Time-domain modelling of peripheral auditory processing:
A modular architecture and a software platform*
Roy D. Patterson and Mike H. Allerhand
MRC Applied Psychology Unit, 15 Chaucer Road, Cambridge CB2 2EF, UK
Christian Giguere Laboratory of Experimental Audiology, University
Hospital Utrecht, 3508 GA Utrecht, The Netherlands
(Received December, 1994) (Revised 31 March 1995)
A software package with a modular architecture has been developed to
support perceptual modelling of the fine-grain spectro-temporal
information observed in the auditory nerve. The package contains both
functional and physiological modules to simulate auditory spectral
analysis, neural encoding and temporal integration, including new
forms of periodicity-sensitive temporal integration that generate
stabilized auditory images. Combinations of the modules enable the
user to approximate a wide variety of existing, time-domain, auditory
models. Sequences of auditory images can be replayed to produce
cartoons of auditory perceptions that illustrate the dynamic response
of the auditory system to everyday sounds.
PACS numbers: 43.64.Bt, 43.66.Ba, 43.71.An
Running head: Auditory Image Model Software
Several years ago, we developed a functional model of the cochlea to
simulate the phase-locked activity that complex sounds produce in the
auditory nerve. The purpose was to investigate the role of the
fine-grain timing information in auditory perception generally
(Patterson et al., 1992a; Patterson and Akeroyd, 1995), and in speech
perception in particular (Patterson, Holdsworth and Allerhand, 1992b).
The architecture of the resulting Auditory Image Model (AIM) is shown
in the left-hand column of Fig. 1. The responses of the three modules
to the vowel in 'hat' are shown in the three panels of Fig. 2.
Briefly, the spectral analysis stage converts the sound wave into the
model's representation of basilar membrane motion (BMM). For the vowel
in 'hat', each glottal cycle generates a version of the basic vowel
structure in the BMM (top panel). The neural encoding stage
stabilizes the BMM in level and sharpens features like vowel formants,
to produce a simulation of the neural activity pattern (NAP) produced
by the sound in the auditory nerve (middle panel). The temporal
integration stage stabilizes the repeating structure in the NAP and
produces a simulation of our perception of the vowel (bottom panel),
referred to as the auditory image. Sequences of simulated images can
be generated at regular intervals and replayed as an animated cartoon
to show the dynamic behaviour of the auditory images produced by
An earlier version of the AIM software was made available to
collaborators via the Internet. From there it spread to the speech and
music communities, indicating a more general interest in auditory
models than we had originally anticipated. This has prompted us to
prepare documentation and a formal release of the software (AIM R7).
A number of users wanted to compare the outputs from the functional
model, which is almost level independent, with those from
physiological models of the cochlea, which are fundamentally level
dependent. Others wanted to compare the auditory images produced by
strobed temporal integration with correlograms. As a result, we have
installed alternative modules for each of the three main stages as
shown in the right-hand column of Fig. 1. The alternative spectral
analysis module is a non-linear, transmission line filterbank based on
Giguere and Woodland (1994a). The neural encoding module is based on
the inner haircell model of Meddis (1988). The temporal integration
module generates correlograms like those of Slaney and Lyon (1990) or
Meddis and Hewitt (1991), using the algorithm proposed by Allerhand
and Patterson (1992). The responses of the three modules to the vowel
in 'hat' are shown in Fig. 3 for the case where the level of the vowel
is 60 dB SPL. The patterns are broadly similar to those of the
functional modules but the details differ, particularly at the output
of the third stage. The differences grow more pronounced when the
level of the vowel is reduced to 30 dB SPL or increased to 90 dB SPL.
Figures 2 and 3 together illustrate how the software can be used to
compare and contrast different auditory models. The new modules also
open the way to time-domain simulation of hearing impairment and
distortion products of cochlear origin.
Switches were installed to enable the user to shift from the
functional to the physiological version of AIM at the output of each
stage of the model. This architecture enables the system to implement
other popular auditory models such as the gammatone- filterbank,
Meddis-haircell, correlogram models proposed by Assmann and
Summerfield (1990), Meddis and Hewitt (1991), and Brown and Cooke
(1994). The remainder of this letter describes the integrated software
package with emphasis on the functional and physiological routes, and
on practical aspects of obtaining the software package.*
I. THE AUDITORY IMAGE MODEL
A. The spectral analysis stage
Spectral analysis is performed by a bank of auditory filters which
converts a digitized wave into an array of filtered waves like those
shown in the top panels of Figs 2 and 3. The set of waves is AIM's
representation of basilar membrane motion. The software distributes
the filters linearly along a frequency scale measured in Equivalent
Rectangular Bandwidths (ERB's). The ERB scale was proposed by Glasberg
and Moore (1990) based on physiological research summarized in
Greenwood (1990) and psychoacoustic research summarized in Patterson
and Moore (1986). The constants of the ERB function can also be set to
produce a reasonable approximation to the Bark scale. Options enable
the user to specify the number of channels in the filterbank and the
minimum and maximum filter center frequencies.
AIM provides both a functional auditory filter and a physiological
auditory filter for generating the BMM: the former is a linear,
gammatone filter (Patterson et al., 1992a); the latter is a
non-linear, transmission-line filter (Giguere and Woodland, 1994a).
The impulse response of the gammatone filter provides an excellent fit
to the impulse response of primary auditory neurons in cats, and its
amplitude characteristic is very similar to that of the 'roex' filter
commonly used to represent the human auditory filter. The motivation
for the gammatone filterbank and the available implementations are
summarized in Patterson (1994a). The input wave is passed through an
optional middle-ear filter adapted from Lutman and Martin (1979).
In the physiological version, a 'wave digital filter' is used to
implement the classical, one-dimensional, transmission-line
approximation to cochlear hydrodynamics. A feedback circuit
representing the fast motile response of the outer haircells generates
level- dependent basilar membrane motion (Giguere and Woodland,
1994a). The filterbank generates combination tones of the type
f1-n(f2-f1) which propagate to the appropriate channel, and it has the
potential to generate cochlear echoes. Options enable the user to
customize the transmission line filter by specifying the feedback gain
and saturation level of the outer haircell circuit. The middle ear
filter forms an integral part of the simulation in this case.
Together, it and the transmission line filterbank provide a
bi-directional model of auditory spectral analysis.
The upper panels of Figs 2 and 3 show the responses of the two
filterbanks to the vowel in 'hat'. They have 75 channels covering the
frequency range 100 to 6000 Hz (3.3 to 30.6 ERB's). In the
high-frequency channels, the filters are broad and the glottal pulses
generate impulse responses which decay relatively quickly. In the
low-frequency channels, the filters are narrow and so they resolve
individual continuous harmonics. The rightward skew in the
low-frequency channels is the 'phase lag,' or 'propagation delay,' of
the cochlea, which arises because the narrower low-frequency filters
respond more slowly to input. The transmission line filterbank shows
more ringing in the valleys than the gammatone filterbank because of
its dynamic signal compression; as amplitude decreases the damping of
the basilar membrane is reduced to increase sensitivity and frequency
B. The neural encoding stage
The second stage of AIM simulates the mechanical/neural transduction
process performed by the inner haircells. It converts the BMM into a
neural activity pattern (NAP), which is AIM's representation of the
afferent activity in the auditory nerve. Two alternative simulations
are provided for generating the NAP: a bank of two-dimensional
adaptive- thresholding units (Holdsworth and Patterson, 1993), or a
bank of inner haircell simulators (Meddis, 1988).
The adaptive thresholding mechanism is a functional representation of
neural encoding. It begins by rectifying and compressing the BMM; then
it applies adaptation in time and suppression across frequency. The
adaptation and suppression are coupled and they jointly sharpen
features like vowel formants in the compressed BMM representation.
Briefly, an adaptive threshold value is maintained for each channel
and updated at the sampling rate. The new value is the largest of a)
the previous value reduced by a fast-acting temporal decay factor, b)
the previous value reduced by a longer-term temporal decay factor, c)
the adapted level in the channel immediately above, reduced by a
frequency spread factor, or d) the adapted level in the channel
immediately below, reduced by the same frequency spread factor. The
mechanism produces output whenever the input exceeds the adaptive
threshold, and the output level is the difference between the input
and the adaptive threshold. The parameters that control the spread of
activity in time and frequency are options in AIM.
The Meddis (1988) module simulates the operation of an individual
inner haircell; specifically, it simulates the flow of
neurotransmitter across three reservoirs that are postulated to exist
in and around the haircell. The module reproduces important properties
of single afferent fibres such as two-component time adaptation and
phase-locking. The transmitter flow equations are solved using the
wave-digital-filter algorithm described in Giguere and Woodland
(1994a). There is one haircell simulator for each channel of the
filterbank. Options allow the user to shift the entire rate-intensity
function to a higher or lower level, and to specify the type of fibre
(medium or high spontaneous-rate).
The middle panels in Figures 2 and 3 show the NAPs obtained with
adaptive thresholding and the Meddis module in response to BMMs from
the gammatone and transmission line filterbanks of Figs 1 and 2,
respectively. The phase lag of the BMM is preserved in the NAP. The
positive half-cycles of the BMM waves have been sharpened in time, an
effect which is more obvious in the adaptive thresholding NAP.
Sharpening is also evident in the frequency dimension of the adaptive
thresholding NAP. The individual 'haircells' are not coupled across
channels in the Meddis module, and thus there is no frequency
sharpening in this case. The physiological NAP reveals that the
activity between glottal pulses in the high-frequency channels is due
to the strong sixth harmonic in the first formant of the vowel.
C. The temporal integration stage
Periodic sounds give rise to static, rather than oscillating,
perceptions indicating that temporal integration is applied to the NAP
in the production of our initial perception of a sound -- our auditory
image. Traditionally, auditory temporal integration is represented by
a simple leaky integration process and AIM provides a bank of lowpass
filters to enable the user to generate auditory spectra (Patterson,
1994a) and auditory spectrograms (Patterson et al., 1992b). However,
the leaky integrator removes the phase-locked fine structure observed
in the NAP, and this conflicts with perceptual data indicating that
the fine structure plays an important role in determining sound
quality and source identification (Patterson, 1994b; Patterson and
Akeroyd, 1995). As a result, AIM includes two modules which preserve
much of the time-interval information in the NAP during temporal
integration, and which produce a better representation of our auditory
images. In the functional version of AIM, this is accomplished with
strobed temporal integration (Patterson et al., 1992a,b); in the
physiological version, it is accomplished with a bank of
autocorrelators (Slaney and Lyon, 1990; Meddis and Hewitt, 1991).
In the case of strobed temporal integration (STI), a bank of delay
lines is used to form a buffer store for the NAP, one delay line per
channel, and as the NAP proceeds along the buffer it decays linearly
with time, at about 2.5 %/ms. Each channel of the buffer is assigned a
strobe unit which monitors activity in that channel looking for local
maxima in the stream of NAP pulses. When one is found, the unit
initiates temporal integration in that channel; that is, it transfers
a copy of the NAP at that instant to the corresponding channel of an
image buffer and adds it point-for-point with whatever is already
there. The local maximum itself is mapped to the 0-ms point in the
image buffer. The multi-channel version of this STI process produces
AIM's representation of our auditory image of a sound. Periodic and
quasi-periodic sounds cause regular strobing which leads to simulated
auditory images that are static, or nearly static, and which have the
same temporal resolution as the NAP. Dynamic sounds are represented
as a sequence of auditory image frames. If the rate of change in a
sound is not too rapid, as is diphthongs, features are seen to move
smoothly as the sound proceeds, much as characters move smoothly in
An alternative form of temporal integration is provided by the
correlogram (Slaney and Lyon, 1990; Meddis and Hewitt, 1991). It
extracts periodicity information and preserves intra-period fine
structure by autocorrelating each channel of the NAP. The correlogram
is the multi-channel version of this process. It was originally
introduced as a model of pitch perception (Licklider, 1951) with a
neural wiring diagram to illustrate that it was physiologically
plausible. To date, however, there is no physiological evidence for
autocorrelation in the auditory system, and the installation of the
module in the physiological route was a matter of convenience. The
current implementation is a recursive, or running, autocorrelation. A
functionally equivalent FFT-based method is also provided (Allerhand
and Patterson, 1992). A comparison of the correlogram in the bottom
panel of Fig. 3 with the auditory image in the bottom panel of Fig. 2
shows that the vowel structure is more symmetric in the correlogram
and there are larger level contrasts in the correlogram. It is not
yet known whether one of the representations is more realistic or more
useful. The present purpose is to note that the software package can
be used to compare auditory representations in a way not previously
II. THE SOFTWARE/HARDWARE PLATFORM
i. The software package: The code is distributed as a compressed
archive (in unix tar format), and can be obtained via ftp from the
address: ftp.mrc-apu.cam.ac.uk (Name=anonymous; Password=<your email
address>). All the software is contained in a single archive:
pub/aim/aim.tar.Z. The associated text file pub/aim/ReadMe contains
instructions for installing and compiling the software. The AIM
package consists of a makefile and several sub-directories. Five of
these (filter, glib, model, stitch and wdf) contain the C code for
AIM. An aim/tools directory contains C code for ancillary software
tools. These software tools are provided for pre/post-processing of
model input/output. A variety of functions are offered, including:
stimulus generation, signal processing, and data manipulation. An
aim/man directory contains on-line manual pages describing AIM and the
software tools. An aim/scripts directory contains demonstration
scripts for a guided tour through the model. Sounds used to test and
demonstrate the model are provided in the aim/waves directory. These
sounds were sampled at 20 kHz, and each sample is a 2-byte number in
little-endian byte order; a tool is provided to swap byte order when
ii. System requirements: The software is written in C. The code
generated by the native C compilers included with Ultrix (version 4.3a
and above) and SunOS (version 4.1.3 and above) has been extensively
tested. The code from the GNU C compiler (version 2.5.7 and above) is
also reliable. The total disc usage of the AIM source code is about
700 kbytes. The package also includes 500 kbytes of sources for
ancillary software tools, and 200 kbytes of documentation. The
executable programs occupy about 1000 kbytes, and executable programs
for ancillary tools occupy 7000 kbytes. About 800 Kbytes of temporary
space are required for object files during compilation. The graphical
interface uses X11 (R4 and above) with either the OpenWindows or Motif
user interface. The programs can be compiled using the base Xlib
library (libX11.a), and will run on both 1- bit (mono) and multi-plane
(colour or greyscale) displays.
iii. Compilation and operation: The makefile includes targets to
compile the source code for AIM and the associated tools on a range of
machines (DEC, SUN, SGI, HP); the targets differ only in the pathnames
for the local X11 base library (libX11.a) and header files (X11/X.h
and X11/Xlib.h). AIM can be compiled without the display code if the
graphics interface is not required or if X11 is not available (make
noplot). The executable for AIM is called gen. Compilation also
generates symbolic links to gen, such as genbmm, gennap and gensai,
which are used to select the desired output (BMM, NAP or SAI). The
links and the executables for the aim/tools are installed in the
aim/bin directory after compilation. Options are specified as:
name=value on the command line; unspecified options are assigned
default values. The model output takes the form of binary data routed
by default to the model's graphical displays. Output can also be
routed to plotting hardware, or other post- processing software.
III. APPLICATIONS AND SUMMARY
In hearing research, the functional version of AIM has been used to
model phase perception (Patterson, 1987a), octave perception
(Patterson et al., 1993), and timbre perception (Patterson, 1994b).
The physiological version has been used to simulate cochlear hearing
loss (Giguere, Woodland, and Robinson, 1993; Giguere and Woodland,
1994b), and combination tones of cochlear origin (Giguere, Kunov, and
Smoorenburg, 1995). In speech research, the functional version has
been used to explain syllabic stress (Allerhand et al., 1992), and
both versions have been used as preprocessors for speech recognition
systems (e.g. Patterson, Anderson, and Allerhand, 1994; Giguere et
al., 1993). In summary, the AIM software package provides a modular
architecture for time- domain computational studies of peripheral
* Instructions for acquiring the software package electronically are
presented in Section II. This document refers to AIM R7 which is the
first official release.
The gammatone filterbank, adaptive thresholding, and much of the
software platform were written by John Holdsworth; the options handler
is by Paul Manson, and the revised STI module by Jay Datta. Michael
Akeroyd extended the postscript facilities and developed the xreview
routine for auditory image cartoons. The software development was
supported by grants from DRA Farnborough (U.K.), Esprit BR 3207 (EEC),
and the Hearing Research Trust (U.K.). We thank Malcolm Slaney and
Michael Akeroyd for helpful comments on an earlier version of the
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(1992). "Assessing syllable strength via an auditory model," Proc.
Inst. Acoust. 14, 297-304.
Assmann, P.F., and Summerfield, Q. (1990). "Modelling the perception
of concurrent vowels: Vowels with different fundamental frequencies,"
J. Acoust. Soc. Am., 88, 680- 697.
Brown, G.J., and Cooke, M. (1994) "Computational auditory scene
analysis," Computer Speech and Language 8, 297-336.
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Figure 1. The three-stage structure of the AIM software package.
Left-hand column: functional route, right-hand column: physiological
route. For each module, the figure shows the function (bold type), the
implementation (in the rectangle), and the simulation it produces
Figure 2. Responses of the model to the vowel in 'hat' processed
through the functional route: (top) basilar membrane motion, (middle)
neural activity pattern, and (bottom) auditory image.
Figure 3. Responses of the model to the vowel in 'hat' processed
through the physiological route: (top) basilar membrane motion,
(middle) neural activity pattern, and (bottom) autocorrelogram image.