32  may be realized by a simple difference amplifier producing the error signal e(t) as the difference
   e ( t )= y ( t )− y ′( t ) 
of the two input signals. The error signal e(t) reveals the instantaneous signal distortion versus time t, which depends on the properties of system under test  28 , the properties of stimulus x(t), and the transfer properties of the model system  30 . If model system  30  is a linear system which models the linear properties of the system under test, then all nonlinear effects of the system  28  contribute to error signal e(t). If model system  30  is a nonlinear system, then nonlinear distortion caused by regular nonlinearities may be generated in the desired signal y(t)′ with the same amplitude and phase as in the measured signal y(t). The subtraction performed by subtraction circuit  32  causes a cancellation, or at least a reduction, of the regular distortion in e(t). Thus, error signal e(t) reveals the triggered distortion or any other excessive distortion components, even if their amplitudes are much smaller than the amplitude of the regular distortion.
 
   Note that variables x, y, and e might alternatively be defined in the frequency domain, in which case error signal e(f) would be given by
 
 e ( f )= y ( f )− y ′( f ).
 
   Model system  30  has a parameter input which receives a parameter vector P from estimator  34 . The parameter vector changes the properties of model system  30 , such as its linear transfer function H(f), impulse response h(t), or nonlinear characteristics. Estimator  34  generates the optimal parameter vector P to adjust model system  30  to the particular system under test. Estimator  34  is supplied with input signal x(t) and output signal y(t). To avoid a systematic bias, estimator  34  may model the total transfer behavior of the system under test, including the system nonlinearities, and then separate the desired properties in the parameter vector P. Estimator  34  may generate the parameters adaptively, or may average the parameter vectors from different realizations and then store an optimal vector P as a reference for other systems under test. 
     FIG. 5  shows one possible embodiment for assessment system  44  in accordance with the invention. Assessment system  44  receives error signal e(t) at its input  48 , and provides it to a storage or memory device  50  which produces a time delayed output signal e(t−T). The instantaneous error signal e(t) at input  48  and the delayed signal e(t−T) are supplied to a correlator  52 , which produces the instantaneous distortion measure V(t). 
   If the stimulus is not periodical, or if the period T is not known, then the distortion measure V(t) may be calculated by 
             V   ⁡     (   t   )       =              e   ⁡     (   t   )                    y   ⁡     (   t   )       ′2     +         y   k     ⁡     (   t   )       2           .           
This is a relative measure which describes the ratio between the absolute value of the error signal e(t) and the envelope of the desired signal y′(t). The envelope is estimated by using the analytical signal y k (t), calculated by the Hilbert transform of the desired signal y(t)′.
 
   If the signal source provides a deterministic signal x(t) with the known period T, then sequences of error signal e(t) may be compared with each other and additional distortion measures may be calculated: 
   The minimal value of the error signal searched over N periods: 
             V   ⁡     (   t   )       =         e   min     ⁡     (   t   )       =         min     N   -   1         i   =   0       ⁢          e   ⁡     (     t   -   iT     )                      
or the arithmetical mean value:
 
             V   ⁡     (   t   )       =         e   _     ⁡     (   t   )       =       1   N     ⁢       ∑     i   =   0       N   -   1       ⁢          e   ⁡     (     t   -   iT     )                        
are distortion measures which suppress stochastic disturbances (ambient noise, loose connection).
 
   The maximal deviation of the error from the mean value: 
               V   ⁡     (   t   )       =         e   max     ⁡     (   t   )       =             max   (       N   -   1         i   =   0       ⁢          e   ⁡     (     t   -   iT     )              -       e   _     ⁡     (   t   )             )         
may be used for the detection of stochastic disturbances (e.g. a loose electrical connection).
 
   The instantaneous distortion measure V(t) is a function of time t, and depends on the properties of the instantaneous signal y(t). To simplify the interpretation of this measure, it is useful to replace the time by other signal properties such as frequency and amplitude. This mapping is accomplished by a rating device  56 . If the stimulus is deterministic, then the relationship between some signal properties (instantaneous frequency, amplitude) and the time t is known a priori. If an arbitrary signal is used as stimulus, then a signal analyzer  54  is supplied with the output signal y(t) via input  40  to identify such properties. If signal analyzer  54  identifies a periodical signal, then the period T may be supplied to the memory  50 . If the physical structure (nonlinear differential equation) of the system under test (loudspeaker) is known and provided as a priori information to signal analyzer  54 , then important state variables (voice coil displacement x) may be identified. The identified information of the system (amplitude, frequency, state variables) are supplied to the rating device  56 . Rating device  56  displays the instantaneous distortion as a function V(f) of instantaneous frequency f, as a function V(y) of instantaneous amplitude y, or as a function V(f,y) of both variables f and y. The function V(f,y) may be displayed as a three-dimensional plot and reveals the conditions (e.g., instant time, phase, polarity, dependency of y) for generating triggered distortion. This information are helpful to understand the physical cause (e.g., rubbing of the coil in the gap, hitting the back-plate, mechanical limiting of the suspension). 
   The rating device  56  may also produce control signal S at output  42 , which is supplied to the control input of signal source  26  to generate a stimulus with optimal properties. Thus, the amplitude or the spectral content may be changed to ensure sufficient signal-to-noise ratio or to protect the device under test for an overload situation. 
   A signal produced by rating device  56  at output  60  describes the quality (Q) of the system under test quantitatively, by using a rating (0&lt;Q&lt;1) or a logical quantity (0=pass or 1=fail). Simple threshold and known identification algorithms may be used. 
   The following figures show aspects of the invention in greater detail: 
     FIG. 6  shows a sinusoidal sweep defined by:
   x ( t )= U   0  sin(2 πf ( t ) t ), 
as an example of a deterministic stimulus, commonly used for the measurement of loudspeakers. The frequency f(t) varies steadily with time t. There is an exponential relationship between instantaneous frequency:
   f ( t )= f   start α t   
and time t, using the starting frequency f start , with the parameter a affecting the speed of frequency variation.
 
     FIG. 7  shows the sound pressure time signal y(t), measured in the near field of a loudspeaker excited by the stimulus x(t) in  FIG. 6 . 
     FIG. 8  shows the identified linear and nonlinear parameters, calculated by: 
             h   ⁡     (   t   )       =       FT     -   1       ⁢     {       FT   ⁢     {     y   ⁡     (   t   )       }         FT   ⁢     {     x   ⁡     (   t   )       }         }             
in estimator  34 . This equation is the inverse Fourier transform of the ratio of the Fourier-transformed sound pressure output y(t) and the sinusoidal sweep input x(t). It reveals the impulse response of the fundamental and harmonic components. Due to the logarithmical increase of instantaneous frequency versus time t, the impulse responses are separated in h(t) and may be assessed by windowing. By using a rectangular windowing function defined by:
 
             w   ⁡     (   t   )       =     {         1           t   1     ≤   t   ≤     t   2               0         0   ≤   t   ≤     t   1               0           t   2     ≤   t   ≤   T           }           
the desired part of the impulse response:
   h   mod ( t )= w ( t )· h ( t )
 
may be extracted from h(t). If all the effects of the nonlinearities inherent in system under test  28  are considered as undesired distortion, and only the variation of the linear amplitude and phase response are considered acceptable, then the limits t 1  und t 2  of the window function w(t) are adjusted in such a way that only the linear part of the impulse response is considered in the model system  30 . Thus, only the fundamental components are generated by  30  and are removed from the error signal e(t).
 
   If some of the harmonics are considered as regular distortion which is typical for the particular system under test, then the corresponding nonlinear impulse responses have to be assigned to the model system  30 . 
     FIG. 9  shows the desired signal:
   y ′( t )= h   mod ( t )* x ( t ) 
generated by convolution of the windowed impulse response h mod (t) with the stimulus x(t) in the model system  30 .
 
   The difference between the measured and estimated signal provides the error signal:
 
 e ( t )= y ( t )− y ′( t ).
 
Alternatively, the error signal:
 
 e ( t )=( h ( t )− w ( t )· h ( t ))* x ( t )=((1− w ( t ))· h ( t ))=( w ′( t )· h ( t ))* x ( t )
 
may be generated by the convolution of the windowed impulse response h(t) using the distortion window
 
               w   ′     ⁡     (   t   )       =       1   -     w   ⁡     (   t   )         =     {         0           t   1     ≤   t   ≤     t   2               1         0   ≤   t   ≤     t   1               1           t   2     ≤   t   ≤   T           }             
with the excitation signal x(t).
 
   The thin curve in  FIG. 10  shows the distortion measure V(f) as a function of the instantaneous frequency f. The bold curve in  FIG. 10  shows the total harmonic distortion in percent according IEC 60268: 
                 d   t     ⁡     (   f   )       =               Y   ⁡     (     2   ⁢   f     )       2     +       Y   ⁡     (     3   ⁢   f     )       2     +   …   +       Y   ⁡     (   Nf   )       2             Y   ⁡     (   f   )       2     +       Y   ⁡     (     2   ⁢   f     )       2     +       Y   ⁡     (     3   ⁢   f     )       2     +   …   +       Y   ⁡     (   Nf   )       2           *   100       ,         
using the Fourier transformed output signal
   Y ( f )= FT{y ( t )}. 
   The total harmonic distortion d t (f) describes the mean power of the harmonic distortion related to the total signal, but neglects the phase of the signal components which determine the peak value of the instantaneous distortion. If the nonlinearities of the system under test can be represented primarily by low-order nonlinearities (e.g., with quadratic, cubic characteristics), then the total harmonic distortion d t (f) is comparable with the instantaneous distortion V(f). This is the case in the particular system under test in  FIG. 10  for frequencies above 200 Hz. The peak values of the instantaneous distortion V(t) are 6-10 dB above the total harmonic distortion d t . Below 100 Hz, the system  28  produces very short disturbances with high peak values in V(f) below 100 Hz, which are up to 30 dB above the total harmonic distortion. In this example, the high crest factor of the harmonic distortion is caused by a loose glue joint in the mechanical system of loudspeakers. The rating system  56  compares the instantaneous V(f) with a threshold V s (f)=−20 dB, and reports a defect at the assessment output  60 . 
     FIG. 11  shows the instantaneous distortion V(y) as a function of the instantaneous signal amplitude y(t). 
   The above description shall not be construed as limiting the ways in which this invention may be practiced but shall be inclusive of many other variations that do not depart from the broad interest and intent of the invention.