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A new error correction technique for digital recording All digital recording and playback techniques have some form of error correction which allows signals to he restored after transmission errors. But really large errors present a problem in that they can practically destroy the signal. Now, a new technique has been devised with offers significant improvements. At the Philips Research Laboratories in Eindhoven, Raymond Veldhuis has developed a method which allows even large errors in signals, such as speech, music or images, to be restored. For this he makes use of the regularity present in almost every signal. By analysing the error's environment, it is possible to correct the error in such a way that there is no longer any perceptible distortion or picture disturbance. Us1ng knowledge of human hearing, Veldhuis also succeeded in reducing the bandwidth required for the transmission of audio signals by a factor of between six and eight without any audible loss of sound quality. This means that the bandwidth of an FM transmitter can be quite sufficient for the transmission of digital music. The coding method for this bandwidth reduction has been included as a proposal in the Eureka project on Digital Audio Broadcasting (DAB). Digitisation Audio and video signals as we perceive them are analog. Audio signals vary continuously over time; picture signals change continuously as a function of place and time. The continuously changing value of, for example, sound pressure, pitch or picture brightness can be recorded (digitally) with a high level of accuracy by taking a sample of the signal at very specific time intervals. For example, in the case of signals recorded on compact disc, the signal is sampled 44,100 times per second. The sampling frequency is then specified as 44. lkHz. The digitised signal is a series of binary numbers; ie, zeros and ones which are also called bits. If there is a fault, then a bit error can arise with the result that a binary number is no longer completely correct. When the fault is not too large, such errors can be both detected and corrected by adding extra bits to the signal. However, in the case of large errors, such as the loss of several milliseconds of a conversation on a earphone due to interfering reflections from mountains or high buildings, other ways of restoring the digitised signal must be sought. The environment Veldhuis bases his restoration method on the fact that speech, music or picture signals all have a certain regularity, characterised by the signal spectrum. This regularity can be measured and then it is possible to replace the missing numbers in the series so that the restored part of the signal shows, as far as possible, the same regularity as the rest of the signal. Speech signals ........ .... , ~~~········ •••••• ~~L ·•• If the data stream for a picture is corrupted, lots of picture elements will be lost, as shown in this extreme case. 4 SILICON CHIP When applied to speech signals, the technique has been used to restore gaps as long as 12.5 milliseconds. When the restored signals were observed on an oscillograph, the differences between them and the original signals were negligible. And in listening tests, no difference could be heard between the original and the restored signal. The speech sampling frequency in the case of digital telephony, as used in car telephones, is 8kHz ... This photo shows how the new error correction method can restore the seriously corrupted picture on the opposite page. It can also restore badly corr.ected data streams for audio signals. (8000 samples per second). Hence, around 100 consecutive samples were restored in the 12.5 millisecond section of signal. Music signals In the restoration of music signals, Veldhuis also used, in addition to the regularity concept, the fact that a music signal can be considered a s the output signal of a filter. The spectrum is then fully characterised by the filter coefficients. It is thus possible to devise a mathematical equation with which every unknown sample can be estimated on the basis of the preceding samples. If some of the samples following the unknown sample are also known, then a prediction can be made which comes very close to the original sample. In practice, it is possible, at a sampling frequency of 44. lkHz, to fill in up to 30 successive missing samples by calculation, without any musical defeqts being apparent. Picture signal correction Picture signals can also be digitised. Still pictures can be conceived as a changing brightness signal varying according to the position (ie, in two dimensions). With digitisation, the signal be- comes a field of numbers. In such a field, every number represents the brightness of a picture element [pixel). Moving pictures can then be considered as a succession of still pictures. In the (future) transmission of digitised pictures, for example, groups of 8 x 8 pixels will be transmitted. If there is a transmission error then an entire 8 x 8 area can suddenly disappear and this is seen as a picture fault. Restoration can again take place in the manner indicated; determine the regularity in the brightness distribution in the area's surroundings and from this, calculate the brightness of the missing picture elements. As the accompanying photos demonstrate, a picture full of transmission errors can be fully restored - a dramatic demonstration of the technique. Economical coding The digitisation of signals, whether they be music, speech or picture signals, gives an excellent quality of reproduction and, as indicated above, possibilities for effectively repairing damaged signals. There is, however, a price to pay; the sampling frequency required for good quality must be at least twice as high as the highest frequency to be reproduced. In addition, a number of bits are needed to record the content of a sample digitally. An audio signal with a maximum frequency of Z0kHz calls for a sampling frequency of 44. lkHz. Further, to digitally code each sample, an accuracy of 16 bits is required in order to prevent the disturbing influence of rounding errors. In this way, the reproduction of a signal with a bandwidth of Z0kHz requires a bit frequency of 700,000 bits/second; for a stereo signal this figure has to be doubled again. The resulting requirement of 1.4 million bits per second far exceeds the capacity of an FM channel, making economies necessary in the coding of the signals. However, with data compression, the bandwidth required for the transport of digital signals can be greatly reduced. Signal masking Studies of hearing have shown that strong signals with a certain frequency mask weaker signals with neighbouring frequencies; ie, make them inaudible. This only happens if these neighbouring frequencies do not differ too greatly from the frequency of the stronger signal, and their strength does not exceed a certain threshold value (the masking threshold). However, if a musical signal is divided into narrow frequency bands, then it is possible to make do with a rougher coding (less bits per sample) as the resulting interference then remains below the masking threshold. In cooperation with the IRT, the German Institute for Broadcast Technology, and the CCETT, the laboratory of the French Post and Telecommunications organisation, this coding method has now been incorporated as a proposal in EUREKA project 147, Digital Audio Broadcasting. The purpose of this EUREKA project is to arrive at a new transmission standard for digital audio. Footnote: the results described here relate exclusively to laboratory research. They do not involve the marketing or manufacturing of new products. ~ MAY1990 5