Patent Application: US-4774705-A

Abstract:
a method to reduce memory requirements for a packet loss concealment algorithm in the event of packet loss in a receiver of pulse code modulated voice signals . packet losses are concealed by using the spectral analysis filter memory to smooth a signal gap and by using a technique for determining a maximum repeatable waveform range instead of using the pitch period to reproduce lost packets . the invention uses fewer processing resources and results in improved performance compared to a packet loss concealment algorithm under g . 711 appendix i standards .

Description:
the present invention includes a method to reduce the memory and processing resources requirements for a packet loss concealment ( plc ) algorithm . the method of the preferred embodiment is applied to an itu g . 711 plc algorithm and is illustrated by the flowchart in fig2 . when a receiver 10 determines that a packet from the incoming packet stream is lost 28 , the preferred algorithm analyzes 20 ms of samples that have been saved into the history buffer through a spectral analysis filter 30 . a spectral analysis filter filters samples for parameters of audible speech that produce the inflections of sound encoded in voice data such as impulse and excitations . fig4 illustrates a block diagram of a spectral analysis filter a ( z ) 42 that receives input samples 40 . the spectral analysis filter a ( z ) 42 is a linear predictor with prediction coefficients , a i , that is defined as a system whose output is r ⁡ ( n ) = ∑ i = 0 n ⁢ a i ⁢ s ⁡ ( n - i ) where r ( n ) is the sample index of the residual samples in time domain . a filter of an n th order linear predictor is the polynomial a ⁡ ( z ) = ∑ i = 0 n ⁢ a i ⁢ z - 1 residual , or prediction error , samples r ( z ) are obtained from applying the spectral analysis filter to the incoming voice samples using the formula r ( z )= a ( z ) s ( z ), where s ( z ) are the voice samples 12 that are used to determine if a bad frame 28 has occurred in the packet flow stream . the output 44 of spectral analysis filter 42 are 20 ms of residual samples r ( x ) 44 from the filtered input speech samples 40 . the spectral analysis filter 42 calculates spectral analysis coefficients of the input samples 40 . it is these coefficients which will be later fed into an inverse spectral analysis filter that filters a reconstructed residual sample in order to provide a synthesized packet that replaces or “ conceals ” the packet that was lost . the next step is illustrated in block 32 of fig2 , which describes a process to calculate a maximum repeatable range t within the 20 ms of residual samples r ( x ) 44 in the residual waveform . this method is illustrated using the flowchart diagrams in fig4 and 5 and an exemplary residual waveform in fig6 . the method first 46 analyzes each individual samples x ( i ) of residual r ( x ) from step 30 to determine the first maximum amplitude and the second maximum amplitude of samples x ( i ) 48 . the first maximum amplitude is labeled x 1 and the second maximum amplitude is labeled x 2 . next , in step 48 a ratio α of the two maximum amplitudes are calculated . the formula divides x 2 by x 1 and subtracts 0 . 1 according to : the ratio is then used to determine a threshold formula 50 for the amplitude . the threshold th is calculated by finding the product of the ratio α and the first maximum amplitude x 1 according to the equation th = α · x 1 . once the threshold formula is determined , then in step 52 two thresholds are defined . referring contemporaneously to fig6 , the concept of the positive and negative thresholds are defined on an exemplary waveform 64 . the threshold formula in 50 is used in step 52 to define a positive threshold t_p and a negative threshold t_n for the waveform 64 . in step 54 , a positive threshold t_p 66 and a negative threshold t_n 68 are then calculated using the maximum positive amplitude and the minimum negative amplitude of x ( i ), respectively . based on the positive and negative thresholds , the next step 56 is to determine all positions of residual samples whose amplitudes are above the positive threshold t_p 66 and all positions of residual samples whose amplitudes are below negative threshold t_n 68 . after these positions are determined , the next step 58 is to determine a maximum time period duration between consecutive positions of the waveform 64 that are above positive threshold t_p 66 . in fig6 , the duration between positive amplitudes above positive threshold t_p 66 are shown as t p1 and t p2 . the maximum duration t max , p from these two durations is calculated from choosing the largest time period out of all durations measured and is shown as : in step 60 , a similar procedure is used to determine a maximum time period duration between consecutive positions of the waveform 64 that are below negative threshold t_n 68 . in fig6 , the duration between negative amplitudes below negative threshold t_p 66 are shown as t n1 , t n2 , and t n3 . the maximum duration t max , n from these three durations is calculated from choosing the largest time period out of all durations measured and is shown as : finally , in step 62 , a duration t is determined as the maximum duration of either one of t max , p and t max , n . the maximum duration is calculated as : the result of calculating t max is the definition of the time period t . referring again to fig2 , once t is known the next step 34 generates frame samples ( f size ) by repeating t samples . this step generates a new set of residual samples of 20 ms buffer time denominated r_new ( x ) made of multiple samples of t samples . in step 36 , the spectral synthesis filter 42 memory r_m ( x ) is set to r ( x ) for cases where the filter may require more memory for an expanded r ( x ). then , the reconstructed voice sample is generated s_rec ( x ) using 72 . fig7 is an exemplary flow diagram illustrating this procedure . in fig7 , the generated f size samples ( r_new ( x )) that were created from repeating t samples are used as input 70 into an synthesis filter 72 . the output 74 of the synthesis filter 72 is reconstructed voice samples 74 . these synthesized signals are then used to replace the lost packet in the voice data stream . since the frames are synthesized using the maximum repeatable waveform determination spectral analysis filter memory is used to smooth the signal gap and the recreated frame does not need the overlap and add ( ola ) operation specified in g . 711 appendix i to smoothly transition into the real voice signal . thus , the preferred embodiment does not require a delay , such as the 3 . 75 ms specified in g . 711 appendix i , for the synthetic signal to transition into the real signal . referring again to fig2 , if after packets 12 are received into the vpu 14 there are no bad frames 28 , then the packet reconstruction method of the present invention is bypassed and the vpu moves on to analyze the next frame 38 . the present invention lower mips costs for equivalent or better plc performance . generally , the higher the order of spectral analysis filter polynomials , the higher the mips costs rise to process the voice samples . the preferred embodiment provides for lower - order filter equations that have a lower mips cost and do not negatively impact the overall performance of the plc algorithm . table 1 below shows pesq mos ( perceptual evaluation of speech quality mean opinion scores ) results for various i p polynomial order of filter equations run against different durations of total signal loss in milliseconds . the mos scores are based upon itu - t recommendation p . 862 , “ perceptual evaluation of speech quality , an objective method for end - to - end speech quality assessment of narrow - band telephone networks and speech codecs .” i p 10 ms 20 ms 30 ms 40 ms 10 3 . 99 3 . 94 3 . 78 3 . 49 4 4 . 02 3 . 94 3 . 76 3 . 46 3 3 . 74 3 . 76 3 . 46 3 . 20 2 3 . 52 3 . 53 3 . 66 3 . 19 the above results show that using a 4th order filter in the present invention will result in nearly the same speech quality as using a 10th order filter . this clearly results in the present invention saving processing resources with lower order filters that result in the same speech quality as much higher order filters . one skilled in the art will appreciate that the present invention can be practiced by other than the described embodiments , which are presented for purposes of illustration and not limitation , and the present invention is limited only by the claims that follow 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