14.1 Strategies to achieve low delay

Strategy 1. Reduce frame length to 20 samples. From Chapter 1 we know that the

biggest component of coding delay is due to buffering at the encoder and is

directly linked to the length of the frame selected for analysis. Therefore, an

obvious solution is to reduce the length of the frame. Like conventional CELP,

the LD-CELP coder partitions the input speech samples into frames that are

further divided into subframes; each frame consists of 20 samples, containing

four subframes of five samples each. As we will see later, encoding starts

when five samples are buffered (one subframe); this leads to a buffering delay

of 0.625 ms, producing a coding delay in the range of 1.25 to 1.875 ms. These

value are much lower than conventional CELP, having a buffering delay of

20 to 30 ms.

Strategy 2. Recursive autocorrelation estimation. The first step for finding the

LPCs is to calculate the autocorrelation values, which can be done using conventional techniques such as Hamming windowing (nonrecursive); due to the short

frame length, the scheme gets highly computationally expensive and inefficient

since the windows become overlapping for consecutive frames. To simplify,

STRATEGIES TO ACHIEVE LOW DELAY 373

recursive methods can be employed. The LD-CELP coder utilizes the Chen windowing method (Chapter 3) to estimate the autocorrelation values, which in principle is a hybrid technique, combining both recursive and nonrecursive

approaches.

Strategy 3. External prediction. Since the signal frame is relatively short, its statistical properties tend to be close to the near past or near future. It is therefore possible to estimate the LPCs entirely from the past and apply these coefficients to

the current frame, which is the definition of external prediction (Chapter 4). By

using external prediction, the encoder does not have to wait to buffer the whole

frame (20 samples) before analysis; instead, the LPCs are available at the instant

that the frame begins; encoding starts as soon as one subframe (five samples) is

available. This is in high contrast to conventional CELP, where the LPCs are

derived from a long frame, with the resultant coefficients used inside the frame

(internal prediction).

Strategy 4. Backward adaptive linear prediction. Conventional speech coders use

forward adaptation in linear prediction, where the LPCs are derived from the

input speech samples; the coefficients are then quantized and transmitted as

part of the bit-stream. The LD-CELP coder utilizes backward adaptation, with

the LPCs obtained from the synthetic speech. By doing so, there is no need

to quantize and transmit the LPCs since the synthetic speech is available

at both the encoder and decoder side, thus saving a large number of bits for transmission. Note that this is a necessity to achieve low bit-rate, since otherwise

the quantized LPCs must be transmitted at short frame intervals, leading to

unacceptably high bit-rate. A disadvantage of the approach is its vulnerability

toward channel errors: any error will propagate toward future frames while

decoding.

Strategy 5. High prediction order. The LD-CELP coder utilizes a short-term synthesis filter with a prediction order equal to 50; no long-term predictor is employed.

This design choice is due to the following reasons.

The operation

of the LD-CELP algorithm can be summarized as follows.

Like conventional CELP, samples of speech are partitioned into frames and

subdivided into subframes. In LD-CELP, each frame consists of 20 samples,

which contains four subframes of five samples each. Since LP analysis is

performed in a backward adaptive fashion, there is no need to buffer an entire

frame before processing. Only one subframe (five samples) needs to be stored

before the encoding process begins.

The perceptual weighting filter has ten linear prediction coefficients derived

from the original speech data. The filter is updated once per frame. The

current frame’s coefficients are obtained from previous frames’ samples.

The synthesis filter corresponds to that of a 50th-order AR process. Its

coefficients are obtained from synthetic speech data of previous frames.

The filter is updated once per frame. The zero-input response of the current

frame can be obtained from the known initial conditions.

Excitation gain is updated every subframe, with the updating process

performed with a tenth-order adaptive linear predictor in the logarithmic-gain

domain. The coefficients of this predictor are updated once per frame, with the

LP analysis process done on gain values from previous subframes.

The excitation sequence is searched once per subframe, where the search

procedure involves the generation of an ensemble of filtered sequences: each

excitation sequence is used as input to the formant synthesis filter to obtain an

output sequence. The excitation sequence that minimizes the final error is

selected.

In the decoder, the initial conditions are restored. The synthetic speech is

generated by filtering the indicated excitation sequence through the filter

without any perceptual weighting. Since the excitation gain and the LPCs are

backward adaptive, there is no need to transmit these parameters. A postfilter

can be added to further enhance the output speech quality