Harmonic RES

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In each channel of all four panels, there is a local maximum at the F0 of the stimulus, and together these peaks produce a vertical ridge in each panel that corresponds to the pitch that the listener hears. In the upper panels [ Figs. The corresponding correlograms of Slaney and Lyon and the autocorrelograms of Meddis and Hewitt would have a similar form in as much as there would be local peaks at F0 and prominent modulation for the stimuli where the lowest component is the 11th, but the pattern of activity within the period of the sound would be blurred and the envelope of the modulation would be more symmetric.

High Performance Audio System

Stabilized auditory images SAI of four harmonic complexes. All stimuli have eight consecutive harmonics; they differ in their fundamentals and lowest components. Side panels show the spectral profiles vertical and temporal profiles horizontal of the auditory images. The vertical and horizontal side panels to the right and below each sub-figure show the average of the activity in the auditory image across one of the dimensions. The average over time interval is shown in the vertical, or spectral, profile; the average over frequency or channels is shown in the horizontal, or temporal, profile.

The unit on the time-interval axis is the frequency equivalent of time interval, that is, time interval It is used to make the spectral and temporal profiles directly comparable. The spectral profile of the auditory image is very similar to that of the corresponding NAP. The temporal profile of the auditory image shows that the timing information in the neural pattern of these stimuli is very regular, and if the auditory system has access to this information it could be used to explain pitch perception.

The advantage of time-domain models of auditory processing is that the spectral and temporal profiles are derived from a common simulation of the information in the auditory nerve, which facilitates comparison of the spectral and temporal pitch models based on such profiles. Moreover, the parameters of the filterbank are derived from separate, masking experiments, so the resulting models have the potential to explain pitch and masking within a unified framework. In the spectral profile, when the lowest component is increased from three to eleven, the profile ceases to resolve individual components.

This is shown by comparing the peaky spectral profile for the stimulus with a LC of 3 in Fig. The effect of increasing LC is similar for the lower F0 in the left column, but the harmonic resolution is reduced in both cases. In the temporal profile, when the lowest component is increased from three to eleven, the pronounced peak at 75 Hz in the left-hand column remains; compare Figs.

The Hz peak in the temporal profile in the right-hand column becomes much less pronounced relative to the surrounding activity, [compare Figs. As F0 is increased from 75 to Hz, activity in the spectral profile shifts up along the frequency axis. For the stimuli with higher order components [ Figs. But for the stimuli with lower order components [ Figs. As a result, a model based on spectral profiles would predict a that performance for stimuli with higher order components will be poor independent of F0, and b that performance for stimuli with lower order components will be better for the higher F0 Hz.

As F0 is increased from 75 to Hz, the peak in the temporal profile shifts to the right along the time-interval axis. For stimuli with lower order components [ Figs. For stimuli with higher order components [ Figs. So, a model based on temporal profiles would predict reasonable performance for the low F0 and poorer performance for the higher F0.

Thus, there is a clear difference between the predictions of the two classes of model. The melody task is based on the procedure described previously by Patterson et al. Listeners were presented with two successive melodies. The second melody was a repetition of the first but had one of the notes changed by one diatonic interval up or down.

The task for the listener was to identify which note had changed in the second melody. Melodies consisted of four notes from the diatonic major scale.


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The structure of the notes was such that only the residue pitch was consistent with the musical scale, and that sinusoidal pitch could not be used to make judgments. A melody task was used rather than a pitch discrimination task as it is a better measure of pitch strength. The notes in the melodies were synthesized from a harmonic series whose lowest components were missing.

The pitch of the note corresponded to the F0 of the harmonic series. Performance on a melody task was measured as a function of three parameters: The ALC was 3, 7, 11, or The NC was either 4 or 8. Stimuli were generated using matlab ; they had a sampling rate of 48 kHz and 16 bit amplitude resolution. They were played using an Audigy-2 soundcard.

The duration of each note was ms, which included a ms raised cosine onset and a ms raised cosine offset. Difference tones in the region of F0 and its immediate harmonics Pressnitzer and Patterson, were masked by bandpass filtered white noise; the frequency range was Hz for the lower F0 and Hz for the higher F0. The level of the noise was 50 dB SPL. Cubic difference tones just below the lowest harmonic were not masked as this would involve inserting a loud noise that would overlap in the spectrum with the stimulus.

Cubic difference tones might increase pitch strength slightly in all conditions, but they would not be expected to contribute a distinctive cue to the melody that would affect performance differentially for a particular F0 or lowest harmonic number. The experiment was run in an IAC double-walled, sound-isolated booth. Three listeners participated in the first experiment; their ages ranged from 20 to 26 years.

SA Amplifier Harmonics Measurement

All listeners had normal hearing thresholds at Hz, 1, 2, and 4 kHz. Listeners were not chosen on the basis of musical ability, but two of the listeners were trained musicians. All listeners were paid at an hourly rate. Listeners were trained on the melody task over a 2 h period, although they would be allowed to take frequent breaks so the actual training time was somewhat less than 2 h. The training program varied between listeners. Typically it involved starting with an easy condition having eight components, an ALC of three, and no roving of the lowest component.

The difficulty of the task was then increased by including stimuli with fewer components i. Three potential listeners were rejected after the training period because they were unable to learn the task sufficiently well within the allotted time. Listeners were presented with two consecutive four note melodies. The second melody had one of the notes changed, and listeners had to identify the interval with the changed note. The procedure is illustrated schematically in Fig. The two melodies are presented in the second and fourth bars; the tonic, which defines the scale for the trial, is presented twice before each of the melodies, as a pick up in the third and fourth beats of the first and third bars.

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Feedback was then given as to which note actually changed, then another trial begun. In the example shown in Fig. Schematic of the procedure of the melody task, adapted from Patterson et al. One note changes by a single diatonic interval between the first and second presentations of the melody, and the listener has to identify the changed note, marked here by a grey square.

The notes of the melodies were harmonic complexes without their lowest components. The melody was defined as the sequence of fundamentals that is, the residue pitch rather than the sequence of intervals associated with any of the component sinusoids.


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    • On each trial, the F0 of the tonic was randomly selected from a half-octave range, centered logarithmically on F0. The actual ranges were and Hz.

    I. INTRODUCTION

    The intervals are musical but, due to the randomizing of the F0, the notes of the melodies are only rarely the notes found on the A keyboard. The purpose of randomizing the F0 of the tonic was to force the listeners to using musical intervals rather than absolute frequencies to perform the task. The notes of the first melody of a trial were drawn randomly, with replacement, from the first five notes of the diatonic scale based on the randomly chosen tonic for that trial.

    The melody was repeated in the same key, and one of the notes was shifted up or down by a single diatonic interval. This shift can result in either a tone or a semitone change, since the size of a diatonic interval depends on its position in the scale. The LC of each note in each melody was subjected to a restricted rove, the purpose of which was to preclude the use of the sinusoidal pitch of one of the components to perform the task.

    There were two further restrictions on the value of the LC: First, adjacent notes in a melody were precluded from having the same LC; second, each note had a different LC in the second melody from that which it had in the first melody. With these restrictions, it sufficed to alternate between the LC and the one above it using one of the patterns or for the first melody and the other pattern for the second melody.

    The order of these 16 conditions was randomized, and together they constituted one replication of the experiment. The listeners performed three or four replications in a 20 min block, with four or five blocks in a 2 h session. All listeners completed 45 or 46 replications. The average results for the three listeners are shown in Fig. The abscissa shows the ALC of the harmonic series; the ordinate shows the probability of the listener correctly identifying which of the notes changed in the second melody.

    The solid and dashed lines show the results for the four- and eight-harmonic stimuli, respectively. Figure 4 shows that, as ALC is increased, performance decreases, i. However, the effect is much more marked for the Hz F0, where performance decreases abruptly as ALC increases beyond 7. This is the most important result, as it differentiates the spectral and temporal models: Strictly spectral models would predict that there should be no reduction in listener performance when F0 is increased; indeed, performance should improve slightly with increasing F0 because the auditory filter becomes relatively narrower at higher center frequencies.

    Temporal models predict that there will be a decrease in performance with increasing F0 because of the progressive reduction in the phase locking of nerve fibers. The effect of increasing NC from four to eight had no consistent effect on listener performance. Performance on the melody task with the 75 and Hz fundamentals. The abscissa shows the average lowest component and the ordinate shows the probability of the listener correctly identifying the note which changed.

    Performance is plotted for each NC condition as a function of average lowest component. Prior to running the main experiment, two similar ancillary experiments were performed. They are presented briefly here inasmuch as they provide additional data concerning the effects observed in the main experiment, and they provide data on the effects of a larger component rove. The experimental task and the procedure were the same as those described for the main experiment in Sec. See All Spectrum Analyzer Examples. This measurement example helps you understand the basic operation of the Spectrum Analyzer application and how to optimize the measurement setup to avoid instrument-generated spurious responses.

    This example measures the harmonics at maximum gain of an amplifier and confirms that the harmonics are solely caused by the amplifier, not by the PNA source or receivers. On the PNA front panel, press Preset. Press [Power Level] and set the Port 1 source power to be in a linear region of the amplifier's output response. Press Meas and select [S21]. You should see the amplifier's frequency response as shown in the following example.

    Press Search then [Max] to place a marker on the maximum gain. The HRS Isolation Bases are a primary element for significantly reducing structural borne noise in any audio or video component. They are designed to be used on any surface, with your existing furniture or can load at any time directly into the HRS Audio Stand Frame Systems. They are manufactured from billet machined aluminum alloys and a custom polymer material developed by HRS specifically for the audio industry.


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