CS6422 datasheet(33/48 Pages) CIRRUS

33 / 48 page

CS6422

room, when someone moves the speaker or the mi-

crophone, or when someone drops a piece of paper

on top of the speaker. So, the filter needs to adapt

to modify its transfer function to match that of the

environment. It does so by measuring the error sig-

nal at point E and trying to minimize it. This signal

is fed back to the adaptive filter to measure perfor-

mance and how best to adapt, or train.

The trouble arises when the person at the near-end

the adaptive filter should not change. If it tries to

train to the near-end signal, the adaptive filter has

no way to reduce the error signal, because there is

no input to the filter, and therefore no output from

it. The adaptive filter would mistrain.

To prevent this mistraining, the echo canceller uses

double-talk detection algorithms to determine

when to update. These update control algorithms

are the heart of most echo canceller implementa-

tions.

The worst case situation for the CS6422 is when

parties at both ends are speaking and the person at

the near-end is moving. In this case, the echo can-

celler will cease to adapt because of the double-

talk, but the echo will not be optimally reduced be-

cause of the change in path.

4.1.1.2

Adaptive Filter

The adaptive filter in the CS6422 uses an algorithm

called

the

“Normalized

Least-Mean-Square

(NLMS)” update algorithm to learn the echo path

transfer function. This Finite Impulse Response

(FIR) filter has 508 taps, which can model up to

63.5 ms of total path response at a sampling rate of

8kHz. The coverage time is calculated by the fol-

lowing formula:

x 508 = 63.5 ms.

The CS6422’s adaptive filter, like all FIR filters,

only models Linear and Time Invariant (LTI) sys-

tems. So, any non-linearity in the echo path can not

be modeled by the adaptive filter and the resulting

signals will not be cancelled. Signal clipping and

poor-quality speakers are very common sources of

non-linearity and distortion.

A common integration problem for echo cancellers

is signal clipping in the echo path. For example, if

a speaker driver is driven to its rails, the distortion

of the speech may be hard to perceive, but it is very

bad for the echo canceller. The technique of over-

driving the speaker has been used in half-duplex

phones to provide good low-level signal gain at the

expense of distortion with high amplitude signals.

Since this does not work for the CS6422, an AGC

mechanism has been introduced to provide equiva-

lent behavior without clipping. See Section 4.1.3,

“AGC” for more details.

Another common problem is speaker quality. A

poor quality speaker which is perfectly acceptable

for a half-duplex speakerphone, may limit the echo

canceller’s performance in a full-duplex speaker-

phone. The distortion elements are not modeled by

the adaptive filter and so limit its effectiveness.

Speakers should have better than 2% THD perfor-

mance to not impede the adaptive filter.

Volume control should be implemented using the

CS6422 Microcontroller Interface. A real-time ex-

ternal change in the gain of the speaker driver re-

sults in a change in the transfer function of the echo

path, and will force the adaptive filter to readapt. If

the volume control is done before the input to the

adaptive filter, the echo path does not change, and

retraining is not necessary. Another side benefit of

the CS6422 volume control is that it transparently

provides dynamic range compression through the

AGC function.

4.1.1.2.1 Pre-Emphasis

The typical training signal for the adaptive filter is

speech, but most adaptive filters train optimally

with white noise. Speech has very different spectral

8kHz

-------------





CS6422

DS295F1

CS6422 Datasheet(PDF) 33 Page - Cirrus Logic

CS6422 Datasheet(HTML) 33 Page - Cirrus Logic

Html Pages

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Datasheet Download

Link URL