Yi Zheng

Romain Brette
email: romain.brette@inserm.fr

On the relation between pitch and level

Keywords: Pitch, sound level, pure tone

Pitch is the perceptual dimension along which musical notes are ordered from low to high. It is often described as the perceptual correlate of the periodicity of the sound's waveform.

Previous reports have shown that pitch can depend slightly on sound level. We wanted to verify that these observations reflect genuine changes in perceived pitch, and were not due to procedural factors or confusion between dimensions of pitch and level. We first conducted a systematic pitch matching task and confirmed that the pitch of low frequency pure tones, but not complex tones, decreases by an amount equivalent to a change in frequency of more than half a semitone when level increases. We then showed that the structure of pitch shifts is antisymmetric and transitive, as expected for changes in pitch. We also observed shifts in the same direction (although smaller) in an interval matching task. Finally, we observed that musicians are more precise in pitch matching tasks than non-musicians but show the same average shifts with level. These combined experiments confirm that the pitch of low frequency pure tones depends weakly but systematically on level. These observations pose a challenge to current theories of pitch.

Introduction

Pitch is the perceptual dimension along which tones are ordered from low to high on a musical scale. For musical tones, the main physical attribute that determines pitch is the repetition rate or fundamental frequency of the sound's waveform [START_REF] Oxenham | Pitch perception[END_REF].

Accordingly, theories of pitch perception, which can be broadly categorized as emphasizing either temporal cues or cochlear place of activation, have focused on how the auditory system might extract fundamental frequency [START_REF] De Cheveigne | Pitch perception models[END_REF]. Recent psychophysical work has focused on distinguishing between these theories by assessing the perception of relatively complex pitch-evoking sounds, for example dichotic sounds [START_REF] Bernstein | Pitch discrimination of diotic and dichotic tone complexes: harmonic resolvability or harmonic number?[END_REF], transposed tones [START_REF] Oxenham | Pitch perception beyond the traditional existence region of pitch[END_REF] or mistuned harmonics [START_REF] Hartmann | On the pitches of the components of a complex tone[END_REF].

In this study, we wanted to address a more basic question: to what extent is the pitch of musical tones the perceptual correlate of fundamental frequency (for complex tones) or frequency (for pure tones)? In other words, is there a one-to-one mapping between pitch and (fundamental) frequency?

A number of earlier studies suggest that this is actually not exactly the case, specifically that the pitch of a pure tone can depend weakly on its level, a finding that is not straightforward to explain with standard theories of pitch [START_REF] Licklider | A duplex theory of pitch perception[END_REF]Terhardt, 1974a).

According to Stevens' rule [START_REF] Stevens | The relation of pitch to intensity[END_REF], the pitch of low-frequency pure tones (<500 Hz) decreases with increasing level while the pitch of high-frequency pure tones (>4000 Hz) increases with increasing level. This finding was obtained by a relatively indirect method, mainly with one subject, in which two tones of different frequencies were presented and the subject was instructed to change the second tone's level so that the two pitches matched. A similar finding for low frequencies was mentioned by [START_REF] Fletcher | Loudness, pitch and the timbre of musical tones and their relation to the intensity, the frequency and the overtone structure[END_REF] and shown with another method by [START_REF] Snow | Change of Pitch with Loudness at Low Frequencies[END_REF], who asked subjects to rank two tones of different levels and frequencies as higher or lower in pitch; a lack of effect on complex tones was also mentioned

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(but not shown). These results were later confirmed with more subjects [START_REF] Morgan | Pitch and intensity[END_REF]Terhardt, 1974b;[START_REF] Terhardt | The influence of intensity on the pitch of complex tones[END_REF], although with substantial inter-individual variability, using a pitch matching method -the subject adjusted the frequency of the second tone to match the pitch of the first tone. At 200 Hz, when the tone level was increased from 40 dB to 80 dB SPL, the pitch shifted down by an amount equivalent to about half a semitone (Terhardt, 1974b), well above the just noticeable difference (for tones of identical level). This was confirmed more exhaustively with two subjects by [START_REF] Verschuure | The effect of intensity on pitch[END_REF]. [START_REF] Terhardt | The influence of intensity on the pitch of complex tones[END_REF] reported small pitch shifts with complex tones, but the shifts varied markedly across participants, and the statistical significance of the shifts was not assessed.

The goals of this psychophysical study were (1) to show that the reported changes with level truly reflect the level dependence of melodic pitch (as opposed to procedural biases or confusion of the perceptual dimensions of pitch and loudness); (2) to determine the level dependence of the pitches of pure tones and complex tones with identical fundamental frequency; (3) to determine whether the phenomenon is influenced by musical experience.

Using pitch-and interval-matching experiments, we found that the pitch of low frequency pure tones, but not complex tones, depends on level regardless of musical experience, and that the measured phenomenon reflects small but actual changes in melodic pitch that partially transfer to the perception of melodic intervals.

Materials and Methods

Subjects and equipment

Ethical approval was granted by the local ethics committee (Comité pour la Protection des Personnes Ile de France). All subjects were fully informed about the goal of the study and

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provided written consent before their participation. All subjects had normal hearing (<20 dB hearing loss (HL) between 100 and 8000 Hz), and were 18-35 years old. Subjects in the nonmusician group had never played an instrument or only briefly played one (<2 years) during childhood. Subjects in the musician group had 7-22 years of musical training, and played at least 1 hour per day at the time of the experiments (Table 1). Experiments 1 and 3 included 4 non-musicians and 4 musicians; experiment 2 included 2 musicians and 2 non-musicians from the same pool; experiment 4 included 6 other musicians.

Stimuli were generated digitally at a sampling rate of 44.1 kHz. Stimuli were presented diotically via a RME Fireface UC soundcard and a Sennheiser HD580 headphone. Sound levels were calibrated for each tone frequency with a sound pressure level meter, giving estimated sound levels at the eardrum. Subjects were seated individually in a double-walled, sound-insulated booth.

Experiments 1 and 2: Pitch matching of pure tones

Each trial began with a 500-ms reference pure tone followed by a 300-ms gap and a 500-ms comparison tone. Listeners were asked to adjust the frequency of the comparison tone until its pitch matched that of the reference tone. The starting frequency of the comparison tone was randomly chosen from a uniform distribution on a discrete semitone scale with a range of 4 semitones around the frequency of the reference tone. After each trial, listeners could adjust the frequency of the comparison tone up or down by 2, 0.5 or 0.125 semitones, without exceeding ±4 semitones around the reference frequency, could elect to hear the same tone pair again, or could indicate that they were satisfied with the pitch match. Listeners were encouraged to start with a big step size and then change to a smaller step size, and to adjust the comparison tone below and above the chosen frequency before making a final decision.

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No feedback was provided. The reference pure tone had a frequency of 200, 1000 or 4000 Hz.

The level of reference tone varied from 20 to 70 dB SPL in steps of 10 dB. In experiment 1, the level of comparison pure tone was set to 40 dB SPL. In experiment 2, it also varied from 20 to 70 dB SPL. Each combination of reference and comparison levels was presented 10 times.

For experiment 1, the comparison level was fixed, while reference levels and frequency conditions were randomized between trials. For experiment 2, comparison levels were randomized across listening sessions. In each listening session, the comparison level was fixed but reference levels and frequency conditions were randomized. Each session contained 18 different conditions (6 reference levels times 3 frequency conditions), and lasted 5-20 minutes depending on the subject. Experiment 1 consisted of 10 sessions per subject (i.e., 10 trials for each condition), while experiment 2 consisted of 60 sessions per subject (6 comparison levels times 10 trials) -on average about 10 hours in total per subject.

Experiment 3: Pitch matching of harmonic complex tones

The pitch-matching procedure was the same as for Experiment 1, except that both the reference and comparison tones were harmonic complex tones, composed of 6 or 12 consecutive harmonics (order 1~6 or 1~12) with equal amplitude and random phase. The level of the comparison tones was fixed at 30 dB SPL per component (overall level was thus 37.8 or 40.8 dB), and the level of reference tones was varied from 10 to 60 dB SPL per component. The initial fundamental frequency of the comparison tone was randomly chosen from a uniform distribution on a discrete semitone scale with a range of 4 semitones around the fundamental frequency of the reference tone (200, 1000 or 4000 Hz).

Experiment 4: Pitch interval matching

An interval consisted of a pair of 500-ms pure tones presented in sequence (no gap).

Each trial began with a reference interval, followed by a 300-ms gap and then a comparison interval. The resulting sequence of 4 tones always increased in frequency. Subjects were instructed to adjust the frequency of the last tone of the comparison interval until its pitch interval matched that of the reference interval, with the same procedure as in the pitchmatching experiments. To prevent listeners from memorizing the reference interval, its size was set to 2 or 3 semitones with equal probability. To ensure that listeners were performing the task by comparing intervals rather than the pitches of individual tones, the frequency of the first tone of the reference interval was set randomly at 200 Hz ±3 semitones (uniform distribution on a semitone scale), and the frequency of the first tone of the comparison interval was set randomly between 0 and 3 semitones above that of the second tone of the reference interval. The levels of the two tones in the reference pair were as follows: ( 1

Results

Level dependence of the pitch of pure tones

Fig. 1 shows the results of experiment 1 for 8 subjects, of whom 4 were musicians (dashed lines). For the low-frequency reference tone (200 Hz), all subjects lowered the matching frequency of the comparison tone when the reference tone's level increased (Fig. 1A; ANOVA, ܨ ହ =19.68, p<0.001, effect size ߟ ଶ =0.71). These results indicate that the reference

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tone sounded lower in pitch when its level was increased, consistent with Stevens' rule [START_REF] Stevens | The relation of pitch to intensity[END_REF] and previous observations with 300-Hz tones [START_REF] Verschuure | The effect of intensity on pitch[END_REF]. Averaged over subjects, the mean pitch shift exceeded 0.6 semitone (3.5% frequency change) at 70 dB SPL (Fig. 1D, black). At 1000 Hz, there was a smaller but significant downward shift in pitch with increasing level (Fig. 1B; ANOVA, ܨ ହ =6.87, p<0.001, effect size ߟ ଶ =0.45), reaching about 0.2 semitone (1.1%) at 70 dB (Fig. 1D, blue). At 4000 Hz, the mean pitch shift was small and not significant (Fig. 1C and1D, red; ANOVA, ܨ ହ =0.81, p=0.55, effect size ߟ ଶ =0.09). Thus the pitch of high-frequency pure tones does not depend on level, in contradiction with Stevens rule. However, we cannot exclude the possibility that a significant effect would be seen with more subjects, and a subset of subjects did show the upward shift observed by Stevens for one subject. Stevens also observed upward pitch shifts at high levels for pure tones with frequencies higher than 4000 Hz (up to 12 000 Hz). We chose to restrict our study to tones with repetition rate lower than 4000 Hz because only these elicit a pitch sensation salient enough to carry melodic information [START_REF] Attneave | Pitch as a medium: a new approach to psychophysical scaling[END_REF].

Psychophysical experiments on pitch perception are often done with musically trained subjects, because their results tend to be more consistent and therefore require fewer trials.

We tested whether the same effects were seen for non-musicians and musicians. There was no significant difference in pitch shift between the two groups (t-test, p = 0.9): for 200 Hz tones; musicians (more than 10 years of musical training, Table 1) and non-musicians showed a negative pitch shift of the same magnitude with increasing level. However, the precision of pitch matching was markedly better for musicians (t-test of standard deviations, SDs, p<0.001), consistent with previous studies of pitch discrimination [START_REF] Kishon-Rabin | Pitch discrimination: are professional musicians better than non-musicians?[END_REF][START_REF] Mcdermott | Musical intervals and relative pitch: frequency resolution, not interval resolution, is special[END_REF]: SDs were 0.11 (200 Hz), 0.075 (1000 Hz) and 0.07 semitones (4000

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Hz) for musicians (corresponding to 0.64, 0.43 and 0.41% frequency change), vs. 0.18, 0.084 and 0.13 semitones for non-musicians (1, 0.49 and 0.75%), averaged across all levels.

We selected 2 musicians and 2 non-musicians for an in-depth analysis of the level dependence of pitch (experiment 2). We wanted to make sure that performance was indeed determined by the pitches of the two tones taken individually and was not influenced by some procedural aspect. For example, one might hypothesize that the pitch of the second tone is influenced by properties of the first tone, via some adaptive or context-dependent process [START_REF] Chambers | Perceptual hysteresis in the judgment of auditory pitch shift[END_REF]. To this end, we varied the level of both the comparison and reference tones between 20 and 70 dB SPL in steps of 10 dB, yielding a matrix of pitch shifts (Table 2). Each curve in Fig. 2A-C shows the pitch shift as a function of reference tone level for a particular comparison tone level.

If the observed frequency shift required to equate the pitch of two tones with different levels, A and B, were entirely determined by the difference in pitch of the two tones, then it should be possible to express this shift S(A,B) (in semitones) as a difference S(A,B) = Pitch(B) -Pitch(A). This implies in particular anti-symmetry (S(A,B) = -S(B,A)) and transitivity (S(A,C) = S(A,B) + S(B,C)). An alternative hypothesis could be, for example, that the observed shift is a function of the difference in levels between the two tones because of contextual effects: S(A,B) = f(B-A). Our data do not follow this hypothesis, as shown in Fig. 2D, where the observed pitch shift is plotted as a function of reference level when the level of the comparison tone was 10 dB above (solid) or below (dashed) that of the reference tone.

The difference in level was the same for all data points in each of the two sets. At 200 Hz, it appears that the pitch shift depends on reference level and not only on level difference (repeated measures ANOVA, p<0.01; no significant effect at 1000 Hz and 4000 Hz).

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We now examine the hypothesis that the observed shift can indeed be expressed as a difference S(A,B) = Pitch(B) -Pitch(A). As there were no significant shifts for high frequency tones (4000 Hz, Fig. 2C), we only analyzed the results for reference tones of 200 Hz and 1000 Hz. First, a two-way repeated measures ANOVA on the observed shifts vs. the levels of reference and comparison tones showed no significant interaction (p=0.9), while each of the two levels had a significant effect on the reported shift (p<0.001 for reference and comparison levels, at 200 Hz and 1000 Hz). This means that the data are statistically consistent with the hypothesis that the observed shift is a sum of two quantities determined by each of the two tones: S(A,B) = f 1 (A) + f 2 (B). Second, Fig. 2A shows that the pitch shift was near 0 when the reference and comparison tones had the same level, i.e., S(A,A) = 0 (mean ± s.d.: 0.01 ± 0.05 semitones at 200 Hz; -0.01 ± 0.06 semitones at 1000 Hz; 0.01 ± 0.07 semitones at 4000 Hz). Therefore, for all tones, f 1 (A) + f 2 (A) = 0, i.e. f 1 = -f 2 . It follows that S(A,B) = f 2 (B) -f 2 (A), i.e., the observed shift can indeed be expressed as a difference between two identical functions of tone level. This property also appears in Fig. 3A: curves showing the relationship between pitch shift and reference tone level all have the same shape, and differ by a constant shift that only depends on the level of the comparison tone.

Overall, this analysis strongly supports the claim that the pitch of low frequency pure tones decreases when the level increases above 50 dB SPL.

Level-dependence of the pitch of complex tones

In experiment 3, the reference tone was a harmonic complex tone with fundamental frequency 200 Hz, composed of either the first 6 harmonics (200 -1200 Hz) or the first 12 harmonics (200 -2400 Hz). In the first case, all harmonics were resolved, whereas in the second case the M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT higher harmonics were not. All subjects reported that they heard the complex tone as a whole without hearing out individual components.

As shown in Fig. 3, there was no significant effect of level on the pitch of the complex tones, whether they contained only resolved harmonics or also unresolved harmonics (ANOVA,p=0.13). This result is important because for the resolved complex tone, the pitch of each individual component was presumably sensitive to level when presented in isolation, as experiments 1 and 2 suggest.

Level dependence of interval perception

Non-musically trained listeners can discriminate differences in musical intervals of around 1 semitone [START_REF] Burns | Categorical perception--phenomenon or epiphenomenon: evidence from experiments in the perception of melodic musical intervals[END_REF][START_REF] Burns | Frequency and frequency-ratio resolution by possessors of absolute and relative pitch: examples of categorical perception[END_REF][START_REF] Mcdermott | Musical intervals and relative pitch: frequency resolution, not interval resolution, is special[END_REF], but precision is greater for musicians [START_REF] Mcdermott | Musical intervals and relative pitch: frequency resolution, not interval resolution, is special[END_REF]. As experiment 1 showed that the level dependence of pure tone pitch was similar for musicians and non-musicians, all 6 subjects of experiment 4 were musicians. In this experiment, listeners had to match a comparison interval with a fixed level to a reference interval in which the level of the two tones could increase or decrease (see Methods).

As the pitch of low-frequency pure tones decreases when level increases, we expected that the reference interval would sound smaller when the level increased than when it was fixed. Fig. 4 shows the results of the interval-matching procedure for the three level conditions (solid line). Subjects were able to accurately match interval size when all four tones had the same level (fixed level, error: -0.08±0.05 semitone). The matched interval size decreased in the increasing level condition by about 0.4 semitone (2.3%) (p<0.01), and conversely increased in the decreasing level condition (p<0.01). These results conform to our

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13 expectations based on the pitches of isolated pure tones, although the effect was smaller than predicted (dashed line).

Discussion

Taken together, our results confirm that the pitch of low frequency pure tones (200 Hz) depends on level. Specifically, the pitch decreased by an amount equivalent to a change in frequency of more than half a semitone (3.5%) when the level was increased from 40 to 70 dB SPL. This is of course small compared to the effect of repetition rate on pitch, especially given the very large change in level, but it is significant and systematic. We found no dependence on level for high frequency tones (4000 Hz) or complex tones. The analysis for many different pairs of comparison and reference levels indicates that the frequency adjustments in the matching task reflect changes in perceived pitch as opposed to contextual or procedural effects. Additionally, similar, although smaller, pitch changes were measured in an interval matching task. Finally, we found that musicians were more precise than nonmusicians in pitch matching tasks but showed the same pitch shifts with level.

Overall, our results confirm and complement previous studies [START_REF] Fletcher | Loudness, pitch and the timbre of musical tones and their relation to the intensity, the frequency and the overtone structure[END_REF][START_REF] Stevens | The relation of pitch to intensity[END_REF][START_REF] Snow | Change of Pitch with Loudness at Low Frequencies[END_REF][START_REF] Morgan | Pitch and intensity[END_REF]Terhardt, 1974b;[START_REF] Verschuure | The effect of intensity on pitch[END_REF] with a more exhaustive and controlled analysis. A number of studies had already shown the effect of level on low frequency pure tones, a few of them with a pitch matching procedure. We have replicated these findings. Our analysis of pitch matching with many different pairs of comparison and reference levels (Experiment 2) confirms that the observed changes in frequency deviation have the expected properties of pitch changes (e.g. antisymmetry, additivity); a similar analysis was performed in one study by [START_REF] Verschuure | The effect of intensity on pitch[END_REF] with two subjects. We have complemented this analysis by using our

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ACCEPTED MANUSCRIPT 14 interval matching task, which showed effects of level in the expected direction, but of smaller magnitude; it is not clear why level had less influence on interval perception than expected from the results of the pitch matching experiments (Fig. 4). An absence of an effect of level on the pitch of complex tones was mentioned by [START_REF] Snow | Change of Pitch with Loudness at Low Frequencies[END_REF] but not shown. [START_REF] Terhardt | The influence of intensity on the pitch of complex tones[END_REF] reported small pitch shifts with level for complex tones, but the shifts varied markedly across participants and the mean shift was probably not statistically significant. In particular, a small negative pitch shift was reported for complex tones with fundamental frequency 200 Hz and harmonics 1-5, similar to one of the stimuli we used (harmonics 1-6). This shift was interpreted as consistent with pitch shifts of the individual harmonics, although it did not appear to be statistically significant. We applied the same procedure to complex tones with the same fundamental frequency, and we observed no significant pitch shifts, even though pitch shifts were observed with the with pure tones of 200 and 1000 Hz. Finally, it is known that musical experience can influence psychophysical performance in pitch tasks (for example discrimination), but previous studies did not mention the musical experience of listeners. We have analyzed the data for musicians and non-musicians separately, and we found that even though musicians were more precise in pitch matching, the two groups showed the same average dependence of pitch on level. We conclude that the level-dependence of the pitch of low frequency tones is genuine and independent of musical experience.

We found that musical training has an effect on the precision of pitch matching, but not on the average effect of level. This observation reinforces the claim that the pitch of low frequency pure tones truly depends on level by ruling out one alternative interpretation of the experimental observations, namely that the level-dependence of the deviation in match reflects the level-dependence of uncertainty in the task. That is, one could argue that pitch is more uncertain at low levels and therefore listeners are biased towards some prior, which could reflect for example the spectrum of natural sounds. This could predict level-dependent

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responses, with larger shifts for frequencies far from the prior. If this were true, we would expect smaller level-dependent shifts when responses are more precise, but this was not the case for musicians.

The results confirm that there is no exact one-to-one mapping between repetition rate and pitch, even for pure tones. This finding is related to other surprising phenomena in pitch perception (Hartmann, 1998). Diplacusis, which exists in normal hearing subjects, is the phenomenon that the pitch of a tone can differ slightly but significantly between the two ears [START_REF] Jeffress | Variations in Pitch[END_REF][START_REF] Van Den Brink | The Relation between Binaural Diplacusis for Pure Tones and for Complex Sounds under Normal Conditions and with Induced Monaural Pitch Shift[END_REF][START_REF] Burns | Pure-tone anomalies. I. Pitch-intensity effects and diplacusis in normal ears[END_REF]. Another known phenomenon is octave stretch, the fact that when subjects are asked to adjust the frequency of a tone so that it sounds an octave higher than a reference tone of frequency f, they tend to set a frequency greater than 2f. This phenomenon is also observed for listeners with absolute pitch [START_REF] Ward | Subjective Musical Pitch[END_REF]. Pitch shifts can also be induced by masking noise [START_REF] Houtsma | Noise-induced shifts in the pitch of pure and complex tones[END_REF], by a previous adapting tone of similar frequency [START_REF] Larkin | Pitch Shifts Following Tone Adaptation[END_REF][START_REF] Rakowski | Poststimulatory pitch shifts for pure tones[END_REF], or by changes in the envelope of the tone [START_REF] Rossing | Effects of signal envelope on the pitch of short sinusoidal tones[END_REF][START_REF] Hartmann | The effect of amplitude envelope on the pitch of sine wave tones[END_REF].

The level dependence of pitch is rather difficult to explain by standard temporal [START_REF] Licklider | A duplex theory of pitch perception[END_REF] or place (Terhardt, 1974a) theories of pitch. The implications of this phenomenon on theories of pitch have been discussed previously [START_REF] Hartmann | Signals, Sound, and Sensation[END_REF][START_REF] Moore | An Introduction to the Psychology of Hearing[END_REF], and we therefore only give a general overview. Temporal theories of pitch based on the periodicity of the sound's waveform, predict that pitch is independent of sound level.

Significantly, our results show that the pitch of low frequency tones, but not of complex tones with the same periodicity, depends on level; it is not straightforward to see how temporal theories of pitch could predict differences between these two cases. It has been shown that the most frequent all-order interspike interval in auditory nerve recordings corresponds to the pitch of complex tones, and varies little with level [START_REF] Cariani | Neural correlates of the pitch of complex tones. I. Pitch and pitch salience[END_REF], in contrast with estimates from first-order intervals (successive spikes). A possible explanation for the

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level dependence of the pitch of low frequency pure tones is that the estimate from interspike intervals can deviate from the stimulus period because of the refractory period of auditory nerve fibers [START_REF] Ohgushi | The origin of tonality and a possible explanation of the octave enlargement phenomenon[END_REF]. At higher levels, this deviation could become more important as the firing rate increases. However, at low frequencies, this effect is only seen in first-order intervals but not in all-order intervals [START_REF] Mckinney | A possible neurophysiological basis of the octave enlargement effect[END_REF].

Place theories of pitch in which pitch is indicated by the place of maximal activation of the cochlea, suffer from the fact that auditory nerve fibers saturate at high levels and the locus of maximal activation varies dramatically with level [START_REF] Sachs | Encoding of steady-state vowels in the auditory nerve: representation in terms of discharge rate[END_REF][START_REF] Kim | Cochlear mechanics: implications of electrophysiological and acoustical observations[END_REF][START_REF] Chatterjee | Cochlear mechanisms of frequency and intensity coding. I. The place code for pitch[END_REF][START_REF] Cedolin | Pitch of complex tones: rate-place and interspike interval representations in the auditory nerve[END_REF][START_REF] Versteegh | Response characteristics in the apex of the gerbil cochlea studied through auditory nerve recordings[END_REF], by an amount equivalent to about 0.5 octave for a level change from 40 to 80 dB [START_REF] Mcfadden | The curious half octave shift: Evidence for a basalward migration of the travelling-wave envelope with increasing intensity[END_REF][START_REF] Zwislocki | Place code for pitch: a necessary revision[END_REF][START_REF] Moore | Behavioural measurement of level-dependent shifts in the vibration pattern on the basilar membrane[END_REF]. It has been proposed that pitch could be indicated by the low frequency edge of the activation pattern (Zwislocki and Nguyen, 2009), but the predicted variation of pitch with level far exceeds that measured psychophysically [START_REF] Temchin | Spatial irregularities of sensitivity along the organ of Corti of the cochlea[END_REF].

In template theories of pitch (Terhardt, 1974a), it is postulated that the cochlear activation profile associated with each pitch is learned, which could potentially include leveldependent effects. In this context, it could be argued that the pitch of low-frequency tones, but not of complex tones, shows level-dependence because the auditory system is mostly exposed to complex tones (in particular voices) while pure tones are less natural. In this conceptual framework, the observation that musical training enhances precision but has no effect on the average level-dependence of the pitch of low frequency tones is not straightforward to interpret. Indeed, what underlies the enhanced precision of musicians? A natural explanation in the framework of template theory is that increased exposure to templates yields better discrimination via learning. In this case, we would expect that a reduction in bias accompanies an enhancement of precision, but this was not the case. Thus if template theory

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is correct, then the learning that underlies the level independence of the pitch of complex tones must be of a different type than the learning that underlies the enhancement in precision in musicians.

A few models of pitch [START_REF] Loeb | Spatial cross-correlation. A proposed mechanism for acoustic pitch perception[END_REF][START_REF] Cedolin | Spatiotemporal representation of the pitch of harmonic complex tones in the auditory nerve[END_REF][START_REF] Laudanski | A structural theory of pitch[END_REF] and of tone detection [START_REF] Carney | Auditory phase opponency: A temporal model for masked detection at low frequencies[END_REF] are based on timing differences between the spike trains in different fibers. For example, Loeb's model proposes that the frequency of a pure tone can be estimated by comparing signals across the basilar membrane: the distance that separates places that vibrate in phase is related to the tone's frequency. Because the phase of the basilar membrane response to tones depends on level [START_REF] Robles | Mechanics of the Mammalian Cochlea[END_REF], pitch would depend on level according to this model. However, a quantitative study using guinea pig auditory nerve responses [START_REF] Carlyon | Across-channel timing differences as a potential code for the frequency of pure tones[END_REF] showed that in Loeb's model [START_REF] Loeb | Spatial cross-correlation. A proposed mechanism for acoustic pitch perception[END_REF], the predicted variation of pitch with level far exceeds that measured by psychophysical measurements: the change corresponds to more than two octaves for a 40 dB level change.

The structural theory of pitch [START_REF] Laudanski | A structural theory of pitch[END_REF] is a generalization of the models of Licklider and Loeb [START_REF] Licklider | A duplex theory of pitch perception[END_REF][START_REF] Loeb | Spatial cross-correlation. A proposed mechanism for acoustic pitch perception[END_REF], in which pitch is postulated to be the perceptual correlate of the regularity structure of the vibration pattern on the basilar membrane, across place and time. Regularity structure at the level of cochlea includes periodicity within a cochlear channel (mathematically, S(x,t) = S(x,t-δ) for all t, where S(x,t) is the mechanical signal at position x on the basilar membrane), named within-channel structure, and identities across frequency channels of the form S(x,t) = S(y,t-δ) for all t (x and y are two fixed cochlear places), named cross-channel structure. Within-channel structure is level-independent, but cross-channel structure is level-dependent if the basilar membrane responds nonlinearly. As in Licklider's model, delays δ in the regularity structure are matched by conduction (e.g. axonal) delays. If there is a physiological upper bound on these delays (δ<δ max ), then low frequency pure tones have only cross-channel structure (more precisely, within-channel structure is not usable), while complex tones also have within-channel structure [START_REF] Laudanski | A structural theory of pitch[END_REF]. Thus, the theory predicts level dependence for low frequency pure tones but not complex tones if it is postulated that within-channel structure dominates cross-channel structure. The extent of level dependence depends on the spacing between channels and therefore on the specific instantiation of the theory: if channels are widely spaced, then the model would suffer from the same problem as Loeb's model [START_REF] Carlyon | Across-channel timing differences as a potential code for the frequency of pure tones[END_REF]. The model produces weak level dependence for narrowly spaced channels, for tone frequencies lower than 1/δ max .

In conclusion, the pitch of low frequency tones depends weakly but significantly on level, and this finding poses an interesting and unresolved challenge for theories of pitch. Average frequency shift as a function of reference level, when the comparison level was 10 dB higher (solid line) or lower (dashed line) than that of the reference, for the 3 frequencies (same color code as in Fig. 1). • Pitch of harmonic complex tones does not change with sound level.

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• Musical training has no effect on sound-level effects

• The relationship between pitch and level poses a challenge to current theories of pitch

  ) increasing level, 40 dB SPL then 70 dB SPL; (2) decreasing level, 70 dB SPL then 40 dB SPL; (3) fixed level, 55 dB SPL then 55 dB SPL. The level of the two tones of the comparison interval was fixed at 55 dB SPL.

Figure 1 .

 1 Figure 1. Level-dependence of the pitch of pure tones. A-C. Subjects adjusted the frequency

Figure 2 .

 2 Figure 2. Detailed analysis of the level dependence of pure tone pitch for 4 subjects. A-C.

Figure 3 .

 3 Figure 3. Level dependence of the pitch of complex tones. Black: average frequency shift at

Figure 4 .

 4 Figure 4. Level dependence of frequency adjustments required for interval matching,

•

  Pitch of low frequency pure tones decreases with increasing sound level.

Table 1 .

 1 List of subjects and their musical experience.

	Subject	Age	Sex	Musical experience	Experiments
	SAG	22	M	Drums, 12 years	1, 3
	PES	22	F	None	1, 2, 3
	PSS	25	M	None	1, 2, 3
	PAM	22	F	Piano, 14 years	1, 2, 3
	PJC	33	M	Guitar, 15 years	1, 2, 3
	PTC	24	F	Trumpet, 15 years	1, 3
	PAL	20	M	None	1, 3
	SYZ	33	F	None	1, 3
	LHK	27	F	Piano and violin, 10	4
				years
	PPG	27	M	Violon, 7 years	4
	SLC	21	F	Violin, 15 years	4
	SLJ	20	M	Piano, guitar and	4
				saxophone, 10 years
	SEC	28	F	Violin, 22 years	4
	SOP	23	M	Trumpet, 10 years	4

Table 2 .

 2 Frequency shifts in semitones for all combinations of reference and comparison Comparison 20 dB SPL 30 dB SPL 40 dB SPL 50 dB SPL 60 dB SPL 70 dB SPL

	levels (experiment 2). Columns: reference level; rows: comparison level.
	200 Hz
	Reference level

Acknowledgments

This work was supported by the European Research Council (ERC StG 240132). We thank the reviewers and the editor for their help.


Neila Ramdhani

Wisnu Wiradhany

Indonesian Digital Natives ICT Usage Pattern Study across Different Age Groups

Keywords: ICT, Internet, Digital Natives

Since its first appearance on early 2000's at the U.S, the idea that a new generation of students called digital natives or net generation has entered the world has been widely discussed by parents and educators alike. It is said that this generation think, socialize, and act differently; and they will alter roles and regulation of work and educational institutes. Now, at the second decade of the 21st century, Indonesia has to ready herself to meet this new generation. In this paper, we compared information and technology (ICT) access, activities within ICT, investment on ICT, and attitude towards ICT between five hundred Indonesian in three different groups: those who born before the 1980s; those who born between 1980s to 1989's, and those who born after the 1990s by ANOVA. We found that there were no difference on information and technology (ICT) access, activities, investment on ICT, and attitude towards ICT between the groups.

Introduction

Since its emergence in the late 1970s, the internet has growth rapidly and change people's way of life [START_REF] Palfrey | Born Digital: Understanding the First Generation of Digital Natives[END_REF]. At the 1980s, electronic mail has complimented face to face communication and correspondence communication. World Wide Web (web browser tool) made its debut in 1991, followed by the emergence with browser some time afterwards. At the end of the 1990s, search engines, portals, and e-commerce started to emerge. These innovations in the field of information technology are increasing at the 2000'swhen social networking sites and blogs were launched. In late 2008, music and video are integrated into the network via iTunes and YouTube [START_REF] Palfrey | Born Digital: Understanding the First Generation of Digital Natives[END_REF].

Internet have changed the way people interact by eliminating physical boundaries and changing the way people manipulate the external world by opening a range of possibilities in using collaborative media. In this environment a generation who no longer intimidated by the presence of new digital technologies was born. They are able to use the technology better than their parent. Tapscott [START_REF] Tapscott | Grown Up Digital: How the Net Generation is changing Your World[END_REF] extremely states that this digital generation is breathing with the technology.

In the United States, this generation is made up of citizens who were born after 1980s. They spent their teenage years in the late 1990s, where the Internet and personal computers can be found in every private home. This digital generation is known also known as net generation [START_REF] Tapscott | Grown Up Digital: How the Net Generation is changing Your World[END_REF] or digital natives [START_REF] Palfrey | Born Digital: Understanding the First Generation of Digital Natives[END_REF][START_REF] Prensky | Digital Natives Digital Immigrants[END_REF]. As a generation who are familiar with technology, digital natives have different characteristics than the previous generation. Through nGenera survey from 6,000 digital generations, Tapscott [START_REF] Tapscott | Grown Up Digital: How the Net Generation is changing Your World[END_REF] summarizes these differences into eight main characteristics that reduced further to four characteristics by Leung [9] as follows:

1. Global orientation and emotionally uninhibited: this generation finds it easier to share their thoughts online to everyone they can find, protected by the anonymous nature of the internet. 2. Right to information and learning: this generation beliefs in equal opportunity and freedom of expressions of opinions. Keeping up with newest information helps them to ensure their employability 3. Technological savvy: this generation has a keen interest not only in new technologies, but also how they work. 4. Preoccupied with maturity and adulthood: this generation expect to be treated as an adult, no matter how old they are.

Digital natives also use technology in different ways from their previous generations.

As the generation that was born and grew up with technology, they are able to use technology in a more diverse and more efficient way than their previous generation who dealt with technology in older age. A survey from Kvavik [START_REF] Kvavik | Convenience, Communications, and Control: How Students Use Technology[END_REF] shows how digital natives use technologies for wide arrays of activities, from productive activities such as writing documents and sending e-mails to recreational activities such as random browsing and chatting with friends and colleagues. Their activities also varied from using basic technology such as word processing and presentation to using advanced technologies such as 3D Graphics and web design. These characteristics and competencies are different from previous generations in which work and entertainment are more limited, and most of the work can takes a long time in the settlement process.

If Prensky [START_REF] Prensky | Digital Natives Digital Immigrants[END_REF] calls this digital generation "digital natives," the previous generation is called with a digital immigrant. The digital natives born and raised in the technology, but digital immigrants born before the technology was rampant. The digital natives became familiar with the technology at their adolescence, while the digital immigrant met these technologies in adulthood. With these different environment, comes different characteristics between the generations.

In the context of work for example, it can already be found digital natives who are smarter than the digital immigrants on operating software. The digital natives were able to find the information more quickly, and could complete certain work more efficiently with the help of the internet [START_REF] Palfrey | Born Digital: Understanding the First Generation of Digital Natives[END_REF]. In the context of education, students were found correcting information from their teachers, with the help of information found in the internet by smart phones [START_REF] Tapscott | Grown Up Digital: How the Net Generation is changing Your World[END_REF].

In Indonesia, the internet were started to use widely by the end of 1996 [START_REF] Hill | Wiring the warung to global gateways: The internet in Indonesia[END_REF]. In contrast to the characteristics of Internet used in the United States, where each house has its own connection, internet connection in Indonesia can be find communally in internet cafes, or commonly known as warnet [START_REF] Hill | Wiring the warung to global gateways: The internet in Indonesia[END_REF][START_REF] Lim | Internet, Social Networks, and Reform in Indonesia[END_REF] which has different characteristics from private pay system Although having different way to access the Internet, interestingly Indonesian people have similar pattern in terms of the diversity of activities done online with Western society. Such similarity depicted in a survey conducted by Wiradhany [START_REF] Wiradhany | Uji Validitas dan Reliabilitas Alat Ukur Trust pada Pengguna E-Commerce di Indonesia[END_REF], which shown that the Indonesian people are familiar with common functions such as internet browsing (surfing), chat room, and e-mail. Set of studies summarized by Hill and Sen [START_REF] Hill | Internet in Indonesia's New Democracy[END_REF] also showed that in general, Indonesia's young generation is using the internet. Campus ranks third as a place to access the internet; Undergraduate and High School Students ranked first and second levels of internet users; and students and youth as the two largest Internet user groups [START_REF] Hill | Internet in Indonesia's New Democracy[END_REF].

The goal of this paper is to identify ICT usage pattern of young internet user of Indonesia. From the previous findings, it is expected that it will show similar pattern with findings from Western Countries. Secondly, by comparing young internet users to their previous generation, this paper also wants to look up how much differently young internet user use technology compared to their previous generation.

The results of this research can enrich the discourse on the impact of technology to the people of Indonesia, since a similar study has not been done much. Psychological discourse of the digital generation can be widely diverse, ranging from discussion of internet addiction [START_REF] Young | Internet Addiction: The Emergence of a New Clinical Disorder[END_REF], the use of technology in the learning process [START_REF] Oblinger | Journal of Educating Net Generation. Educase[END_REF], the dynamics of interaction patterns in the family [START_REF] Rosen | Me, MySpace, and I: Parenting the Net Generation[END_REF], organizations that can maximize the potential of this generation [START_REF] Tapscott | Grown Up Digital: How the Net Generation is changing Your World[END_REF], and various other areas of Psychology in contact with the digital generation. The results of this study can be used as a basis for the study of digital natives in Indonesia in the future.

Methods

There are three variables in this study: age, ICT-related behavior, and ICT-related attitudes. Age were grouped by those who were born in the 1970s and before, those who born between the 1980 to the 1989, and those who born after 1990s. ICT-related behavior was described by (1) accessibility and gadgets owned (2) activities within the internet and gadgets and (3) the investment for technologies monthly.

The data in this study were obtained through a survey on technology ownership, internet access, use of internet, mobile phone use, computer use, use of game consoles, and attitudinal scales of the digital generation. Items in the general survey were developed based on Wiradhany [START_REF] Wiradhany | Uji Validitas dan Reliabilitas Alat Ukur Trust pada Pengguna E-Commerce di Indonesia[END_REF] findings, while the scale of the digital generation attributes were adopted from a similar scale made by Leung [START_REF] Leung | Net-Generation Attributes and Seductive Properties of the Internet as Predictors of Online Activities and Internet Addiction[END_REF].The digital generation attitudinal scale consists of four dimensions, namely (1) the right to information, [START_REF] Combes | Generation Y: Are they really digital natives or more like digital refugees?[END_REF] unhibition in the net, (3) technological savvy, and (4) adulthood and confident. The overall scale of the attribute consists of 24 items with an internal consistency of each of the items ranged from 0.25 to 0.47 and Cronbach Alpha reliability of 0.82.

Ninety-nine percent of respondents (304 people) write their gender. Of that quantity, 56.4% (172 people) of respondents are female, while the remainders (132 people) are men. They were born in the 1970s, 1980s, and 1990s. The digital generation was assumed born in 1990s. Generation born in the two decades prior is used as a comparison to see the contrast between the use of technology between digital generation and previous generations.

Result

Survey analyses were divided into two parts, namely (A) mapping of the various technologies used and (B) attitude toward technology. In the first part, the results of this survey will be presented to the (1) Variety of technologies used across generation, (2) the activities within Internet, and (3) The amount of the investments made to access the technology.

3.1

Mapping of the various technologies used 1. Variety of technologies used across the generations Figure 1 shows the difference in technology products ownership between digital generation and the previous generation. For those who were born in the 1970s and 1980s, USB flash drive (100% and 94%) are the most widely owned product, followed by mobile phones (85.7% and 92.8%), and Internet access/laptops (71.4% and 90.4%). In the group that was born in 1990s, three top technologies are a cell phone (95%), laptops (92.5%), and Internet access (90.5%). Data management (flash drive and laptops) and communication (mobile phone and internet) are agreed by whole groups as essentials technologies. High usage of laptops than PC, and mobile phone also shows how important mobility is. Our data from internet access also shows that internet hotspot, HSDPA modem, and mobile internet plays significant role for people to access internet.

Activities within Technologies

Efforts to understand the intensity and engagement of digital generation with technology were done by comparing activities within technology with the previous generation. Activities compared included: activities on mobile phone, PC/laptop, and internet. While all groups use mobile phone to send text and make phone calls, respondent born at the 1990s also used them for social networking and chat. If mobile phone is used mainly for organizing schedule, computer is mainly used to work on documents related to schoolwork or office.

All groups of respondents used computer more to access the Internet (100%, 96.4%, and 94%, respectively). Figure 2 shows that all groups go online mainly for checking their e-mails, browsing, downloading data, and social networking. It is interesting that younger people tend to do more recreational browsing while older generation browses for task-related activity.

Investing on Information Technology

Access to information technology in Indonesia is still a serious problem for its users. However, the price is often defeated by necessity. For the digital generation, information technology has become imperative. This survey shows that respondents born 1980s are the most likely group to spend money on the Internet than respondents born 1990s and 1970s. On average, respondents who were born in the 1970s to spend Rp. 57,857.14 per week for a mobile telephone, respondents were born in 1980s for Rp. 43,939.76 -and 1990s Rp. 40,055.13.

Another interesting investment observed to understand the information technologyrelated behavior is in terms of time. The data obtained showed that the range of time spent by respondents from each generation tend to be different. The respondents were born in the 1970s spent most of the time with their cell phones for noncommunication activities such as listening to a song and taking pictures (31.67 minutes) and check e-mail. Those born in the 1980s spent more time on noncommunication activities (25.38 minutes) and also for chatting (22.06 minutes). Diversity is becoming increasingly different on respondents born in 1990s because they mostly use his mobile phone to chat (56.42 minutes) and send short messages (33.10 minutes).

Chatting is a synchronous communication activity between two or more users of information technology. This mobile phones facilitated communication is widely used by the digital generation. Compared with communication via e-mail, chat allowed users of Internet technology to obtain near real-time response. Chat usage depicts speed as one of the characteristics of the digital generation.Speed is required by the digital generation not only in terms of obtaining the response, but also in fulfilling their needs of information. Browsing or surfing the internet is an activity done to meet these needs.

For activities within computer, the most time-consuming activities are web design, browsing, using Ms. Office, and offline gaming. No perceived differences between groups were found here. But interestingly, when asked about online activities, we found that respondents born on 1970s spent most of their time online for gaming and chat, while younger generation tend to do more browsing for task-related activities. Activities done to develop relationships that have been established through chat and online gaming seem to be two examples of activities done by1970s generation. Prensky [START_REF] Prensky | Digital Natives Digital Immigrants[END_REF] refer to this generation who born on the1970s as digital immigrants because the internet was present at the time they are adult. They are required to live and conform to the pattern of life in the era of digital technology. They treat technology as a tool to facilitate the achievement of their goals and enrich the quality of communication that had been established. In the country with a collectivist culture, social life is managed by growing need among individuals and another. Greeting each other is not just lip service but rather form of fulfillment of needs. Media that facilitates the echo-reply brief communication like chat become a very popular choice in Indonesia. Ramdhani [START_REF] Ramdhani | Komunikasi berbasis Paperless Office: Studi tentang PLO Psikologi UGM[END_REF] found that in the Paperless Office communication media, social topic is the most frequent topic discussed among university lecturers.

Different trend is shown by the generation born at the 1990s. They spent more time browsing for task-related activities. Internet technology already exist at the time of this generation hit adolescence, so doing job/school related activities are more important for them.

3.2

Attitude towards Technology

Utilization of various technologies for life is determined by the perception of how important each feature provided by these technologies. All generations put internet and mobile phones as the technology they need. The difference between them lies in the management of data. If the generation born 1970s requires a USB Flash Drive, those who were born in the 1990sand 1980s find laptops more important. There are at least three possibilities that could be used to understand this. First, laptop with various specification and prices can be easily found in market nowadays. Second, USB Flash drives can only be used for storing data, while laptop can be used for various purposes. Third, digital generation can utilize various features of the Internet to access their files (e.g. cloud storage), so that files can be accessed from anywhere without having to be burdened with the risks of the use of USB Flash Drive.

In addition to the popular range of technology, the survey also shows that the Game Console and tablet PCs including the iPad and the Galaxy tab is a technology that is considered less important than other technologies such as laptops and computers. This may be based on the assumption that console and PC games do not directly support productivity. However, Figure 4 Four attributes of the digital generation [START_REF] Leung | Net-Generation Attributes and Seductive Properties of the Internet as Predictors of Online Activities and Internet Addiction[END_REF] is elaborated by comparing the pattern of the four attributes among respondents who were born in the 1970s, 1980s, and 1990s. The data show that the distribution of scores of all respondent groups did not differ, with the exception of right to information. Younger people tend to demand higher right to information as the number of information stored online increases [START_REF] Palfrey | Born Digital: Understanding the First Generation of Digital Natives[END_REF]. However, contrary to the assumption of net generation, those born in the year 1980s has a higher net-generation attributes average score (70.18) than those born at the 1990s (68.79). The figure also shown that adulthood and confidence score has a posi-tive correlation with age. This is expected because people tend to be more confident as they become older.

Finally, we weight and compare all of our data regarding the five attributes related to technology (access, activities, cost, time, and attitude) with each other. We then compute their differences using ANOVA. From the comparison shown on table 5 it can be seen that there are no significant difference between access, activities, cost, time, and technology-related attitudes among those who were born in the 1970s, 1980s, and 1990s. This result is contrary to the first survey was done by the Americans.

Conclusion

While showing certain characteristics of digital generation, this survey find that those assumed as digital generation in Indonesia show no different pattern in technological access, activity, cost, time spent, and attitude from their previous two generations. These results are in line with Carrier, Cheever, Rosen, Benitez, and Chang [START_REF] Carrier | Multitasking across generations: Multitasking choices and difficulty ratings in three generations of Americans[END_REF] who found that there were no significant multitasking behavior patterns as sign of digital generation across different age groups in the United States. Other surveys done in the U.K. [START_REF] Helsper | Digital natives: Where is the evidence?[END_REF][START_REF] Margaryn | Are digital natives a myth or reality?: Students' use of technologies for learning[END_REF]; and Australia [START_REF] Combes | Generation Y: Are they really digital natives or more like digital refugees?[END_REF][START_REF] Kennedy | First year students' experiences with technology: Are they really digital natives? Preliminary Reports of Findings[END_REF] have also shown similar pattern. While reporting themselves as more engaged in using different kinds of technologies, those assumed as digital generation are too heterogeneous to be classified as a group: only a minority of them is truly technological savvy. Most of individual fall in this group are not more technologically illiterate than their previous generation.

However, results from this survey came from limited numbers of respondents. It seems that the digital generation of Indonesia behavioral patterns cannot be described with a limited number of respondents in the study. In addition, Indonesia also faced the inclusion of Internet technology. The responsiveness of inter-generational Internet users in Indonesia still needs to be studied further.

Fig. 1 .

 1 Fig.1. Technology Owned across Generation

Fig. 2 .

 2 Fig. 2. Online Activities

Fig. 3 .

 3 Fig. 3. Time Spent Online

Table 1 .

 1 Age of Respondents percent of the respondents (289) wrote their date of birth. The mean of respondent's age was 21.96 with 4.26 SD. The youngest respondent is 13 years old and the oldest respondent is 60 years old by 2012.

	Decades	Respondents
	<1979	7
	1980-1989	83
	≥1990	199
	Total	289
	Ninety-four

  also shows that music player is pretty much used by all three generation.

	Importance	1.00 1.50 2.00 2.50 3.00 3.50 4.50 4.00 0.50 0.00	Digital Camera	External Hard …	Laptop/Netbook	USB Flash Drive	PC/Desktop	Game Console …	Internet Access	Tablet PC …	Mobile Phone	MP3 Player	1970s 1980s 1990s
							Gadgets
	Fig. 4. Importance of Gadgets (1=Very Unimportant; 4=Very Important)

Table 2 .

 2 Net Generation Model Comparison (N = 289)

					Time
	Access	Activity	Cost	Spent	Attitude
	Total Mean	3.260	3.372	3.264	3.272	2.876
	Std. Devia-	1.186	0.842	2.836	2.513	0.428
	tion
	F	0.961	0.874	1.476	0.357	0.086
	Sig.*	0.384	0.418	0.230	0.700	0.917
	* Significant for p<0.05


Ng Liang Shen
email: ngliangshen@gmail.com

Noraniah Abdul Aziz

Tutut Herawan

End-to-End Delay Performance for VoIP on LTE System in Access Network

Keywords: Quality of Service (QoS), Voice over Internet Protocol (VOIP), Internet, performance, Future, Network Access ,analysis, delay

The desire Quality of Service (QoS) of Voice over Internet Protocol (VOIP) is of growing importance for research and study Long Term Evolution (LTE) is the last step towards the 4 th generation of cellular networks. This revolution is necessitated by the unceasing increase in demand for high speed connection on LTE networks particularly for under variable mobility speed for VoIP in the LTE. This paper mainly focuses on performance of VOIP and the impact of resource limitations in the performance of Access Networks particularly important in regions where Internet resources are limited and the cost of improving these resources is prohibitive. By determine rate communication quality, is determined by end to end delay on the communication path, delay variation, packet loss. These performance indicators can be measured and the contribution in the access network can be estimated using simulation tool OPNET Modeler in varying mobility speed of the node. The overall performance of VOIP thus greatly improved significantly by deploying OPNET Modeler.

Introduction

The startling growth of Internet technology, coupled with the relatively low deployment cost of IP networks, has pushed for an integrated "IP-based core" -a single network for data, video and voice access. However, the diverse servicerequirements and novel traffic characteristics of the emerging Internet applications have posed many technical challenges that the Internet community must address in the near future, as the emerging multimedia applications begin to constitute an everincreasing fraction of Internet traffic. High quality interactive voice and video applications can tolerate little delay variation and packet loss. Quality of Service (QoS) is a defined level of performance in a data communications system. As an example, to ensure that real time voice and video are delivered without irritating blips, a guaranteed bandwidth is required. The plain old telephone system (POTS) has delivered the highest quality of service for years, because there is a dedicated channel between parties. However, when data is broken into packets that travel through the same routers in the LAN or WAN with all other data, QoS mechanisms are the only way to guarantee quality by giving real time data priority over non-real time data. A large number of factors are involved in making a high-quality VoIP call. These factors include the speech codec, packetization delay, packet loss, delay (coding, transmission, propagation and queuing), delay variation, and the network architecture to provide QoS. Other factors involved in making a successful VoIP call include the call setup signalling protocol, call admission control, security concerns, and the ability to traverse Network Access Translation (NAT). Although VoIP involves the transmission of digitized voice in packets, the telephone itself may be analog or digital. The voice may be digitized and encoded either before or concurrently with packetization. In making a high quality VOIP call and the engineering trade-offs that must be made between delay and the efficient use of bandwidth [START_REF]Overview of 3GPP Release 8: Summery of all Release 8 Features[END_REF][START_REF] Puttonen | Voice-Over-IP Performance in UTRA Long Term Evolution Downlink[END_REF]. VoIP becoming popular can be mainly attributed to the cost advantages to consumers over traditional telephone networks. The traditional business model for telephone services has been that most people pay a flat monthly fee for local telephone call service and a perminute charge for long-distance calls.Introduction of Long Term Evolution (LTE), the 4th Generation (4G) network technology release 8 specifications are being finalized in 3GPP have developed and planning to globalize extensively compared to 3rdGeneration (3G) and 2nd Generation (2G) networks [START_REF]Overview of 3GPP Release 8: Summery of all Release 8 Features[END_REF]. LTE determines goals peak data rate for Downlink (DL) 100 Mbps and Uplink (UL) data rate for 50Mbps, increased cell edge user throughput, improved spectral efficiency and scalable bandwidth 1.4 MHz to 20 MHz [START_REF] Puttonen | Impact of Control Channel Limitations on the LTE VoIP Capacity[END_REF]. VoIP capacity of LTE has to show better performance as Circuit Switch voice of UMTS. LTE should be at least as good as the High Speed Packet Access (HSPA) evolution track also in voice traffic. It discusses the challenging issues that need to be faced by computer networks to transmit the VoIP applications. It gives the description idea about the VoIP over LTE and their functionality and design parameters of the LTE networks. After determining the problems it is necessary to identify the research questions that lead the research process to be in the scope [START_REF] Puttonen | Impact of Control Channel Limitations on the LTE VoIP Capacity[END_REF]. a) How much the maximum throughput is support in the different bandwidth (e.g. The 3GPP LTE is a new standard with comprehensive performance targets, therefore it is necessary to evaluate the performance and stability of this new system at an early stage to promote its smooth and cost-efficient introduction and deployment. The motivation behind the design models presented in this report is to discuss issues related to traffic behavior for VoIP alone as well as along with other traffic in the LTE network. E2E delay for VoIP is a matter of fact for performing real-time application efficiently over the Internet. Today, emergence of the real-time application demands more resources. The main motivation of our paper work is to ensure fast and reliable voice communication for huge number of users in wireless network. LTE evolved packet system (EPS) is the bearer of the QoS level of granularity. This system also establishes the packet flow between the user terminal (UE or MS) and the packet data network gateway (PDN-GW). The traffic running between a particular client application and the service can be wrecked into split service data flows (SDFs). Mapping the same bearer, SDFs receive common QoS activities (e.g., scheduling policy, queue management policy, rate shaping policy, and radio link control (RLC) configuration). A scalar value referred to as a QoS class identifier (QCI) with the help of bearer, specifies the class to which the bearer belongs. Set of packet forwarding treatments referred by QCI (e.g., weights scheduling, ad-mission thresholds, configuration of link layer protocol and queue management thresholds) preconfigured through the operator on behalf of each network element. The class-based technique applies in the LTE system to improve the scalability of the QoS framework. In the LTE framework, bearer management and control follows the network-initiated QoS control paradigm that initiated network establishment, modification, and deletion of the bearers. Two types of bearers in LTE [START_REF] Puttonen | Impact of Control Channel Limitations on the LTE VoIP Capacity[END_REF][START_REF] Puttonen | Voice-Over-IP Performance in UTRA Long Term Evolution Downlink[END_REF][START_REF] Khan | LTE for 4G Mobile Broadband: Air Interface Technologies and Performance[END_REF]: a. Guaranteed bit rate (GBR): Dedicated network resources correlated to a GBR value connected with the bearer and permanently allocated when a bearer becomes established or modified. b. Non-guaranteed bit rate (non-GBR): In the LTE system, non-GBR bearer is assigned as the default bearer, similar to the preliminary SF in WiMAX, used to establish the IP connectivity. A non-GBR bearer has enough knowledge about congestion-related packet loss. In the framework, additional bearer is assigned as a dedicated bearer which is GBR or non-GBR. Future Research need to cover further analyses could be carried out using the measurement data captured. A synthetic workload model for VoIP could be further developed, based on the workload characteristics identified in the measurement study.

How is the Performance for VoIP on LTE System in Access Network

In LTE, the core network operations are completely based on packet switching domain, example, all the network interfaces are dependent on IP protocols, and hence it is known as Evolved Packet Core (EPC). The essence of EPC is to keep the number of operating nodes and interfaces as minimum as possible. The EPC divides the network components into control-plane objects such as data/barer-plane entity (i.e. a Serving Gateway) and the Mobility Management Entity (MME). The MME is considered as a signaling entity and used to represent the control plane function of the EPC. Such control functions include, among others, location function, the subscribers' equipment paging, and the bearer establishment and the connections establishment, roaming management [START_REF] Khan | LTE for 4G Mobile Broadband: Air Interface Technologies and Performance[END_REF]. The fundamental architecture of LTE system is presented in Figure 1.

All the network interfaces are based on internet protocols (IP). The LTE system comprised of the core network and radio access network which represent the IP connectivity layer of LTE system The Serving Gateway (S-GW) functions as switching as well as routing node to route and forward the data packets to and from the Base or Evolved-Universal Terrestrial Radio Access Network NodeB (eNB) [START_REF] Martín-Sacristán | On the Way towards Fourth-Generation Mobile: 3GPP LTE and LTE-Advanced[END_REF]. Between the EPC and the external packet data network, a PDN-GW is often used as an inter-face point or an edge router. It is also possible that a UE has synchronized connectivity with more than one PDN GW.

The responsibilities of the PDN-GW include establishment, maintenance, and deletion of GTP tunnels to S-GW or SGSN in the case of inter-RAT mobility scenarios. The PDN-GW routes the user plane packets by allocating the user's dynamic IP addresses.

Apart from that, it provides functions for lawful interception, policy/QoS control, and charging [START_REF] Holma | LTE for UMTS -OFDMA and SC-FDMA Based Radio Access[END_REF].

LTE QoS Framework and Voice over IP (VoIP)

LTE evolved packet system (EPS) is the bearer of the QoS level of granularity. This system also establishes the packet flow between the user terminal (UE or MS) and the packet data network gateway (PDN-GW). The traffic running between a particular client application and the service can be wrecked into split service data flows (SDFs).

Mapping the same bearer, SDFs receive common QoS activities (e.g., scheduling policy, queue management policy, rate shaping policy, and radio link control (RLC) configuration) [START_REF] Muhleisen | Evaluation and Improvement of VoIP Capacity for LTE[END_REF][START_REF] Holma | LTE for UMTS: OFDMA and SC-FDM Based Radio Access[END_REF]. A scalar value referred to as a QoS class identifier (QCI) with the help of bearer, specifies the class to which the bearer belongs. Set of packet forwarding treatments referred by QCI (example, weights scheduling, admission thresholds, configuration of link layer protocol and queue management thresholds) preconfigured through the operator on behalf of each network element [START_REF]Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestri-al Radio Access Network (E-UTRAN)[END_REF]. The classbased technique applies in the LTE system to improve the scalability of the QoS framework. [START_REF] Olorunda | Bridging the Digital Divide -The Social and Cultural Impact of VoIP in Developing Countries: Nigeria as a Case Study[END_REF].

Two types of bearers in LTE

There are two types of bearer in LTE which are GBR and non-GBR. In the framework, additional bearer is assigned as a dedicated bearer which is GBR or non-GBR. Real Transport Protocol (RTP) is the basic protocol in VoIP engineering, which is, not only for transporting media streams but also to initialize the media session in concord with SIP. It is also used for media stream supervision and intended to provide out-of-band control information for the RTP flow. In response to the media quality that supplies to the other members in the media session via separate UDP port, there are many additional functionalities of RTP. Audio and video synchronization and quality improvements through low compression instead of high compression are a few of them. VoIP transmissions are deployed through traditional routing. A typical VoIP structural design is shown on the Figure 2, though many "possible" modifications of this architecture are implemented in existing systems. In VoIP engineering, original voice signal is sampled and is encoded to a constant bit rate digital stream at the end of the sending process.In place of circuit-switched voice transmission and traditional dedicated lines, these packets flow over a general-purpose packet-switched digital to analog signal in the receiving end for it to be easily detected. The VoIP infrastructure can be visualized as three layers: end user equipment, network components, and a gateway to the traditional telephone network as stated in Figure 3. According to [START_REF]One Way Transmission Time[END_REF], for voice signal, 250 ms is the maximum tolerable mouth-to-ear delay and around 100 ms delay for the Core Network and also less than 150 ms acceptable delay for Medium Access Control (MAC) buffering and Radio Link Control (RLC). Both end users are LTE users and assume less than 80 ms acceptable delay for buffering and scheduling. For 3 GPP performance evaluations 50 ms delay has been bound for variability in network endto-end delays. End-to-end delay means the time required for a packet to be traversed from source to destination in the network and is measured in seconds. Generally, in VoIP network there are three types of delays occurring during the packet transverse. They are: sender delays when packets are transverse from source node, network delay and receiver delay. In one direction from sender to receiver for VoIP stream flow, end-to-end delay can be calculated by the equation. Let say in seconds-that a network needs to send a message with n bytes as  

n n T    
In mathematical terms, this is a line equation, where α is the constant, and β is the line slope or gradient [START_REF]One Way Transmission Time[END_REF][START_REF] Karam | Analysis of delay and delay jitter of voice traffic in the Internet[END_REF].

Simulation Design and Methodology

In real world scenarios, performance evaluation of a well-designed network model and the model itself carries significant importance. To cope with the challenge, practice using simulator OPNET (Optimized Network Engineering Tool), as kernel source code is not open source. [START_REF][END_REF]12]. In order to determine the maximum number of calls that can be supported by an existing network, while limiting VoIP delay constraint utilize queuing analysis to approximate and determine the maximum number of calls that the existing network can support while maintaining a delay of less than 80ms.,the Principles of Jackson theorem for analysing queing [12]. Analysis by decomposition is summarized in first isolating the queuing network into subsystems, example, and single queuing node [START_REF] Salah | An OPNET-based simulation approach for deploying VoIP[END_REF][START_REF] Dantu | Issues and challenges in securing VoIP[END_REF][START_REF] Salah | An Analytical Tool to Assess Readiness of Existing Networks for Deploying IP Telephony[END_REF][START_REF] Masum | End-to-End Delay Performance Evaluation for VoIP in the LTE network[END_REF]. Next, analysing each subsystem separately, considering its own network surroundings of arrivals and departures. Then, finding the average delay for each individual queuing subsystem finally, aggregating all the delays of queuing subsystems to find the average total endto-end network delay [START_REF] Gupta | Performance issues for VOIP in Access Networks[END_REF][START_REF] He | Analysing the Characteristics of VoIP Traffic[END_REF][START_REF] Muhleisen | Evaluation and Improvement of VoIP Capacity for LTE[END_REF][START_REF] Holma | LTE for UMTS: OFDMA and SC-FDM Based Radio Access[END_REF]. Figure 4 presents an algorithm computes the maximum number of calls considering VoIP delay constraint. a. Initially, no calls are introduced only traffic in the network background traffic. b. A new call is added, according to the call distribution. c. For each network element, λ = λVoIP +λbg is computed. Where, λVoIP is the total added new traffic from a single VoIP in pps, and λbg is the background traffic in pps). The value λbg is known for each element; however, λVoIP can get affected by introducing a new call depending on the call traffic flow, i.e. whether or not the new call flow passes through the network element. d. For each network element, the average delay of a VoIP packet is computed. e. The end-to-end delay is computed by summing up all the delays of step (d) encountered for each possible VoIP flow. This includes all external and internal flows, with internal flows consisting of intra-floor and inter-floor. f. If the maximum network delay is less than 80 ms, then the maximum number of calls has not been reached. Therefore a new call can be added, and hence go to steps (b)-(f). If not, the maximum delay has been reached. Therefore the number of VoIP calls bounded by the delay is one less than the last call addition.

Fig. 4: Algorithms number of calls VoIP [START_REF] Olorunda | Bridging the Digital Divide -The Social and Cultural Impact of VoIP in Developing Countries: Nigeria as a Case Study[END_REF] 5 Result and Analysis

End-to-End (E2E) Delay Performance

For VoIP applications, the packet E2E delay should not exceed 150 ms to evaluate that the quality of the created VoIP calls are accepted. In this section, the packet E2E delays result for different scenarios are presented in various statistical plots. Scenario 1, correspond to the Baseline VoIP, while Scenario 2 correspond to the Congested with VoIP. In all the scenarios, the sources and destinations happen to be started at 100 seconds as the start time of the profile and application configuration has been set to 40 seconds and 60 seconds, respectively. the figures presented in following sections, X axis represents simulation time in seconds; Y axis represents in seconds. Figure 5 illustrates the comparable performance of the E2E delay under the different case scenarios of Baseline VoIP network. E2E delay is measured for the VoIP traffic flows between source node and destination node through Baseline network with various node speeds the blue line shows the E2E delay of the scenario where the node speed is fixed (0 mps). The blue line show the E2E delay of scenarios where the node speeds are 23.83 mps, whereas red line shows in Figure 6, the congested VoIP network level of about 60.84 mps. With the increasing simulation time, the both curves settle at Simulation time 2.5 sec.

Packet Loss Performance for Baseline VoIP Network

In congested VoIP network. Can observe in figure, sent and received data are overlapped between 100 seconds to 140 seconds in all the simulation scenarios. After 140 seconds when the load is around 95%, the packet loss is found in all the cases. In the Congested VoIP and VoIP congested with FTP network scenario, on an average, packet loss in the VoIP Congested with FTP network for case 1 is about 74% higher than that of Congested VoIP network. In other both cases, average packet losses are 88%, 95% and 84% higher respectively. To wrap it, all of the voice traffic corresponding to each case in VoIP Congested with FTP experience higher packet loss than Congested VoIP network.

Conclusion

Comprehensive study, analysis and evaluation of the end-to-end delay performance evaluation for VoIP in the LTE network have been done. The evaluation is made by simulating in OPNET Modeler based on performance metrics such as end-to-end delay and throughput. Three network scenarios have been simulated: Baseline VoIP network scenario, congested VoIP network scenario and VoIP congested with FTP network scenario. It has been found that maximum throughput increased as the bandwidth incremented. Out of the three methods simulated for throughput measurement, the scenario with highest bandwidth (example 20 MHz) exhibited maximum throughput.

After that, quality measurement of VoIP has been done with respect to E2E delay, both for a network congested exclusively with VoIP and VoIP with FTP. Four scenarios have been created for this evaluation, one case with stationary node and other three cases with mobile nodes (gradually increasing the node speed). The simulation results showed that when the node is not moving, E2E delay is slightly higher for network congested with VoIP only. In other cases, better E2E is obtained for this network due to the presence of moving node. Finally the rate of packet loss for the remains quite minimal for congested with VoIP network regardless of the node speed. Mean-while, for VoIP with FTP network, packet loss rate is also quite insignificant for fixed node case but the rate upshots as the node starts moving [START_REF] Muhleisen | Evaluation and Improvement of VoIP Capacity for LTE[END_REF][START_REF] Holma | LTE for UMTS: OFDMA and SC-FDM Based Radio Access[END_REF].

1 . 4

 14 MHz, 3 MHz, 5 MHz, 10 MHz, 15 MHz and 20 MHz)? b) What is the impact on the VoIP quality in terms of E2E delay when the network is congested with VoIP only? c) To what extent do the performances of packet loss for interactive voice vary?

Fig. 1 :

 1 Fig.1: Networks Architecture of LTE[START_REF] Masum | End-to-End Delay Performance Evaluation for VoIP in the LTE network[END_REF]

Fig. 2 : 3 :

 23 Fig. 2: VoIP Architecture of LTE Fig.3: VOIP. Network Archtecture 3.2 Characteristics VoIP traffic delay AMR codec provides the VoIP traffic along with the Voice Activity Detector, Relieve Noise Generation and Discontinuous Transmission. Depending on the speed activity of the traffic, AMR provides a constant rate of small packets transmission. During the active period, one VoIP packet took at 20 ms intervals and 160 ms interval for one Silence Description (SID) packet during silent period. To improve the spectral efficiency of the VoIP traffic, UDP, IP and RTP headers in LTE are also compressed with Robust Header Compression (ROHC).According to[START_REF]One Way Transmission Time[END_REF], for voice signal, 250 ms is the maximum tolerable mouth-to-ear delay and around 100 ms delay for the Core Network and also less than 150 ms acceptable delay for Medium Access Control

  : maximum number of calls // network delay for VoIP call in ms [ ]

Fig. 5 :Fig. 6 :

 56 Fig. 5: End-to-End Delay of Baseline Network

Table 1 .

 1 Statistics of E2E delay of Based-line Congested VoIP Network

	Baseline	Congestion	Simulation	Baseline	Congestion	Simulation
	Before	End to End	time	Before	End to	time
	Congestion	Delay		Congestion	End Delay
	23	75	3.26	48.82	49.18	1.01
	23.83	74.17	3.11	49.66	48.34	0.97
	24.67	73.33	2.97	50.49	47.51	0.94
	25.50	72.50	2.84	51.32	46.68	0.91
	26.33	71.67	2.72	52.16	45.85	0.88
	27.17	70.84	2.61	52.99	45.01	0.85
	28.00	70.00	2.50	53.82	44.18	0.82
	28.83	69.17	2.40	54.65	43.35	0.79
	29.66	68.34	2.30	55.49	42.51	0.77
	30.50	67.50	2.21	56.32	41.68	0.74
	31.33	66.67	2.13	57.15	40.85	0.71
	32.16	65.84	2.05	57.99	40.01	0.69
	33.00	65.00	1.97	58.82	39.18	0.67
	33.83	64.17	1.90	59.65	38.35	0.64
	33.83	64.17	1.90	60.49	37.52	0.62
	34.66	63.34	1.83	61.32	36.68	0.60
	35.50	62.51	1.76	62.15	35.85	0.58
	36.33	61.67	1.70	62.98	35.02	0.56
	37.16	60.84	1.64	63.82	34.18	0.54
	37.99	60.01	1.58	64.65	33.35	0.52
	38.83	59.17	1.52	65.48	32.52	0.50
	39.66	58.34	1.47	66.32	31.68	0.48
	40.49	57.51	1.42	67.15	30.85	0.46
	41.33	56.67	1.37	67.98	30.02	0.44
	42.16	55.84	1.32	68.82	29.19	0.42
	42.99	55.01	1.28	69.65	28.35	0.41
	43.83	54.18	1.24	70.48	27.52	0.39
	44.66	53.34	1.19	71.31	26.69	0.37
	45.49	52.51	1.15	72.15	25.85	0.36
	46.32	51.68	1.12	72.98	25.02	0.34
	47.16	50.84	1.08	73.81	24.19	0.33
	47.99	50.01	1.04	74.65	23.35	0.31
	Packet loss refers to the failure of one or more transmitted packets to reach their
	destination across a network. A VoIP user is satisfied if more than 98 % of its voice
	packets are delivered successfully. Packet loss is determined using the following
	equation.