Naval Submarine Medical Research Laboratory

NSMRL REPORT 1199 11 APRIL 1996

Effect of digital recording parameters on discrimination features

of acoustic signals in noise

T. S. Santoro

J. S. Russotti

and

T. E. Hanna

19960904 002

Released by

R. G. Walter, CAPT, DC, USN

Commanding Officer

Naval Submarine Medical Research Laboratory

ZGP&JSS® ^

y

Approved for public release; distribution unlimited
Effect of digital recording parameters on discrimination features

of acoustic signals in noise.

T. S. Santoro

J. S. Russotti,

and

T. E. Hanna

Naval Submarine Medical Research Laboratory

Report 1199

Naval Medical Research and Development Command

Research Work Unit 65856N M0100.001-5051

Approved and Released by

R. G. Walter, CAPT, DC, USN

Commanding Officer

NAVSUBMEDRSCHLAB

Approved for public release; distribution unlimited
Summary Page

Problem

Design parameters of sonar digital signal processing systems are selected for visual presentation of data and sacrifice the quality of the aural presentation to the operator. Two critical parameter choices, sample rate and quantization code, degrade aural signal discrimination in noise

by the human listener and their effect on auditory perception of processed signals is not fully

understood.

Findings

Critical listener perceptions for discrimination are (1) signal beat at low frequencies, (2) spectral shape at higher frequencies, and (3) individual signal temporal modulation. The importance

of each perception is strongly dependent on the sample rate used in signal processing but not the

quantization code.

Applications

Design ofsonar signal processing equipment for optimal human auditory discrimination

performance.

Administrative Information

This work was completed under Naval Medical Research Development Command Research

Work Unit 65856N M0100.001-5051, Digital processing for auditory sonar. The views expressed in

this report are those of the authors and do not reflect the official policy or position of the Department of the Navy, Department of Defense, or the U.S. Government. This report was approved

for publication on 11 Apr 96 and designated NSMRL Report 1199.

u
Abstract

An experiment was performed to determine the effects of digital signal processing sample

rate and quantization code on auditory perception of sonar signals. Fifteen sonar signals were

sampled and played back under nine conditions of sample rate and quantization code. In each

condition all pairwise combinations of these signals in noise were presented to 35 subjects in an

ABX discrimination task. The resulting matrices of discrimination errors were analyzed by

multidimensional scaling. The first two scaling dimensions recovered in order of statistical

significance were associated with perceptions related to (1) signal beat at low frequencies and

(2) signal spectral shape in the higher frequencies. Further recovered dimensions were related to

particular temporal modulation of individual signals. The importance of the first three discrimination features depended on the three sample rate conditions. Each halving of the sample rate

removed one of the features from any significant contribution to the discrimination task. The

quantization conditions had little influence on the significance of the discrimination features

except for the mid-range sample rate (6.25 kHz).

in
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IV
Effect of digital recording parameters on discrimination features

of acoustic signals in noise.

Digital audio recording and playback equipment has introduced two new parameters into

the transmission path between source and

listener: sample rate and quantization code.

Values for these design parameters have a

major effect on both system complexity and

listening quality (Blesser, 1978; Fielder, 1987).

Faster sample rate provides wider recoverable

bandwidth from the original signal. Hence,

more of the high frequency spectrum of signal

and noise sources will be preserved and presented to the listener. The number of bits used in

the computer to represent each data sample

(the quantization code) affects the amount of

uncertainty, and thus the noise, in the representation of sample values (Hayashi & Kitawaki,

1992). We should expect that when digital

audio is used by sonar operators to detect and

classify complex signals in a noise background, these parameters will also be critical.

The detection of acoustic signals in background noise by a trained listener is based on

the perception of both the average acoustic

power difference between signal plus noise

(S + N) versus noise alone (level difference)

and other primitive features distinctly associated with a given S + N combination. The latter

are features that arise in specific, narrow frequency bands or from various amplitude or

frequency modulations over the entire signal

frequency band. They account for detection

performance at very low overall S/N ratios

such as -10 to -15 dB and often depend on the

listener's perception of what is signal and

what is noise in a given stimulus. When signal SI plus noise N and signal S2 plus noise

N are carefully balanced with respect to listener detection threshold for each signal, the

listener can not distinguish any level difference between the SI + N epoch and the S2 + N

epoch. Therefore, the discrimination between

SI and S2 epochs must be based on the other

perceptual features alone. In this situation, a

same/different discrimination task between

pairs of signals using brief listening periods

can define these perceptual features independent

of level or cognitive effects that might arise in

more complicated test procedures.

We obtained this basic characterization of

signal features for a group of typical sonar

signals from ship traffic under different sample

rate and quantization conditions. We employed

multidimensional scaling of pairwise auditory

discriminations (Gray, 1977; Howard, 1977;

Mackie, Wylie, Ridihalgh, Shultz, & Sletzer,

1981) to uncover the perceptual features used

by the listeners. Our test stimuli were from

the same categories ofsonar signals as those

of Mackie et.al (1981) and Howard (1977)

and our scaling analysis uncovered the same

types of perceptual features used by subjects

in performing those discrimination tasks. In

addition, this study shows how those features

depend on sample rate and quantization parameters.

Method

Signals.

We selected a group of 15 signals representative of a variety of sonar sources recorded on analog tapes at very high signal

strength with essentially no background noise

present. The different power spectra all had

one main peak at the upper frequency end (3

to 8 kHz) with varying sharpness. Starting at

different places in the midband (0.25 to 2.5

kHz), some exhibited a raised flat shoulder

leading to the peak. We associated these spectral characteristics with a hissing sound from

the high frequency peak and a dragging sound

from the mid-band shoulder. In addition, the

signals had varying amounts of temporal
modulation giving them certain characteristic

sounds such as a laboring or galloping beat, a

machinery-like hum or rumble, and gurgling or

washing sounds that were quite pronounced in

some cases and made them very easy to distinguish.

A spectral model was made of a sea-state 2

recording from a typical sonar system for use

as background noise. The noise power spectrum was shaped from the output of a white

noise generator using a series of one-third

octave band filters. The resultant spectrum

had a single broad peak at 8 kHz and dropped

off smoothly at lower frequencies at about 5

dB per octave with no mid-band shoulder.

The noise produced an unmodulated, highpitched hissing sound.

Procedure

Each signal and the noise were digitized

separately at three rates: 12.5,6.25, and 3.125

kHz. Anti-aliasing brick-wall filters with upper cut-offs of 5.0 kHz, 2.5 kHz and 1.25

kHz, respectively, were used in both the digital recording and playback procedures. At

each sample rate, the data were quantitized in

three different codes; 12-, 8-, and 4-bit. Thus

we had nine different combinations of sample

rate and quantization code under which to

measure listener performance on the task of

discriminating between pairs of our stimulus

group. The discrimination task was that of

comparing two signals to a third standard and

simply telling which of the two was the same

as the standard. This is known as an ABX

comparison.

An ABX trial sequence proceeded as follows. The subject wore a headset with voice

only to the left ear and test stimuli to the right

ear. With silence to the right ear, the voice

would state the trial number during a 5 second

period. The ABX sequence would then commence to the right ear. Three seconds were

allotted for each of the three signals in continuous noise. Subjects knew the first

signal was always the standard and one of the

next two signals would be the same as the

first although not the identical recording.

There was no quiet time between signals so

that signal differences were the only clues to

determine when each of the ABX segments

occurred. After the 9 second ABX exposure,

both ears were left in silence for a 10 second

period while the subject checked off his response on an answer sheet. Thus a complete

trial took 24 seconds.

We used all four possible orderings of two

stimuli in the ABX paradigm: AAB, ABA,

BAB, and BBA. Thus the original 105 possible AB pairs from our 15 test signals were

counterbalanced into randomized sets of 420

trials for each of the nine conditions of sample

rate and quantization. We divided the sets

into six groups of 70 trials each as this number required about 1/2 hour for a subject to

complete. Subjects thus would require three

hours to do a complete 420 trial test condition. With breaks each half hour to relieve

fatigue, this would constitute a full morning

or afternoon session for subjects.

At each test condition, we set S/N ratio for

each signal to be 7 dB above signal detection

threshold averaged over subjects from data in

a previous study (Russotti & Santoro, 1992).

By carefully adjusting individual signal S/N

ratio relative to its threshold for each ABX

trial, we did, to the extent possible, remove

average signal level as a discrimination clue.

Each 3 second period flowed smoothly into

the next with little or no perceptible change of

overall level. Hence, the discriminations for

the most part were free of level threshold

clues. Because we used averaged thresholds,

there was of course the possibility that an individual subject with above-average sensitivity

on certain signals could still detect a level difference. Subjects were queried during breaks

on their general perceptions of the test signals
and did report that in some trials all three

ABX periods sounded the same. It was as

though there were just noise alone in all three.

We take these to be trials where the test conditions have obliterated all signal-specific discrimination clues and we have done a good job

of balancing out S/N thresholds.

Subjects

Thirty-six naive listeners with normal hearing, none of whom were in the 1992 study,

served as subjects for this study. We divided

subjects into three, 12 member, groups according to the three sample rate conditions. Each

group was tested on the three quantization

conditions at a single given sample rate. The

ordering of quantization condition for each

test session was randomized so that on each

day the subject would be tested at all three

quantization conditions over the six sessions

without repeating the same condition over any

two consecutive sessions. As a control and

for training purposes, all groups were presented

420 trials of 16-bit, 50-kHz sample rate signals over six sessions on the day before the

start of separate group testing.

Results

Subjects entered their judgements on score

sheets and the answers were converted to

lower-half diagonal-absent matrices of error

rates by dividing the total errors made on each

stimulus pair by the number of subjects in the

test group times the number of trials presented

to each subject. One subject's data was discarded because of anomalies. There were thus

12,11, and 12 subjects in the high, medium,

and low sample rate groups designated Groups

I, n, and IE, respectively. The error rate matrices for each signal pair at the 9 test conditions

are shown in Table la, lb, and lc. Ifsubjects

did pure guessing on this two-alternative forcedchoice task, we could expect error rates of 50

percent. For a few test pairs, as seen in the

matrices, rates did reach the chance level indicating that the two sounds in question were

indeed indistinguishable for the given test conditions. Likewise, for certain conditions,

there were a few pairs that all subjects could

distinguish on every trial.

Average error rates over all subjects and

stimulus pairs ranged from a high of 22.8%

to a low of 7.48% as given in Table 2 for

the nine test conditions. A mixed design 2-

way analysis of variance on the data showed

significant effects due to sample rate, F(2,33)=

26.86,/? < .000001, and quantization, F(2,66)=

59.74,/? < .000001, with an interaction statistic of F(4,66)=4.65,/? < .01. It is clear from

the table that the discrimination task was always easier for the Group I, or high sample

rate, condition. Under that condition, overall

error rates were always under 10%. The

major overall change comes in the move to

sample rate Group II or Group HI from Group

I. For both these groups, error rates are more

than double those of Group I. Likewise, for

quantization effects within each sample rate

group, the major change comes from dropping

to 4-bit code from 8- or 12-bit. The overall effect of going from Group II to Group in (6.25

kHz vs 3.125 kHz) or from 12-bit quantization to 8-bit is quite small.

These error rates are shown connected with

solid lines in Fig. 1 superimposed on dotted

lines connecting the detection threshold averages from our previous study. Both thresholds and error rates are lowest in the Group I

condition. There is a major increase in discrimination error rates between the Group I to

Group II condition and a slight dropback in

rates between the Group II and Group HI

conditions. In contrast, detection thresholds

smoothly increase over the three sample rate

conditions.

Multi-Dimensional Scaling

In addition to the statistical tests of significance, a Multi-Dimensional Scaling (MDS)

analysis was undertaken to interpret the
Table la

Error rate matrices group Ipercent errorper 48

subject-trials

Table lb

Error rate matrices group IIpercent errorper 44

subject-trials

GROUP 1 04 BITS PERCENT ERROR RATE PER 48 SUBJECT-TRIALS

12 SUBJECTS 4 TRIALS EACH SIGNAL PAIR

GROUP 2 04 BITS PERCENT ERROR RATE PER 44 SUBJECT-TRIALS

11 SUBJECTS 4 TRIALS PER SIGNAL PAIR

12 2

4 19 12

15 0 12 4

21 15 17 21 4

12 2 17 4 52 4

23 4 21 2 29 6 12

12 8 6 6 0 12 6 0

15 17 10 44 8 52 4 i 4

444646886 2 10

10 12 25 29 4 27 8 4 0 46 15

10 2 4 4 15 4 12 25 6 2 2 4

2620220420224

2222460402004

32

34 45

25 25 41

30 16 23 34

39 57 43 36 32

32 34 20 27 48 25

18 23 36 27 27 27 36

16 25 9 14 0 20 7 9

41 43 41 39 18 43 16 30 27

30 30 52 30 18 43 34 25 16 32

27 43 48 45 25 57 23 36 11 43 45

11 20 14 11 11 11 14 23 9 23 20 36

0 0 2 7 2 11 2 2 2 9 2 2 2

11 7 5 9 11 11 5 23 9 20 5 16 5

GROUP 1 08 BITS GROUP 2 08 BITS

4

17 4

6 8 15

8 4 4 4

6 19 15 27 8

15 2 4 0 33 2

12 4 21 10 23 2 10

10 0 4 2 4 10 0 6

6 12 4 25 2 50 4 6 10

8 23 2 6 2 17 2 6 8 8

12 10 15 25 0 25 2 2 0 46 8

6044 15 86 12 4820

6000020440224

0204062024000

23

18 11

9 18 16

7 27 30 23

27 50 45 18 14

27 18 7 16 48 27

7 16 16 14 27 25 25

9 11 14 2 7 18 0 7

11 27 39 11 14 43 18 11 18

11 43 36 18 14 34 20 18 2 36

27 55 48 23 25 45 25 36 18 43 23

9 14 11 16 20 14 25 9 5 11 25 11

2 9 2 0 7 5 5 9 2 2 5 2 5

9 18 11 9 2 9 9 16 16 16 11 2 5

GROUP 1 12 BITS

6

19 10

4 21 4

4 2 8 6

10 8 10 31 12

10 2 6 0 44 0

8 2 33 0 17 6 23

10 0 2 2 0 0 4 2

12 15 640033066

8 27 6802040 15

12 10 10 15 0 19 2 0 10 50 4

6060 17 4844 27 26

2000260200240

2000042800440

GROUP 2 12 BITS

11

11 39

14 25 11

11 14 32 20

30 45 27 16 14

11 25 11 9 48 18

16 14 14 16 25 32 27

7 20 5 14 7 34 5 11

9 41 32 11 34 36 27 27 25

16 36 25 18 27 34 30 27 5 30

18 43 41 7 16 30 27 16 7 39 41

2 11 9 11 32 20 14 9 5 11 14 11

5 9 7 0 5 11 7 2 0 11 9 5 2

14 16 9 2 2 14 5 18 14 11 18 7 9 0
Table lc

Error rate matrices group IIIpercent errorper 48

subject-trials

GROUP 3 04 BITS PERCENT ERROR RATE PER 48 SUBJECT-TRIALS

12 SUBJECTS 4 TRIALS PER SIGNAL PAIR

12

12 58

4 27 23

0 15 6 21

15 58 44 12 10

4 8 6 4 44 6

8 29 10 10 10 12 19

17 35 21 31 6 48 12 23

8 46 46 10 2 52 12 17 31

15 50 46 17 6 46 8 10 48 40

12 44 58 10 10 54 17 10 29 54 52

4 6 15 25 25 19 15 15 19 15 12 10

2642080 10 606 10 8

2 25 27 29 38 17 29 38 23 21 15 12 17 31

GROUP 3 08 BITS

4

6 40

4 12 6

0 12 4 12

2 35 29 12 4

2 21 8 4 33 23

6 10 23 8 10 6 35

15 31 27 12 8 54 12 8

4 48 44 17 6 46 12 12 21

8 44 23 12 8 48 6 31 46 40

12 58 40 17 21 29 15 8 23 48 29

0 15 4 12 8 12 12 12 19 12 12 17

266222 15 17 22484

2 19 21 12 23 17 29 35 15 25 10 23 12 10

GROUP 3 12 BITS

6

846

2 12 8

0 15 8 6

12 40 40 4 8

0 17 4 6 27 17

0 4 17 10 27 17 19

10 19 21 21 4 48 8 15

2 50 52 8 12 29 15 12 19

15 42 25 10 12 44 10 8 40 23

2 60 42 8 6 33 12 12 21 50 42

2 19 8 10 4 19 10 15 25 17 19 23

0886480 12 44226

0 31 25 15 50 31 38 29 17 31 19 10 8 4

results in terms of auditory perception. The

computer program INDSCAL (Carroll and

Chang, 1970; Young, 1970) creates a configuration of points in an N dimensional space

whose separations represent measured pairwise confusions of stimuli such as we have in

the data of Table 1. Where, for example, a

data matrix entry is large, signifying a high

confusion between the stimuli represented by

the row/column pair, the associated points

would be very close to each other and vice

versa. A group of M points may always be

represented in M-l dimensions regardless of

the interpoint relationships. When the original point separations are highly correlated,

however, only one or two independent dimensions are required to represent the resulting

configuration. Dimensions that account for a

small percentage of the variance in inter-point

separation may be ignored in the final solution resulting in a minimal dimensional solution that facilitates interpretation. A study of

the arrangement of points along each remaining dimension coupled with a knowledge of

the stimuli represented by each point yields an

insight to the perceptual feature represented

by that dimension.

A combined scaling analysis may be done

when the same stimuli are discriminated under different test conditions such as the nine

conditions used in this study. A single generalized configuration of points is produced

along with a set of weights for each condition.

The weights represent the relative stretching

or shrinking in the coordinates of each point

along each dimension in going from one condition to the next. A study of the change in

weights on each dimension yields an insight

on the effects of test conditions on the perceptual feature the dimension represents. The

dimensions are given rank labels based on the

distribution of variance among them in the

generalized configuration calculated from the

data for all conditions. However, the relative

importance of each dimension in a given
Table 2

Average error ratesfor nine test conditions

Quantization

Code (Bits)

Group I

12.5 kHz

04

08

12

9.5%

7.5%

7.4%

Group II

6.25 kHz

22.8 %

17.5 %

17.3 %

Group in

3.125 kHz

20.2 %

17.0 %

17.1 %

condition is determined by the weights for

that condition.

The results of our INDSCAL analysis are

generally quite similar to the earlier work of

Mackie, et al. (1981) and Howard (1977) on

sonar signals from the same stimulus categories. In those two related studies, scaling

analysis uncovered 3 to 5 perceptual feature

dimensions. One was associated with the

high frequency spectral nature of the stimuli

while the others involved low frequency temporal amplitude or frequency modulation that

produced very strong beat clarity, tonality,

and rate effects. Working for the most part

with configurations scaled into six or fewer

dimensions, we came to the conclusion that

there are three independent perceptual features

involved in discriminating our 15 signals.

These are: beat presence or absence, spectral

shape, and temporal modulation. We based

this on the very consistent and repeatable nature

of the dimension weights for each test condition over all scaling runs.

Figures 2a, 2b, and 2c show the nine sets

of weight vs. dimension results organized

according to the sample rate variable conditions (Groups I, II, and HI) in three graphs.

Each graph has three separate curves, one for

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Figure 1. Average detection thresholds (Russotti & Santoro, 1992) and overall error rates for nine test conditions.

Left vertical axis S/N in dB at threshold, right axis error rate in % incorrect discrimination.
0.6-, 0.5-1

DIMENSION DIMENSION

Figure 2a. Six dimensional solution weights vs.

dimension - Group I condition

Figure 2b. Six dimensional solution weights vs.

dimension - Group II condition

each of the three quantization variable conditions (12-bit, 8-bit, and 4-bit) at the same

sample rate. As shown, a different scaling

dimension is weighted most heavily in each of

the different sample rate conditions and thus

becomes the dimension accounting for the

most variance in the configuration for that

condition. Except for certain dimensions of

the Group II sample rate, the weighting pattern

is the same for the 3 quantization conditions

at each sample rate condition.

Discussion

We have drawn two conclusions from the

results shown on the nine plots of Fig. 2.

First, we conclude that the perceptual cues are

closely linked to the sample rate variable.

Each sample rate condition brings with it a

distinct set of dimension weights. While

three to six scaling dimensions are required in

0.6

0.5-

0.4-

52 0.3

0.2-

0.1 -

4 bits

O 8 bits

12 bits

DIMENSION

Figure 2c. Six dimensional solution weights vs.

dimension - Group III condition
the complete solution for all test conditions,

only one or two play the major role at each of

the three sample rate conditions. Therefore,

we conclude that each change in sample rate

brings into play quite different perceptual

features for discrimination performance. This

is understandable given the range of sample

rates used. As we go from the Group I rate of

12.5 kHz to the Group II rate of 6.25 kHz and

then to 3.125 kHz for Group HI we are removing exactly half of the power spectrum of our

signals on each step. The dramatic changes

observed in the discrimination data are witness

to the considerable amount of information in

the portion of the spectrum that was removed.

Our second conclusion is that perceptual

clues are not linked to the quantization variable. While error rate is higher for the 4-bit

condition as compared to the 8- and 12-bit

conditions, the scaling analysis tells us this is

not due to the introduction or removal of separate perceptual features. As seen in Fig. 2, the

three traces for quantization code weights at

each sample rate group are very close to each

other. They always reach the same single peak

value for the same scaling dimension. Only

for Group II do these weight traces separate

from each other and there it is only on the

lower-weighted dimensions.

By matching up physical stimulus characteristics with corresponding MDS configuration positions, we have associated the scaling

dimensions with the following three perceptual features: signal beat presence or absence,

signal spectra, and temporal modulation characteristics. The features are in descending

order of the amount of variance in the data

accounted for by each dimension in the unweighted generalized solution over all experiment conditions. Dimension 1, associated

with beat presence or absence, accounted for

17.7% of the variance; dimension 2, signal

spectra, 12.8%; and dimensions 3-6, temporal

modulation characteristics, together accounted

for 40.1% of the total variance in the original

data or about 10% for each dimension. However, which dimension accounts for the most

variance, and hence the finest discrimination,

for a given experimental condition depends

on the weights for that condition.

For example, the weight distribution indicates that for the high sample rate condition

when a broad spectrum of each signal is available, subjects use pitch variations arising from

the high frequency peaks and mid-range shoulder characteristics for the major part of their

discrimination decision. Howard (1978) characterized this feature by "tinniness" or the

relative amount of high frequency energy.

Subjects can align all 15 signals on this single

perceptual dimension (dimension 2 in Fig. 2)

with very good separation. We found locations on that dimension do in fact correspond

to the rank ordering of the high frequency

peaks of each signal. Although the error rates

of Table 2 show some change with quantization at the high sample rate, the scaling solution for that rate is quite insensitive to the

quantization parameter down to even the 4-bit

code.

Once the signal spectrum is halved, as in

Group II, and halved again as in Group in,

the subjects lose this fine pitch separation

capability. The scaling dimension associated

with this percept is then weighted very low.

When this happens, subjects turn to other

criteria that we associate with characteristic

sounds due to temporal modulation, i.e.;

beating, galloping, humming, gurgling, etc.

(dimensions 3-6). These are related to lowfrequency modulation of the signals and the

dimensions labeled "beat rate" and "beat

tonality" by Mackie, et al. (1981). If one of

these sounds is distinct enough, it can dominate one of the higher dimensions in the

general scaling solution by itself (e.g., dimensions 3-6). Once the individual temporal
modulation characteristics of each signal

become the perceptual dimensions of importance, sensitivity to quantization code occurs.

Hence, the choice of 12-bit or 8-bit coding

instead of 4-bit coding at sample rates around

6.25 kHz becomes important to discrimination performance.

The last resort for making a discrimination

under the worst sample rate condition is detection of some temporal modulation, usually

beats of any kind, that can be distinguished

from the noise background. In the Mackie, et

al. (1981) study, this dimension was called

"beat clarity" and accounted for the largest

percentage of variance in the data set as it also

does in our overall solutions. Under adverse

conditions, signals without temporal characteristics are indistinguishable from noise

when the signal level cue is balanced out as in

our experiments. Hence many signal pairing

trials become simple comparisons of the one

signal in noise whose beat can be distinguished

against the other signal in noise combination

that has the same perceived level but lacks a

distinct beat. We contend that this is a somewhat different percept than others related to

signal beat rate or beat tonality discrimination.

These discrimination results confirm and

extend the general conclusions on the effects

of reduced sample rate and quantization

drawn from listener detection performance in

our earlier study. Both detection and discrimination measures are better at 12.5 kHz (Group

I) and 12 bits. When adjusted for the detection threshold differences of Fig. 1, 8-bit

coded signals can be discriminated about as

well as 12-bit signals at each sample rate.

However, note from Fig. 1 that, while the

average threshold difference is minor at 3.125

(Group HI) and 6.25 kHz (Group II), it grows

to about 1.5 decibels for Group I indicating

clearer superiority of the 12-bit code at this

sample rate. We always measure lower performance for the 4-bit code at all sample rates.

At the middle sample rate (Group II), we

see changes in the scaling weights with quantization for the lower-weighted dimensions, even

in going from 12- to 8-bit code. Weights, and

consequently reliance, decrease for temporal

modulation dimensions and increase on the

beat presence or absence dimension. In this

way the beat presence or absence dimension

is different in kind from the other dimensions

related to beat rate and tonality. At the lowest

sample rate (Group HI), this "beat clarity"

dimension is most heavily relied upon to give

listeners some indication of signal presence in

the background noise.

References

Blesser, B. (1978). Digitization of audio: a

comprehensive examination of theory, implementation, and current practice. Journal oftheAudio Engineering Society 25,

739-771.

Carroll, J.D., & Chang, J.J. (1970). Analysis

of individual differences in multidimensional scaling via an N-way generalization

of "Eckart-Young" decomposition. Psychometrika, 35,238-319.

Fielder, L. D. (1987). Evaluation of the audible distortion and noise produced by digital audio converters. Journal ofthe Audio

Engineering Society 35(7/8), 517-534.

Gray, J.M. (1977). Multidimensional perceptual scaling of musical timbres. Journal of

theAcoustic Society ofAmerica 61(5),

1270-1277.

Hayashi, S. & Kitawaki, N. (1992). An objective quality assessment method for bit-reduction coding of wideband speech.

Journal ofthe Acoustic Society of America 92(1), 106-113.
Howard, J. H. (1977). Psychophysical structure of eight complex underwater sounds.

Journal oftheAcoustic Society ofAmerica 62(1), 149-156.

Howard, J. H., & Ballas, James. A. (1978).

Feature selection in auditoryperception

(Technical report ONR-78-5). Washington, DC: The Catholic University of America, Human Performance Laboratory.

Mackie, R.R., Wylie, CD., Ridihalgh, R.R.,

Shultz, T.E., & Sletzer, M.L. (1981).

Some dimensions ofauditory sonar signal

perception and their relationships to target classification (Technical Report ONR2723-1). Goleta, CA: Human Factors

Research Incorporated.

Russotti, J.S. & Santoro, T.P., (1992). Effects

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1b. RESTRICTIVE MARKINGS

2a. SECURITY CLASSIFICATION AUTHORITY

2b. DECLASSIFICATION/DOWNGRADING SCHEDULE

3. DISTRIBUTION/AVAILABILITY OF THE REPORT

Approved for public release;

distribution unlimited

4. PERFORMING ORGANIZATION REPORT NUMBER(S)

NSMRL Report 1199

5. MONITORING ORGANIZATION REPORT NUMBER(S)

NA

6a. NAME OF PERFORMING ORGANIZATION

Naval Submarine Medical

Research Laboratory

6b. OFFICE SYMBOL

(IfApplicable)

7a. NAME OF MONITORING ORGANIZATION

Naval Medical Research and Development

Command

6c. ADDRESS (City, State, Zip Code)

Box 900, Naval Submarine Base NLON,

Groton, CT 06349-5900

7b. ADDRESS (City, State, Zip Code)

8901 Wisconsin Avenue

Bethesda, MD 20889-5606

8a. NAME OF FUNDING/SPONSORING

ORGANIZATION

Same as 7a

8b. OFFICE SYMBOL

(IfApplicable)

9. PROCUREMENT INSTRUMENT IDENTIFICATION NUMBER

8c. ADDRESS (City, State, Zip Code)

Same as 7b

10. SOURCE OF FUNDING NUMBERS

PROGRAM

ELEMENT NO.

65856N

PROJECT NO.

M0100

TASK NO.

001

WORK UNIT

ACCESSION NO.

DN240501

11. TITLE (Include Security Classification)

Effect of digital recording parameters on discrimination features of acoustic

signals in noise.

12. PERSONAL AUTHOR(S)

Thomas S. Santoro, Joseph S. Russotti, & Thomas E. Hanna

13a. TYPE OF REPORT 13b. TIME COVERED

FROM TO

14. DATE OF REPORT (/ear, Monti, Day)

1996 Apr 11

15. PAGE COUNT

10

16. SUPPLEMENTARY NOTATION

17. COSATI CODES

FIELD GROUP SUB-GROUP

18. SUBJECT TERMS (Continue on reverse if necessary and identify by block number)

Multidimensional scaling; digital quantization and

sample rate; auditory perception

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

An experiment was performed to determine the effects of digital signal processing sample rate and

quantization code on auditory perception of sonar signals. Fifteen sonar signals were sampled and played

back under nine conditions of sample rate and quantization code. In each condition all pairwise combinations

of these signals in noise were presented to 35 subjects in an ABX discrimination task. The resulting matrices

of discrimination errors were analyzed by multidimensional scaling. The first two scaling dimensions

recovered in order of statistical significance were associated with perceptions related to (1) signal beat at low

frequencies and (2) signal spectral shape in the higher frequencies. Further recovered dimensions were related

to particular temporal modulation of individual signals. The importance of the first three discrimination

features depended on the three sample rate conditions. Each halving of the sample rate removed one of the

features from any significant contribution to the discrimination task. The quantization conditions had little

influence on the significance of the discrimination features except for the mid-range sample rate (6.25 kHz).

20. DISTRIBUTION/AVAILABILITY OF ABSTRACT

UNCLASSIFIED/UNLIMITED "XSAME AS REPORT :DTIC USERS

21. ABSTRACT SECURITY CLASSIFICATION

22a. NAME OF RESPONSIBLE INDIVIDUAL

Ellen Perkins, Publications

22b. TELEPHONE (Include Area Code)

(203) 449-3263

22c. OFFICE SYMBOL

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