

In [ ]:

    
import os, sys
import numpy as np
from TimeFreqAuditoryScene import *
from IPython.display import Audio, display, clear_output
from IPython.html import widgets
from IPython.html.widgets import interactive
%matplotlib inline

Shepard tones ascending/descending sequences testing the reset effect

I here generate a sequence of shepard tones with a constant interval in semitones

One can vary

the interval between consecutive tones
the duration of tones
the delay between tones

Because of the periodic nature of shepard tones with base frequency, sequences are periodic

Perceptually, a 'reset' is experienced when the first tone of the sequence is reheard

This is interesting because it is not due to an intrinsic property of the stimulus.



In [ ]:

    
# Parameterization

# Global parameters
fs = 44100
# Shepard tones
short_delay = 0.1
delay =0.1
duration = 1./8.
# declare gaussian envelope on log frequency
mu_log=np.log(200)
sigma_log=2.
genv = GaussianSpectralEnvelope(mu_log=mu_log, sigma_log=sigma_log)

fb1 = 12.
# sequence
n_sequence = 26
st_step = 3



In [ ]:

    
def reset(st_step=1, delay=0.1, duration=1./8.):
    scene = Scene()
    run_time = 0
    # Constructing the sequence
    sequence = []

    for i in range(n_sequence):
        tmp_st = ShepardTone(fb=fb1*2.**(i*st_step/12.), env=genv, delay=run_time, duration=duration)
        run_time += duration + delay
        sequence.append(tmp_st)
        # uncomment the 3 next line for repetition
        tmp_st = ShepardTone(fb=fb1*2.**(i*st_step/12.), env=genv, delay=run_time, duration=duration)
        run_time += duration + short_delay
        sequence.append(tmp_st)

    scene.add(sequence)
    # draw spectrogram
    sd = SceneDrawer()
    sd.draw(scene)
    plt.show()

    # generate sound
    x = scene.generate(fs=fs)
    display(Audio(x, rate=fs, autoplay=True))

w = interactive(reset, st_step=(-6,6), delay=(0.,2), duration =(0.1,1.))
display(w)