Aerospace and Electronic Systems Magazine February 2018 - 54
Audio Signals to Investigate Alleged Paranormal Phenomena
steps for the time delays τ. In addition, interpolation methods have
been applied, with parabolic interpolation [9] yielding slight improvements, especially for the 48 kHz recordings.
To geometrically calculate the 3D position of the sound source,
three functions, e.g. R1,2, R1,3, R1,4 with k = 1 being the center microphone, would be sufficient, simply choosing the delays τ1,l of their
global maxima (τ1,l is positive or negative depending on which microphone is reached first by the signal). By including the additional
GCC-functions, the solution becomes overdetermined, so rather
than independently choosing the maxima of each GCC-function,
a global optimization is needed. A common approach is the SRPPHAT algorithm [10], which walks through the 3D-coordinate
space to find the maximum sum of all GCC-functions.
Here, a slightly different approach is taken. Because of the
room size and centimeter-accuracy there would have been a huge
computational effort, which even for offline processing would not
have been practical. Optimizations limiting the search to certain
regions, e.g. based on previous localization results or coarse-tofine searches [11] were not considered; during the séance audio
events may quickly jump around, when several people are talking,
and the risk of wrong results had to be minimized. (The localization of different, simultaneous acoustic events and blind source
separation [12], so far have not yet been addressed.) Instead, the
space spanned by τ1,2, τ1,3, τ1,4 for the functions R1,2(τ1,2), R1,3(τ1,3),
R1,4(τ1,3) was searched completely, while adding the values of the
three remaining functions R2,3, R2,4, R3,4 for the resulting delay differences τi,j = τ1,i - τ1,j.
As expected, the algorithm could in general cope quite well
with the reverberation in the room [13]. However, with sound
sources being close to the wall or floor, the reflected signal is
nearly as strong as the direct one and arrives with only a short
delay, potentially leading to wrong results. To double-check
unreliable results, once the 3D location had been calculated,
also the resulting time delays for combinations of direct and
reflected signals arriving at the microphone pair were calculated based on the room size and assuming a damping factor
for reflections. These were indicated in the GCC-function display for verification (Figure 3). Many prominent peaks in the
GCC-function could be explained by correlations involving
reflected signals. When such calculated reflection delays did
not map to the cross-correlation peaks, it was an indication of
a wrong localization result. So far, this data has only been used
for visual verification of critical results. In principle, it could
be incorporated as additional parameter in the search for the
global maximum.
To finish this section, here is more data from a séance. Figure 2 might still have supported the assumption that the medium
somehow could unnoticeably get rid of his shackles and gag, stand
up, move outside the cabinet, and speak by himself, faking a different voice (and reattach the duct tape and gag before the end of
the séance). Figure 4 challenges this explanation: Here, four different voices (marked with names according to how they introduced themselves) are having a dialogue with each other, standing
at different positions, including one voice at about one meter in
height (which was known from previous séances, where it already
claimed to be a child).
54
Figure 3.
Reflected signals: Peaks in cross correlation function.
Figure 4.
Dialogue of four "spirit voices".
FORENSIC VOICE COMPARISON
Even though the "spirit voices" sound very different than the voice
of the medium, it is hard to argue about perceived similarities and
whether a voice could be imitated. But again, signal processing can
provide evidence beyond subjective judgements; it can even take
into account speech features which are hardly perceivable, as they
do not matter in human speech understanding.
The typical task in forensic voice comparison [14] is to analyze and compare speech samples in order to decide whether
a suspect is identical with the offender. The likelihood-ratio
framework [15] is a well-established practice, yielding objective results by incorporating voice samples of the relevant population for comparison. Unfortunately, this approach cannot be
directly applied to evaluate whether the "spirit voices" could
have been feigned by the medium. Here, it has to be taken into
account that somebody trying to fake a "spirit voice" will probably disguise his voice as much as possible, such as altering
the pitch and the rhythm of speech; F1-F2-formant trajectories,
which may provide interesting information about provenance,
gender and age [16], might be altered by appropriate training.
So, many features useful for speaker recognition will not be
adequate here.
On the other hand, while forensic applications often have to
rely on low-quality audio recordings, such as from telephone communication, the séance phenomena can be recorded with highquality. The higher formants, namely F3 and F4, become accessible for analysis, offering the chance to measure features that
IEEE A&E SYSTEMS MAGAZINE
FEBRUARY 2018
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