Submarine Medical Research Laboratory

NSMRL REPORT 1184 4 FEBRUARY 1993

THE EFFECTS OF DIGITAL SAMPLING RATE AND BIT QUANTIZATION

ON PASSIVE AUDITORY SONAR TARGET DETECTION PERFORMANCE

J. S. Russottm

T. P. Santoro

T. E. Hanna

and

J. J. Wojtowtcz

Released by

P. K. Weathersby, CAPT, MSC, USN

Commanding Officer

Naval Submarine Medical Research Laboratory

Approved for public release; distribution unlimited

Naval
The effects of digital sampling rate and bit quantization

on passive auditory sonar target detection performance

J. S. Russotti, T. P. Santoro

T. E. Hanna, and J. J. Wojtowicz

Naval Submarine Medical Research Laboratory

NSMRL Report 1184

Naval Medical Research and Development Command

Work Unit 65856N - M0100.001-5051

Approved and Released by

P90094~

P. K. Weathersby

Commanding Officer

Approved for public release; distribution unlimited
SUMMARY PAGE

PROBLEM

To determine critical parameters for digital presentation of audio in passive broadband sonar

systems.

FINDINGS

There was a systematic degradation in aural detection performance when highly trained sonar

operators were asked to detect more coarsely digitized target/noise signals. Nine combinations

of sample rate and bit quantization were compared to one another and against a baseline of high

quality digital processing. Detection performance, averaged across a representative sample of

15 sonar targets, was reduced from -12.4 dB at 48 kHz, 16-bit sampling to -7.1 dB at 3.125 kHz,

4-bit sampling.

APPLICATION

Advanced auditory sonar system design.

ADMINISTRATIVE INFORMATION

This research was carried out under Naval Medical Research and Development Command Work

Unit No. 65856N - M0100.001-5051, "(U) Digital signal processing for auditory sonar." The

views expressed in this report are those of the author and do not reflect the official policy or

position of the Department of the Navy, Department of Defense, or the U.S. Government. It was

approved for release on 3 Feb 93 and designated NSMRL Report No. 1184.

ii
ABSTRACT

Modern signal-processing techniques have been applied in passive sonar to enhance sonaroperator performance for visually presented information. However, those same techniques

degrade the auditory signal. These facts are surprising because, in many situations, such as the

classification of transients and broadband signals with few tonal components, the sonar operator

must rely heavily on auditory information. Therefore, it is important to ensure that the audio

component of new sonar systems is not degraded by the use of inadequate digital techniques.

This report describes our procedure for evaluation of digital sample rate and quantization and

shows the significant degradation in detection performance as a function of both reduced sample

rate and reduction in number of bits used to code signal amplitude.

iii
THE EFFECTS OF DIGITAL SAMPLING RATE AND BIT QUANTIZATION

ON PASSIVE AUDITORY SONAR TARGET DETECTION PERFORMANCE

Introduction

Sonar-operator performance on visually

presented passive sonar contacts is enhanced

by modern digital signal-processing techniques. However, the implementation of some

of those same techniques has degraded the

auditory signal. These facts are surprising

because, in many situations, such as the

classification of transients and broadband

signals, the sonar operator must rely heavily

on auditory information. Therefore, it is important to ensure that the audio component of

new sonar systems is not degraded by the use of

inadequate digital techniques. This report

describes one step in the solution of that problem: an evaluation of the effect of digital

sample rate and quantization on auditory

detection performance.

Passive sonar systems are becoming completely

digital. Digital beamforming has replaced

large masses of hardware. The fact that digital

processing now occurs nearly immediately

after receipt of the signal by the hydrophones,

makes preserving the quality of that digitally

transformed signal essential. This point is a

critical one for the future quality of auditory

sonar. Digital sonar systems that have already

been developed, provide audio signals of poor

quality (Gersch, Russotti, & Kerivan, 1979;

Russotti, 1987a, 1987b; Hanna, Russotti, &

Marshall, 1987; Marshall & Nash, 1990).

This degradation results from the fact that

digital signals only approximate the true

analog signal. Work by Nyquist (1928) on

signal transmission using periodic discrete

samples of a continuous signal, provides the

basis for digital signal processing. Two

mechanisms underlie the operation of all

sampled data systems: the conversion

(referred to as analysis) of a continuously

varying signal, usually a varying voltage, into

an ensemble of discrete numerical values and

the synthesis of a continuous signal from a

discrete numerical ensemble. The discrete

nature of a numerical representation of the

original continuous signal is the main potential source of problems limiting the validity of

the representation, due to an inability to

recreate a faithful reproduction of the original

continuous signal. Since sampling yields a

sequence of pulses that represent the amplitude of the signal at discrete intervals in time,

the finer the increment in sampling, the more

precisely the temporal changes in the original

continuous signal can be resolved. Similarly,

the precision with which the amplitude of

each discrete sample is represented is critical

to a faithful reproduction of the original. The

precision in amplitude coding is commonly

referred to as the degree of quantization of the

signal. In digital signal processing, the number of bits assigned to coding each sample

during analog-to-digital (AID) conversion

determines the number of discrete levels that

can be assigned during any sample interval.

As applied in sonar system design, the poor

quality of the digitally processed signal results

from the fact that current digitization parameters are selected solely for their influence

on visual displays. If future designs are not

evaluated for audio quality, then parameters

that were selected to efficiently optimize other

important characteristics of the system will

provide limited auditory information to the

operator with no possibility of retrieving that

lost information without major redesign.

1
We can ensure that system designers have the

proper information for effective system design

by evaluating the parameters essential to

optimum human analysis of auditory signals.

By specifying the performance characteristics

associated with various parameters, we can ensure that future systems have sufficient audio

quality to permit sonarmen to perform optimally. The purpose of the current research

was to evaluate the effects of digital sample

rate and bit quantization on auditory detection

performance.

Method

Source Stimuli. Original analog target recordings were done on high quality 1/4" half-track

tapes. The stimuli were a representative

sample of 15 sonar contacts, chosen from a

library of such contacts used extensively in

our performance research on experimental

displays (Russotti, 1987a, 1987b; Russotti &

Wojtowicz, 1989). Background noise was

modeled from analog sea-state 2 recordings

measured on a B&K 2033 analyzer. The output of a General Radio model 1390-B white

noise generator shaped by a GenRad 1500 1/3

octave multifilter was used to regenerate the

model which was then digitized appropriately.

Subjects. Twenty-four highly trained sonar

operator instructors were used as subjects.

All had hearing within normal limits as

measured by routine audiometry.

Experimental Design. Auditory detection of

15 sonar targets serially and randomly

presented in sea-noise was investigated under

normal and nine experimental conditions. In

the normal presentation, the signal and the

noise were digitized at a 48 kHz sample rate

using 16-bit resolution for amplitude coding.

These values are currently used in commercial

professional applications including digital

audio tape (DAT) recorders for extremely

precise fidelity throughout the 20 Hz to 20

kHz bandwidth. In the experimental presentation, sample rates of 12.50 kHz, 6.25 kHz,

and 3.125 kHz were employed in combination

with either 12, 8, or 4-bit amplitude coding.

All presentations were presented diotically

(target and noise identical in both ears).

Subjects were randomly assigned to one of

three groups, each group representing a different sample rate. Each subject was tested

using a random order of the 15 targets, first

with a 48 kHz sample rate with 16-bit coding.

Then on subsequent days, subjects were presented a random order of the 15 targets, under

one of the three randomly ordered conditions

of bit resolution, all at the same sample rate.

Analog-to-Digital Conversion. The governing principle in sampled data systems derives

from the Sampling Theorem of Nyquist

(1928) and Shannon (1948). It states that in

order to resolve the presence of a frequency,

F, in a given test signal, one must sample that

signal at a frequency of at minimum 2F. This

cutoff is often referred to as the "Nyquist rate"

and the process as "Nyquist sampling." For

example, a sampling rate of 50 kHz will identify all frequencies present in the test signal

up to and including 25 kHz. Less well known

predictions of the sampling theorem involve

the consequences of sampling and synthesizing frequencies above the 2F limit. Extraneous

distortions categorized under the general term

of "aliasing" result in both cases. Anti-aliasing

filters (Moore, 1985; McGill, 1985) must be

used at both A/D input (sampling) and digital

to analog (D/A) output (synthesizing) locations

to guarantee the integrity of the results. The

bandwidth and roll-off of these filters must be

selected according to the constraints of the

Shannon theory; namely, that the filter's -3dB

cutoff frequency be set at one-half the sample

2
rate or lower. In practice, the -3 dB cutoff

frequency, F, is set even more conservatively;

the sample rate is commonly 2.5F.

The requirement for an upper cutoff frequency

on the sampled signal creates interesting variations in the sampled waveform as a function

of the particular shape of the sampling filter

or "window" (Marple, 1987). Various input

windows are available for sampled-data systems. Each offers advantages depending on

the particulars of the actual waveform in question and the desired results. We selected a rectangular window for initial study as it is most

generally used and has good overall performance characteristics.

The analog-to-digital converter (ADC) we

used to process our analog signals, producebinary samples of the input waveform that are

16 bits wide, which makes available all integer

values from 0 to 2 16- 1 (65,535) thereby, allowing a dynamic input signal range of 1:65535

or 96 dB. Our ADC and digital-to-analog converter (DAC) devices run under control of a

DEC PDP 11/23 computer. The memory

capacity available for storing sampled data is

about 8 megabytes (4 million samples). We

sampled our original analog data at 50 kHz allowing an input epoch of about 80 s for each

target sample.

Since sample quantization is the primary variable of interest in the current experiments, we

decided that doing the actual conversions with

the highest number of amplitude levels available and then decreasing the number of levels

would be the most efficient and the fairest

way of producing sample epochs that differed

only in this parameter. It is a straightforward

numerical exercise to generate a binary representation of a given 16-bit number with only

12 or 8 or 4 bits. The resulting loss in resolution through truncation is identical to that

which would be suffered if the actual conversion were done at the given precision, but has

the added advantage of providing data

samples identical in the time domain.

Sample rate differences could not be obtained

by simple digital manipulation of high sample

rate data files; because of the confounding

effect of aliasing, we had to repeatedly sample

the same input waveform at each of the different sample rates of interest. In each case

we used anti-aliasing filters with bandwidth

adjusted according to the sampling theorem.

A Wavetek 752A filter having a rejection rate

of 115 dB/octave was used for all low-pass

filtering. The full matrix of sample rates, filter

bands, and quantization levels are shown in

Table 1. As seen in Table 1, the 15 stimuli

were sampled at rates of 50.00k, 12.50k,

6.25k, and 3.125k Hz. Although, for a given

sample rate R, a 1/2 R cutoff is theoretically

3

Table 1. Matrix of test conditions for each of 15 stimuli

Sample Rate Low Pass Quantization

-3dB Downpoint Bits

50.00 kHz 20.0 kHz 16

12.50 kHz 5.0 kHz 12 8 4

6.25 kHz 2.5 kHz 12 8 4

3.12 kHz 1.2 kHz 12 8 4
acceptable, in practice (McGill, 1985), something less is used. For example, at our highest

sample rate, 50 kHz, we could theoretically

resolve 25 kHz, but in fact used a low-pass

filter with a 3dB-down point of 20 kHz.

Similarly, 3dB-down points of 5.0 kHz, 2.5

kHz, and 1.2 kHz were used to filter signals

sampled at 12.50 kHz, 6.25 kHz, and 3.125

kHz, respectively. For the 50.00 kHz sample

rate, 16 quantization bits were assigned. Signals sampled at rates of 12.50 kHz, 6.25 kHz,

and 3.125 kHz were each quantized at three

levels of precision, 12, 8, and 4 bits.

Digital-to-Analog Conversion. Once the A/D

sampling function was completed, we needed

to reproduce continuous analog signals from

the various sample sets. A DAC with a 16-bit

word size was used to generate a continuous

repetition of the digitized sample (sound file).

By editing this data file, we located start and

stop points on the sample signals at zero crossing, thereby eliminating on/off clicks and the

need for slow rise/fall times at the splice in

the looped sound file.

Stimulus Storage and Playback. Fifteen different target test stimuli were processed at

three different sampling rates and three different quantization levels for a total of 135

test signals. A sea-state 2 background was

similarly processed at each of the three different sampling rates and three different quantization levels. In addition, the 15 highquality digital target recordings (at the 50 kHz

sample rate using 16-bit quantization) served

as pretest target stimuli to be presented with

an equivalently processed sea-state 2 background recording. For testing, all D/A signals

were re-recorded using the analog inputs of a

high fidelity DAT recorder. We used a Sony

Professional PCM-2500 DAT running at 48

kHz with 16-bit resolution conforming to AES

Standards (1984), having a 2 Hz - 22 kHz

(± 0.5 dB) bandwidth and > 90 dB dynamic

range, to record a 20-minute sample for each

member of our test stimulus matrix. While

this reduced the upper sampling frequency to

48 kHz, it still provided an adequate rate for

our stimuli.

Test Apparatus. As shown in Figure 1, a

16-channel multiple target injector operated

under the control of a Digital Equipment

Corporation PDP 11-34 microcomputer. All

targets were presented at a single pre-set bearing 0.000. Target signals were supplied from

eight Sony model 2500 DATs. Background

noise recordings were supplied from a

separate Sony model 2500 DAT.

Target levels were always set at 0 dB S/N in a

0.1 to 8 kHz band. Conditions employing

anti-aliasing cutoffs at frequencies less than

8k were all adjusted to correct for the reduction in RMS level caused by the reduced

bandwidth. To accomplish this, the RMS

level of attenuation produced by a given antialiasing filter was compensated for in the

threshold S/N ratios.

Target level attenuation in either 1 dB or 3 dB

steps was programmed through the multiple

target injector to produce the appropriate

negative S/Ns. A Grason Stadler model 829E

electronic switch, under PDP-11 control, gated

the target signal using a 25 ms rise/decay time.

The Sennheiser HD430 headset, which

presented the acoustic signal, provided a good

representation of the measured electrical

stimulus. In the 100 Hz to 8 kHz region, total

frequency response variation was approximately 8 dB (Russotti, et al., 1985). All test

presentations were presented diotically (target

and noise identical in both ears).

4
TURNS ON SIGNAL

OUT

111l

Figure 1. Test apparatus.

5
Test Procedure. The target detection threshold was estimated by rule using an adaptive

tracking technique that was developed from

the modified International Standards Organization threshold-tracking procedures

described by Harris (1980).

The adaptive tracking technique used in

threshold estimation required the subject to

respond by pressing and releasing a button

within specified time limits. Target on-time,

including a 25 ms rise/decay time, was 3050 ms.

This duration provided listeners with at least one

complete period of target temporal changes for

all 15 targets. Responses were evaluated using

these temporal requirements:

(1) A button-press between 0 and 150 ms after

the electronic switch (ES) turned "on" was

ruled an invalid (premature) response.

(2) A button-press between 150 ms and 1035

ms from ES "on" was a valid "on" response.

(3) A button release prior to 3135 ms from ES

"!'on" was an invalid response.

(4) A button release between 3135 and 3885

ms from ES "on" was a valid "off" response.

(5) Both the on- and off-responses had to be

valid for that target to be scored "detected".

At the start of the trial, subjects heard the

target played continuously at a 0 dB S/N ratio

for a 20 s pre-test period. Then adaptive tracking testing began and the target was presented

for detection at -10 dB S/N. If undetected, the

target was raised 3 dB; if detected, it was attenuated an additional 3 dB. The 3 dB step size

continued until the first reversal in the targetlevel adjustment. From this point, the step

size was 1 dB.

Threshold was the dB value half-way between

successive reversals during a trial. From this,

the absolute value of that threshold's deviation from the current trial accumulated mean

was derived. The sum of these absolute

values was used to determine the average

deviation (AD) which had to be 2 dB or less.

At the end of six thresholds, if the value of the

AD exceeded 2, additional thresholds were

measured until six successive thresholds

yielded an AD of 2 dB or less. Once this

criterion was met, the averaged threshold and

AD were recorded for the completed trial. If

after an additional two thresholds, the criterion was not met, testing was suspended and

the subject re-instructed. A trial was also

terminated and the subject re-instructed if the

distance between successive reversals was

greater than 10 dB.

Results and Discussion

Figure 2 presents average detection thresholds

for the nine combinations of sample rate and

bit quantization tested. A mixed design 3-way

analysis of variance, Winer Case I (1962),

showed a significant effect due to sample rate,

F(2,21) = 18.05, p < .0001, and a significant

effect due to bit quantization, F(2,42) = 40.60,

p < .0001. There was an interaction between

sample rate and number of bits used in amplitude coding, F(4,42) = 2.82, p < .05. This

interaction is graphically depicted in Figure 3

which shows the effects of sample rate on

detection. Each curve represents a different

number of bits. As seen in this figure, the interaction between sample rate and number of

bits occurs at the 8-bit quantization level (no

interaction would be graphically seen as three

parallel lines for the three bit-quantizations).

There was a huge significant difference in

detectability of targets, F(14, 294) = 376.46,

p < .0001. This difference in detectability

of targets interacted with sample rate,

6
DETECTION THRESHOLDS

12 BITS 8 BITS 4 BITS

Figure 2. Detection thresholds obtained for the nine test conditions.

F (28, 294) = 22.71, p < .0001, and bit-quantization, F(28,588)=5.79, p < .0001.

Results of a 2-way analysis of variance to

compare each group's pretest performance at

the 48 kHz sample rate with 16-bit quantization, showed no significant difference among

groups, F(2,21) = 2.94, p = .075. As a result of

this similarity in performance we can combine

pretest data for the three groups and also compare performance differences across groups. The

averaged group detection performance at 48 kHz

with 16-bit coding was -12.4 dB. As expected,

these pretest data showed that there was a significant difference in detectability of individual

targets, F(14, 294) = 146.08, p < .0001, which

was why they were chosen as a representative

sample, though these target differences were

not significantly different from one group

to another and produced no interactions,

F(28, 294)=0.81.

Since the 48 kHz 16-bit condition was always

presented as a preliminary training session,

we would expect measured performance to be

reduced somewhat from more practiced performance at that bit-quantization and sample

rate. Despite this predicted reduction, t tests

of individual differences showed that for all

but the 12-bit 12.5 KHz sample rate, there

was always a significant difference,

p < .05, between detection performance at

the 48 kHz, 16-bit condition and all eight

remaining treatment conditions. At the 12.5

kHz sample rate shown as the cross-hatched

bars in Figure 2, detection performance significantly dropped from -12.6 to -11.3

(p = < .05) as the number of bits was reduced

from 12 to 8. A reduction to -10.8 dB at 4

bits, while significantly different from 12

bits (p < .001), was not significantly different

from 8-bit performance at that sample rate.

7

-15

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O3 -aJ

w

'Ji

0~.

-5

-O
At the 6.25 kHz sample rate, seen in Figure

2 as the broad striped bars, 12 bits and 8

bits produced detection thresholds of -10.9

and

-11.0 dB respectively, though there was a

significant reduction in performance from

both of these to -9.0 dB at 4 bits, p < .01.

At the 3.125 kHz sample rate, depicted in

Figure 2 using narrow striped bars, the difference in detectability of -9.5 dB at 12 bits

and -9.1 dB at 8 bits was not significant,

though there again mne significant degradation in performance to -7.1 dB occurred

from both 12 and 8 to 4-bit quantization

(p < .0001).

Since the pretest data analysis of variance

produced no significant differences between

the three groups and no interaction between

the detectability of individual targets and

-14

13

-12

C,

TJU

Wa

-11

-10

-9

-8

-7

-6

groups, we can safely compare performance

across sample rates. See Figure 3.

At 12-bit quantization, averaged detection

performance dropped significantly from

-12.6 dB at 12.5 kHz to -10.9 dB at 6.25

kHz (p < .01) and again dropped significantly from both of these to -9.5 dB at

3.125 kHz (p < .001). At 8-bit quantization, averaged detection performance

remained unchanged at -11.3 and -11.0 dB

respectively when 12.5 kHz and 6.25 kHz

sample rates were employed, while a significant reduction in performance from both

of these to -9.1 dB occurred at 3.125 kHz

(p < .01). At 4-bit quantization, averaged

detection performance dropped significantly from -10.8 dB at 12.5 kHz to

-9.0 dB at 6.25 kHz (p1 < .01) and then

again dropped significantly to -7.1 dB at

3.125 kHz (p- < .0001).

DETECTION THRESHOLDS

12.5K 6.25K

SAMPLE RATE

3.125K

Figure 3. Effect of sample-rate on detection performance at the various levels of bit quantization.

8

0 12 BITS

o <• 8 BITS

iA 4 BITS

A-
Summary

These results show strong and statistically

significant degradation in performance, from

an average of -12.4 dB S/N across targets at

48 kHz sample rate using 16 bits, down to a

low -7.1 dB target average at 3.125 kHz

sample rate with 4-bit quantization.

These data strongly imply that the auditory

detection performance of our sonar operators

will be seriously limited by the failure to

present optimal auditory information as a

result of reduced sample rate and reduction in

the number of bits used to encode amplitude.

The degradation in performance by no means

ends there however, since auditory target

discrimination performance relies on these

same auditory cues. We are in the process of

measuring the effects of these same

parameters on target discrimination performance and will report these in a follow-on

study.

References

Audio Engineering Society. (1984). AES

recommended practice for professional

audio applications--preferred sampling

frequencies. Journal of the Audio Engineering Society, 32(10), 781-785.

Gersch, D. M., Russotti, J. S., and Kerivan, J.

E. (1979). Critical uses of analog audio

display in passive sonar (U) (Report No.

903). Groton, CT: Naval Submarine Medical Research Laboratory. CONFIDENTIAL

Hanna, T. E., Russotti, J. S., and Marshall,

L. M. (1988). Auditory sonar: The importance of high quality channels in system

design (U) (Report No. 1109). Groton, CT':

Naval Submarine Medical Research Laboratory. SECRET.

Harris, J. D. (1980). A comparison of computerized audiometry by ANSI, Bekesy

fixed frequency, and modified ISC

procedures in an industrial hearing conservation program (Report No. 930). Groton,

CT: Naval Submarine Medical Research

Laboratory.

Marple, S. L., Jr. (1987). DigitalAnalysis

with Applications. New Jersey: Prentice

Hall.

Marshall, L. M., and Nash, M. (1990).

Analog audio, digital audio and alarms on

the AN/BQQ5 sonar system: Sonar technicians ratings (U) (Report No. 1169).

Groton, CT: Naval Submarine Medical Research Laboratory. SECRET.

McGill, J. F. (1985). An introduction to digital recording and reproduction. In J.

Strawn (Ed.), Digital audio engineering:

An anthology (pp. 1-28). California:

William Kaufman.

Moore, F. R. (1985). An introduction to the

mathematics of digital signal processing.

In J. Strawn (Ed.), DigitalAudio Signal

Processing: An Anthology (pp. 1-67).

California: William Kaufman.

Nyquist, H. (1928). Certain topics in telegraph transmission theory. Transactions

of the American Institute of Electrical

Engineers, 47, 617-644.

Russotti, J. S. (1987a). Experimental auditory display (U) (Report No. 1094).

Groton, CT: Naval Submarine Medical

Research Laboratory. SECRET.

Russotti, J. S. (1987b). Effects of system

bandwidth on passive sonar target detection using normal and experimental

auditory displays (U) (Report No. 1103).

9
Groton, CT: Naval Submarine Medical Research Laboratory. SECRET.

Russotti, J. S., Haskell, G. B., Neal, R.,

Santoro, T. P., and Carpenter, S. M. (1985).

Frequency response and distortion measurement of headsets for use in auditory

sonar displays (Report No. 1066).

Groton, CT: Naval Submarine Medical

Research Laboratory.

Russotti, J. S., and Marshall, L. M. (1987).

Problems with current sonar system design

(U) (Presentation). Jacksonville, FL:

Acoustic Analysis Working Group of the

Atlantic Fleet Antisubmarine Warfare Improvement Program. SECRET.

Russotti, J. S., and Wojtowicz, J. (1989).

Effects of degree of noise correlation on

passive sonar target detection using experimental auditory displays (U) (Report No.

1139). Groton, CT: Naval Submarine

Medical Research Laboratory. SECRET.

Shannon, C. E. (1948). A mathematical

theory of communication. Bell Systems

Technical Journal, 27, 379-423, 623-656.

Winer, B. J. (1962). Statistical Principles in

experimental design. New York: McGrawHill.

10
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11. TITLE (Include Security Classification)

The effects of digital sampling rate and bit quantization on passive auditory

sonar target detection performance

12. PERSONAL AUTHOR(S)

J. S. Russotti, T. P. Santoro, T. E. Hanna, and J. J. Wojtowicz 13a. TYPE OF REPORT 13b. TIMECOVERED 14. DATEOFRB:EORT(Yea6,Mr,,D 15. PAGECOUNT

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FIELD GROUP SUB-GROUP Sonar operator performance; acoustics; sonar; digital processing

19. ABSTRACT (Continue on reverse it necessary and identify by block number)

Modern signal-processing techniques have been applied in passive sonar to enhance sonar-operator performance

for visually presented information. However, those same techniques degrade the auditory signal. These facts

are surprising beacuse, in many situations, such as the classification of transients and broadband signals, the

sonar operator must rely heavily on auditory information. Therefore, it is important to ensure that the audio

component of new sonar systems is not degraded by the use of inadequate digital techniques. This report describes our procedure for evaluation of digital sample rate and quantization and shows the significant degradation in detection performance as a function of both reduced sample rate and reduction in number of bits

used to code signal amplitude

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