Waveform coding method

Details Waveform coding method Waveform coding method

2001-04-06

2004-06-08

McFadden, Susan (Department: 2655)

Data processing: speech signal processing, linguistics, language

Speech signal processing

For storage or transmission

C704S200100, C704S201000, C704S270000

Reexamination Certificate

active

06748354

ABSTRACT:

FIELD OF THE INVENTION
This invention relates to signal processing arrangements and more specifically to such arrangements comprising coding means for affording a plurality of successive waveform shape descriptors indicative of said signal.
The invention is especially applicable to Time Encoding and Time Encoded Signal Processing and Recognition (TESPAR) as described in the prior art publications and existing patent documentation but is also applicable to other systems using waveform shape descriptors as the basis for signal comparison and classification.
BACKGROUND OF THE INVENTION
One of the major problems facing the designers of signal processing and signal classification systems for incorporation in, for example,
a) word recognition equipment, and in particular speaker independent word recognition systems;
b) condition monitoring equipment, and especially,
(1) Equipment for monitoring rotating machinery,
(2) Equipment for monitoring the flow of substances through mechanical traps and pipes,
(3) Machinery involved in the crushing of ore; and
c) Perimeter intrusion monitoring equipment and systems is that the frequency spectra of the waveforms under examination may shift, in some cases dramatically, due to factors outside the control of the agencies deploying the monitoring equipment.
Thus, for example, in the word recognition task the pitch or frequency spectra of the spoken output of an individual speaker, who is addressing the system, may vary significantly. Rising, for instance, due to excitement or stress, or the effects of external background noise and lowering, for example, due to tiredness or physical fatigue.
In the case of the condition monitoring of rotating machinery, the acoustic vibration output recorded from a machine via a transducer, will, when the machine is rotating quickly, have a different (higher) pitch and frequency spectrum when compared with the spectrum of the identical machine when rotating slowly. Similarly, when monitoring the flow of material through pipes, the natural resonance of the pipes may change according to temperature or atmospheric pressure variations. Such temperature variations when monitoring the vibration of bridges to identify the effects of modifications and mechanical changes to the bridge structure may be a significant adverse factor.
When monitoring machinery involved in the crushing of ore, it is observed that the vibrations derived from the crusher may be a function of ore size and mix. Large sized ore particles producing predominantly low frequency outputs with small size ore particles producing mainly high frequency outputs. These changes and frequency shifts associated with ore size and mix are well known by those skilled in the art.
All the above variations and frequency shifts may be corrected to some extent by means of complicated and relatively inefficient frequency or time “normalisation” procedures whereby, for example, by means of separate additional and parallel procedures, some form of correction factor is estimated and applied to the measurements obtained. In the case of voice recognition, a measure of voice pitch, may be derived from parts of the input waveform and the whole of the input may then be standardised via a normalisation routine, to provide more stable and consistent inputs to the subsequent word recognition circuitry.
When monitoring rotating machinery, rotational speed may be estimated by secondary means such as “tachometer” hardware together with supplementary circuits, to provide a pulse or set of pulses derived from a rotating shaft to enable an indication of approximate speed of rotation to be calculated. From this, a normalisation or standardisation factor or factors may be applied so that a corrected output waveform may be computed.
Similarly temperature may be measured or estimated and normalisation calculated to correct for the adverse effects of temperature changes.
In ore crushing machinery, estimates may be made of the size of the ore by some separate supplementary physical measurement means and normalisation procedures invoked to enable common comparisons to be made over the variability in ore size and mix commonly encountered.
When monitoring underground seismic and or geophonic sensors for example, the output frequency response may change and shift significantly in “pitch”, due to changing soil conditions associated with changes in climatic conditions. Such changes often preclude effective operation in many areas of interest, unless “normalisation” proves economical. In many instances such normalisation processes prove to be computationally intense and, if needed to be carried out in real-time or pseudo real-time they involve a requirement for very fast computer processing and very fast digital signal processing hardware and software. Such requirements with their associated complexity and cost often preclude successful commercial monitoring and classification activities in this and other similar application arenas.
Time Encoding and Time Encoded Signal Processing and Recognition (TESPAR) are well known, as described in EP 0 166 607, EP 0 141 497, U.S. Pat. No. 5,519,805 and WO 97/145831.
In its current prior-art form, the data sets produced by existing TESPAR processes to enable signal representations and classifications to be undertaken are substantially vulnerable to the changes in pitch and frequency previously described in this application. Thus, if an individual speaks in a high pitch voice, the standard ‘S’ matrix for example will contain a larger proportion of short epochs than a similar matrix derived from an input from a normally spoken utterance. Similarly, if the same person speaks the same word in a low pitch, the ‘S’ matrix will contain a larger proportion of symbols associated with longer epochs. Thus standard prior-art TESPAR alphabets and data sets when applied to these frequency shifted signals may also need to have some precursor normalisation processing applied to them, to enable consistent and accurate classification to take place. This may be achieved by many different methods. Uniquely with TESPAR, for example, by the use of Artificial Neural Networks (ANNs), whereby the training material which varies in pitch, as described, may be applied to an ANN after TESPAR coding. Given the fixed TESPAR matrix size and dimensions, in many cases of interest, the network will identify discriminants derived from this input data to provide a characterisation which may be substantially invariant to changes in pitch. This is a complicated normalisation option and the outcome cannot always be guaranteed. A wide range of these and other normalisation procedures are deployed throughout the signal processing community, which accepts the necessity for this additional complexity and equipment and cost to enable relatively stable comparisons and classifications to be made, providing such normalisation is commercially cost effective.
It has been discovered that waveforms subject to pitch variations and frequency variations (associated with speed of rotation, temperature changes, variable ore size, etc), may be advantageously processed by means of a new highly optimised TESPAR coding process, which is substantially invariant to the changes described above, thus eliminating the need for additional complicated and costly “normalisation” procedures.
This advantageous so called “DZ” coding of the TESPAR symbol stream obviates the need to carry out time normalisation, and or frequency normalisation and, DZ coding exhibits properties which enable classifications to be made which are relatively invariant to “sample rate” changes, thus obviating the need, given a particular Analog to Digital (A to D) converter, to carry out interpolation or decimation on the digital signal representations of the original waveform.
Thus the new TESPAR coding method which is substantially invariant to changes in pitch, engine speed, ore size etc. removes the requirement to normalise the waveform under examination, dynamically, or in non-real time, via separate tachometer or other complex computational procedures.

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