# Available processors¶

This section presents a detailed description of all processors that are currently supported by the Auditory front-end framework. Each processor can be controlled by a set of parameters, which will be explained and all default settings will be listed. Finally, a demonstration will be given, showing the functionality of each processor. The corresponding Matlab files are contained in the Auditory front-end folder /test and can be used to reproduce the individual plots. A full list of available processors can be displayed by using the command requestList. An overview of the commands for instantiating processors is given in Computation of an auditory representation.

## Pre-processing (preProc.m)¶

Prior to computing any of the supported auditory representations, the input signal stored in the data object can be pre-processed with one of the following elements:

1. DC bias removal
2. Pre-emphasis
3. RMS normalisation using an automatic gain control
4. Level scaling to a pre-defined SPL reference
5. Middle ear filtering

The order of processing is fixed. However, individual stages can be activated or deactivated, depending on the requirement of the user. The output is a time domain signal representation that is used as input to the next processors. Moreover, a list of adjustable parameters is listed in Table 4.

Table 4 List of parameters related to the auditory representation ’time’.
Parameter Default Description
pp_bRemoveDC false Activate DC removal filter
pp_cutoffHzDC 20 Cut-off frequency in Hz of the high-pass filter
pp_bPreEmphasis false Activate pre-emphasis filter
pp_coefPreEmphasis 0.97 Coefficient of first-order high-pass filter
pp_bNormalizeRMS false Activate RMS normalisation
pp_intTimeSecRMS 2 Time constant in s used for RMS estimation
pp_bBinauralRMS true Link RMS normalisation across both ear signals
pp_bLevelScaling false Apply level scaling to the given reference
pp_refSPLdB 100 Reference dB SPL to correspond to the input RMS
pp_bMiddleEarFiltering false Apply middle ear filtering
pp_middleEarModel 'jepsen' Middle ear filter model

The influence of each individual pre-processing stage except for the level scaling is illustrated in Fig. 7, which can be reproduced by running the script DEMO_PreProcessing.m. Panel 1 shows the left and the right ears signals of two sentences at two different levels. The ear signals are then mixed with a sinusoid at 0.5 Hz to simulate an interfering humming noise. This humming can be effectively removed by the DC removal filter, as shown in panel 3. Panel 4 shows the influence of the pre-emphasis stage. The AGC can be used to equalise the long-term RMS level difference between the two sentences. However, if the level difference between both ear signals should be preserved, it is important to synchronise the AGC across both channels, as illustrated in panel 5 and 6. Panel 7 shows the influence of the level scaling when using a reference value of 100 dB SPL. Panel 8 shows the signals after middle ear filtering, as the stapes motion velocity. Each individual pre-processing stage is described in the following subsections.

### DC removal filter¶

To remove low-frequency humming, a DC removal filter can be activated by using the flag pp_bRemoveDC = true. The DC removal filter is based on a fourth-order IIR Butterworth filter with a cut-off frequency of 20 Hz, as specified by the parameter pp_cutoffHzDC = 20.

### Pre-emphasis¶

A common pre-processing stage in the context of ASR includes a signal whitening. The goal of this pre-processing stage is to roughly compensate for the decreased energy at higher frequencies (e.g. due to lip radiation). Therefore, a first-order FIR high-pass filter is employed, where the filter coefficient pp_coefPreEmphasis determines the amount of pre-emphasis and is typically selected from the range between 0.9 and 1. Here, we set the coefficient to pp_coefPreEmphasis = 0.97 by default according to [Young2006]. This pre-emphasis filter can be activated by setting the flag pp_bPreEmphasis = true.

### RMS normalisation¶

A signal level normalisation stage is available which can be used to equalise long-term level differences (e.g. when recording two speakers at two different distances). For some applications, such as ASR and speaker identification systems, it can be advantageous to maintain a constant signal power, such that the features extracted by subsequent processors are invariant to the overall signal level. To achieve this, the input signal is normalised by its RMS value that has been estimated by a first-order low-pass filter with a time constant of pp_intTimeSecRMS = 2. Such a normalisation stage has also been suggested in the context of AMS feature extraction [Tchorz2003], which are described in Amplitude modulation spectrogram (modulationProc.m). The choice of the time constant is a balance between maintaining the level fluctuations across individual words and allowing the normalisation stage to follow sudden level changes.

The normalisation can be either applied independently for the left and the right ear signal by setting the parameter pp_bBinauralRMS = false, or the processing can be linked across ear signals by setting pp_bBinauralRMS = true. When being used in the binaural mode, the larger RMS value of both ear signals is used for normalisation, which will preserve the binaural cues (e.g. ITD and ILD) that are encoded in the signal. The RMS normalisation can be activated by the parameter pp_bNormalizeRMS = true.

### Level reference and scaling¶

This stage is designed to implement the effect of calibration, in which the amplitude of the incoming digital signal is matched to sound pressure in the physical domain. This operation is necessary when any of the Auditory front-end models requires the input to be represented in physical units (such as pascals, see the middle ear filtering stage below). Within the current Auditory front-end framework, the DRNL filter bank model requires this signal representation (see Dual-resonance non-linear filter bank (drnlProc.m)). The request for this is given by setting pp_bApplyLevelScaling = true, with a reference value pp_refSPLdB in dB SPL which should correspond to the input RMS of 1. Then the input signal is scaled accordingly, if it had been calibrated to a different reference. The default value of pp_refSPLdB is 100, which corresponds to the convention used in the work of [Jepsen2008]. The implementation is adopted from the Auditory Modeling Toolbox [Soendergaard2013].

### Middle ear filtering¶

This stage corresponds to the operation of the middle ear where the vibration from the eardrum is transformed into the stapes motion. The filter model is based on the findings from the measurement of human stapes displacement by [Godde1994]. Its implementation is adopted from the Auditory Modeling Toolbox [Soendergaard2013], which derives the stapes velocity as the output [Lopez-Poveda2001], [Jepsen2008]. The input is assumed to be the eardrum pressure represented in pascals which in turn assumes prior calibration. This input-output representation in physical units is required particularly when the DRNL filter bank model is used for the BM operation, because of its level-dependent nonlinearity, designed based on that representation (see Dual-resonance non-linear filter bank (drnlProc.m)). When including the middle-ear filtering in combination with the linear gammatone filter, only the simple band-pass characteristic of this model is needed without the need for input calibration or consideration of the input/output units. The middle ear filtering can be applied by setting pp_bMiddleEarFiltering = true. The filter data from [Lopez-Poveda2001] or from [Jepsen2008] can be used for the processing, by specifying the model pp_middleEarModel = 'lopezpoveda' or pp_middleEarModel = 'jepsen' respectively.

## Auditory filter bank¶

One central processing element of the Auditory front-end is the separation of incoming acoustic signals into different spectral bands, as it happens in the human inner ear. In psychoacoustic modelling, two different approaches have been followed over the years. One is the simulation of this stage by a linear filter bank composed of gammatone filters. This linear gammatone filter bank can be considered a standard element for auditory models and has therefore been included in the framework. A computationally more challenging, but at the same time physiologically more plausible simulation of this process can be realised by a nonlinear BM model, and we have implemented the DRNL model, as developed by [Meddis2001]. The filter bank representation is requested by using the name tag 'filterbank'. The filter bank type can be controlled by the parameter fb_type. To select a gammatone filter bank, fb_type should be set to ’gammatone’ (which is the default), whereas the DRNL filter bank is used when setting fb_type = 'drnl'. Some of the parameters are common to the two filter bank, while some are specific, in which case their value is disregarded if the other type of filter bank was requested. Table 5 summarises all parameters corresponding to the 'filterbank' request. Parameters specific to a filter bank type are separated by a horizontal line. The two filter bank implementations are described in detail in the following two subsections, along with their corresponding parameters.

Table 5 List of parameters related to the auditory representation 'filterbank'
Parameter Default Description
fb_type 'gammatone' Filter bank type, 'gammatone' or 'drnl'
fb_lowFreqHz 80 Lowest characteristic frequency in Hz
fb_highFreqHz 8000 Highest characteristic frequency in Hz
fb_nERBs 1 Distance between adjacent filters in ERB
fb_nChannels [] Number of frequency channels
fb_cfHz [] Vector of characteristic frequencies in Hz
fb_nGamma 4 Filter order, 'gammatone'-only
fb_bwERBs 1.01859 Filter bandwidth in ERB, 'gammatone'-only
fb_lowFreqHz 80 Lowest characteristic frequency in Hz, 'gammatone'-only
fb_mocIpsi 1

Ipsilateral MOC factor (0 to 1). Given as a scalar

(across all

frequency channels) or a vector (individual per frequency

channel), 'drnl'-only

fb_mocContra 1

Contralateral MOC factor (0 to 1). Same format as

'fb_mocIpsi', 'drnl'-only

fb_model 'CASP' DRNL model (reserved for future extension), 'drnl'-only

### Gammatone (gammatoneProc.m)¶

The time domain signal can be processed by a bank of gammatone filters that simulates the frequency selective properties of the human BM. The corresponding Matlab function is adopted from the Auditory Modeling Toolbox [Soendergaard2013]. The gammatone filters cover a frequency range between fb_lowFreqHz and fb_highFreqHz and are linearly spaced on the ERB scale [Glasberg1990]. In addition, the distance between adjacent filter centre frequencies on the ERB scale can be specified by fb_nERBs, which effectively controls the frequency resolution of the gammatone filter bank. There are three different ways to control the centre frequencies of the individual gammatone filters:

1. Define a vector with centre frequencies, e.g. fb_cfHz = [100 200 500 ...]. In this case, the parameters fb_lowFreqHz, fb_highFreqHz, fb_nERBs and fb_nChannels are ignored.
2. Specify fb_lowFreqHz, fb_highFreqHz and fb_nChannels. The requested number of filters fb_nChannels will be spaced between fb_lowFreqHz and fb_highFreqHz. The centre frequencies of the first and the last filter will match with fb_lowFreqHz and fb_highFreqHz, respectively. To accommodate an arbitrary number of filters, the spacing between adjacent filters fb_nERBs will be automatically adjusted. Note that this changes the overlap between neighbouring filters.
3. It is also possible to specify fb_lowFreqHz, fb_highFreqHz and fb_nERBs. Starting at fb_lowFreqHz, the centre frequencies will be spaced at a distance of fb_nERBs on the ERB scale until the specified frequency range is covered. The centre frequency of the last filter will not necessarily match with fb_highFreqHz.

The filter order, which determines the slope of the filter skirts, is set to fb_nGamma = 4 by default. The bandwidths of the gammatone filters depend on the filter order and the centre frequency, and the default scaling factor for a forth-order filter is approximately fb_bwERBs = 1.01859. When adjusting the parameter fb_bwERBs, it should be noted that the resulting filter shape will deviate from the original gammatone filter as measured by [Glasberg1990]. For instance, increasing fb_bwERBs leads to a broader filter shape. A full list of parameters is shown in Table 5.

The gammatone filter bank is illustrated in Fig. 8, which has been produced by the script DEMO_Gammatone.m. The speech signal shown in the left panel is passed through a bank of 16 gammatone filters spaced between 80 Hz and 8000 Hz. The output of each individual filter is shown in the right panel.

### Dual-resonance non-linear filter bank (drnlProc.m)¶

The DRNL filter bank models the nonlinear operation of the cochlear, in addition to the frequency selective feature of the BM. The DRNL processor was motivated by attempts to better represent the nonlinear operation of the BM in the modelling, and allows for testing the performance of peripheral models with the BM nonlinearity and MOC feedback in comparison to that with the conventional linear BM model. All the internal representations that depend on the BM output can be extracted using the DRNL processor in the dependency chain in place of the gammatone filter bank. This can reveal the implication of the BM nonlinearity and MOC feedback for activities such as speech perception in noise (see [Brown2010] for example) or source localisation. It is expected that the use of a nonlinear model, together with the adaptation loops (see Adaptation (adaptationProc.m)), will reduce the influence of overall level on the internal representations and extracted features. In this sense, the use of the DRNL model is a physiologically motivated alternative for a linear BM model where the influence of level is typically removed by the use of a level normalisation stage (see AGC in Pre-processing (preProc.m) for example). The structure of DRNL filter bank is based on the work of [Meddis2001]. The frequencies corresponding to the places along the BM, over which the responses are to be derived and observed, are specified as a list of characteristic frequencies fb_cfHz. For each characteristic frequency channel, the time domain input signal is passed through linear and nonlinear paths, as seen in Fig. 9. Currently the implementation follows the model defined as CASP by [Jepsen2008], in terms of the detailed structure and operation, which is specified by the default argument 'CASP' for fb_model.

In the CASP model, the linear path consists of a gain stage, two cascaded gammatone filters, and four cascaded low-pass filters; the nonlinear path consists of a gain (attenuation) stage, two cascaded gammatone filters, a ’broken stick’ nonlinearity stage, two more cascaded gammatone filters, and a low-pass filter. The outputs at the two paths are then summed as the BM output motion. These sub-modules and their individual parameters (e.g., gammatone filter centre frequencies) are specific to the model and hidden to the users. Details regarding the original idea behind the parameter derivation can be found in [Lopez-Poveda2001], which the CASP model slightly modified to provide a better fit of the output to physiological findings from human cochlear research works.

The MOC feedback is implemented in an open-loop structure within the DRNL filter bank model as the gain factor to be applied to the nonlinear path. This approach is used by [Ferry2007], where the attenuation caused by MOC the feedback at each of the filter bank channels is controlled externally by the user. Two additional input arguments are introduced for this feature: fb_mocIpsi and fb_mocContra. These represent the amount of reflexive feedback through the ipsilateral and contralateral paths, in the form of a factor from 0 to 1 that the nonlinear path input signal is multiplied by in conjunction. Conceptually, fb_mocIpsi = 1 and fb_mocContra = 1 would mean that no attenuation is applied to the nonlinear path input, and fb_mocIpsi = 0 and fb_mocContra = 0 would mean that the nonlinear path is totally eliminated. Table 5 summarises the parameters for DRNL the processor that can be controlled by the user. Note that fb_cfHz corresponds to the characteristic frequencies and not the centre frequencies as used in the gammatone filter bank, although they can have the same values for comparison. Otherwise, the characteristic frequencies can be generated in the same way as the centre frequencies for the gammatone filter bank.

Fig. 10 shows the BM stage output at 1 kHz characteristic frequency using the DRNL processor (on the right hand side), compared to that using the gammatone filter bank (left hand side), based on the right ear input signal shown in panel 1 of Fig. 7 (speech excerpt repeated twice with a level difference). The plots can be generated by running the script DEMO_DRNL.m. It should be noted that the CASP model of DRNL filter bank expects the input signal to be transformed to the middle ear stapes velocity before processing. Therefore, for direct comparison of the outputs in this example, the same pre-processing was applied for the gammatone filter bank (stapes velocity was used as the input, through the level scaling and middle ear filtering). It is seen that the level difference between the initial speech component and its repetition is reduced with the nonlinearity incorporated, compared to the gammatone filter bank output, which shows the compressive nature of the nonlinear model responding to input level changes as described earlier.

## Inner hair-cell (ihcProc.m)¶

The IHC functionality is simulated by extracting the envelope of the output of individual gammatone filters. The corresponding IHC function is adopted from the Auditory Modeling Toolbox [Soendergaard2013]. Typically, the envelope is extracted by combining half-wave rectification and low-pass filtering. The low-pass filter is motivated by the loss of phase-locking in the auditory nerve at higher frequencies [Bernstein1996], [Bernstein1999]. Depending on the cut-off frequency of the IHC models, it is possible to control the amount of fine-structure information that is present in higher frequency channels. The cut-off frequency and the order of the corresponding low-pass filter vary across methods and a complete overview of supported IHC models is given in Table 6. A particular model can be selected by using the parameter ihc_method.

Table 6 List of supported IHC models
ihc_method Description
'hilbert' Hilbert transform
'halfwave' Half-wave rectification
'fullwave' Full-wave rectification
'square' Squared
'dau' Half-wave rectification and low-pass filtering at 1000 Hz [Dau1996]
'joergensen' Hilbert transform and low-pass filtering at 150 Hz [Joergensen2011]
'breebart' Half-wave rectification and low-pass filtering at 770 Hz [Breebart2001]
'bernstein' Half-wave rectification, compression and low-pass filtering at 425 Hz [Bernstein1999]

The effect of the IHC processor is demonstrated in Fig. 11, where the output of the gammatone filter bank is compared with the output of an IHC model by running the script DEMO_IHC.m. Whereas individual peaks are resolved in the lowest channel of the IHC output, only the envelope is retained at higher frequencies.

## Adaptation (adaptationProc.m)¶

This processor corresponds to the adaptive response of the auditory nerve fibers, in which abrupt changes in the input result in emphasised overshoots followed by gradual decay to compressed steady-state level [Smith1977], [Smith1983]. The function is adopted from the Auditory Modeling Toolbox [Soendergaard2013]. The adaptation stage is modelled as a chain of five feedback loops in series. Each of the loops consists of a low-pass filter with its own time constant, and a division operator [Pueschel1988], [Dau1996], [Dau1997a]. At each stage, the input is divided by its low-pass filtered version. The time constant affects the charging / releasing state of the filter output at a given moment, and thus affects the amount of attenuation caused by the division. This implementation realises the characteristics of the process that input variations which are rapid compared to the time constants are linearly transformed, whereas stationary input signals go through logarithmic compression.

Table 7 List of parameters related to 'adaptation'.
Parameter Default Description
adpt_lim 10 Overshoot limiting ratio
adpt_mindB 0

Lowest audible threshold of the signal

in dB SPL

adpt_tau [0.005 0.050 0.129 0.253 0.500] Time constants of feedback loops
adpt_model ''(empty)

Implementation model 'adt_dau',

'adt_puschel', or 'adt_breebart'

can be used instead of the above three

parameters (See Table 8)

The adaptation processor uses three parameters to generate the output from the IHC representation: adpt_lim determines the maximum ratio of the onset response amplitude against the steady-state response, which sets a limit to the overshoot caused by the loops. adpt_mindB sets the lowest audible threshold of the input signal. adpt_tau are the time constants of the loops. Though the default model uses five loops and thus five time constants, variable number of elements of adpt_tau is supported which can vary the number of loops. Some specific sets of these parameters, as used in related studies, are also supported optionally with the adpt_model parameter. This can be given instead of the other three parameters, which will set them as used by the respective researchers. Table 7 lists the parameters and their default values, and Table 8 lists the supported models. The output signal is expressed in MU which deviates the input-output relation from a perfect logarithmic transform, such that the input level increment at low level range results in a smaller output level increment than the input increment at higher level range. This corresponds to a smaller just-noticeable level change at high levels than at low levels [Dau1996], [Jepsen2008], with the use of DRNL model for the BM stage, introduces an additional squaring expansion process between the IHC output and the adaptation stage, which transforms the input that comes through the DRNL-IHC processors into an intensity-like representation to be compatible with the adaptation implementation originally designed based on the use of gammatone filter bank. The adaptation processor recognises whether DRNL or gammatone processor is used in the chain and adjusts the input signal accordingly.

Table 8 List of supported models related to 'adaptation'.
adpt_model Description
'adt_dau'

Choose the parameters as in the models of [Dau1996], [Dau1997a].

This consists of 5 adaptation loops with an overshoot limit of 10 and

a minimum level of 0 dB. This is a correction in regard to the model

described in [Dau1996], which did not use overshoot limiting. The

adaptation loops have an exponentially spaced time constants

adpt_tau=[0.005 0.050 0.129 0.253 0.500]

'adt_puschel'

Choose the parameters as in the original model [Pueschel1988].

This consists of 5 adaptation loops without overshoot limiting

(adpt_lim=0). The adaptation loops have a linearly spaced time

constants adpt_tau=[0.0050 0.1288 0.2525 0.3762 0.5000].

'adt_breebaart' As 'adt_puschel', but with overshoot limiting

The effect of the adaptation processor - the exaggeration of rapid variations - is demonstrated in Fig. 12, where the output of the IHC model from the same input as used in the example of Inner hair-cell (ihcProc.m) (the right panel of Fig. 11) is compared to the adaptation output by running the script DEMO_Adaptation.m.

## Auto-correlation (autocorrelationProc.m)¶

Auto-correlation is an important computational concept that has been extensively studied in the context of predicting human pitch perception [Licklider1951], [Meddis1991]. To measure the amount of periodicity that is present in individual frequency channels, the ACF is computed in the FFT domain for short time frames based on the IHC representation. The unbiased ACF scaling is used to account for the fact that fewer terms contribute to the ACF at longer time lags. The resulting ACF is normalised by the ACF at lag zero to ensure values between minus one and one. The window size ac_wSizeSec determines how well low-frequency pitch signals can be reliably estimated and common choices are within the range of 10 milliseconds – 30 milliseconds.

For the purpose of pitch estimation, it has been suggested to modify the signal prior to correlation analysis in order to reduce the influence of the formant structure on the resulting ACF [Rabiner1977]. This pre-processing can be activated by the flag ac_bCenterClip and the following nonlinear operations can be selected for ac_ccMethod: centre clip and compress ’clc’, centre clip ’cc’, and combined centre and peak clip ’sgn’. The percentage of centre clipping is controlled by the flag ac_ccAlpha, which sets the clipping level to a fixed percentage of the frame-based maximum signal level.

A generalised ACF has been suggested by [Tolonen2000], where the exponent ac\_K can be used to control the amount of compression that is applied to the ACF. The conventional ACF function is computed using a value of ac\_K=2, whereas the function is compressed when a smaller value than 2 is used. The choice of this parameter is a trade-off between sharpening the peaks in the resulting ACF function and amplifying the noise floor. A value of ac\_K = 2/3 has been suggested as a good compromise [Tolonen2000]. A list of all ACF-related parameters is given in Table 9. Note that these parameters will influence the pitch processor, which is described in Pitch (pitchProc.m).

Table 9 List of parameters related to the auditory representation 'autocorrelation'.
Parameter Default Description
ac_wname 'hann' Window type
ac_wSizeSec 0.02 Window duration in s
ac_hSizeSec 0.01 Window step size in s
ac_bCenterClip false Activate centre clipping
ac_clipMethod 'clp' Centre clipping method 'clc', 'clp', or 'sgn'
ac_clipAlpha 0.6 Centre clipping threshold within [0,1]
ac_K 2 Exponent in ACF

A demonstration of the ACF processor is shown in Fig. 13, which has been produced by the scrip DEMO_ACF.m. It shows the IHC output in response to a 20 ms speech signal for 16 frequency channels (left panel). The corresponding ACF is presented in the upper right panel, whereas the SACF is shown in the bottom right panel. Prominent peaks in the SACF indicate lag periods which correspond to integer multiples of the fundamental frequency of the analysed speech signal. This relationship is exploited by the pitch processor, which is described in Pitch (pitchProc.m).

## Rate-map (ratemapProc.m)¶

The rate-map represents a map of auditory nerve firing rates [Brown1994] and is frequently employed as a spectral feature in CASA systems [Wang2006], ASR  [Cooke2001] and speaker identification systems [May2012]. The rate-map is computed for individual frequency channels by smoothing the IHC signal representation with a leaky integrator that has a time constant of typically rm\_decaySec=8 ms. Then, the smoothed IHC signal is averaged across all samples within a time frame and thus the rate-map can be interpreted as an auditory spectrogram. Depending on whether the rate-map scaling rm_scaling has been set to ’magnitude’ or ’power’, either the magnitude or the squared samples are averaged within each time frame. The temporal resolution can be adjusted by the window size rm_wSizeSec and the step size rm_hSizeSec. Moreover, it is possible to control the shape of the window function rm_wname, which is used to weight the individual samples within a frame prior to averaging. The default rate-map parameters are listed in Table 10.

Table 10 List of parameters related to 'ratemap'.
Parameter Default Description
'rm_wname' 'hann' Window type
'rm_wSizeSec' 0.02 Window duration in s
'rm_hSizeSec' 0.01 Window step size in s
'rm_scaling' 'power' Rate-map scaling ('magnitude' or 'power')
'rm_decaySec' 0.008 Leaky integrator time constant in s

The rate-map is demonstrated by the script DEMO_Ratemap and the corresponding plots are presented in Fig. 14. The IHC representation of a speech signal is shown in the left panel, using a bank of 64 gammatone filters spaced between 80 and 8000 Hz. The corresponding rate-map representation scaled in dB is presented in the right panel.

## Spectral features (spectralFeaturesProc.m)¶

In order to characterise the spectral content of the ear signals, a set of spectral features is available that can serve as a physical correlate to perceptual attributes, such as timbre and coloration [Peeters2011]. All spectral features summarise the spectral content of the rate-map representation across auditory filters and are computed for individual time frames. The following 14 spectral features are available:

1. 'centroid' : The spectral centroid represents the centre of gravity of the rate-map and is one of the most frequently-used timbre parameters [Tzanetakis2002], [Jensen2004], [Peeters2011]. The centroid is normalised by the highest rate-map centre frequency to reduce the influence of the gammatone parameters.
2. 'spread' : The spectral spread describes the average deviation of the rate-map around its centroid, which is commonly associated with the bandwidth of the signal. Noise-like signals have usually a large spectral spread, while individual tonal sounds with isolated peaks will result in a low spectral spread. Similar to the centroid, the spectral spread is normalised by the highest rate-map centre frequency, such that the feature value ranges between zero and one.
3. 'brightness' : The brightness reflects the amount of high frequency information and is measured by relating the energy above a pre-defined cutoff frequency to the total energy. This cutoff frequency is set to sf_br_cf = 1500 Hz by default [Jensen2004], [Peeters2011]. This feature might be used to quantify the sensation of sharpness.
4. 'high-frequency content' : The high-frequency content is another metric that measures the energy associated with high frequencies. It is derived by weighting each channel in the rate-map by its squared centre frequency and integrating this representation across all frequency channels [Jensen2004]. To reduce the sensitivity of this feature to the overall signal level, the high-frequency content feature is normalised by the rate-map integrated across-frequency.
5. 'crest' : The SCM is defined as the ratio between the maximum value and the arithmetic mean and can be used to characterise the peakiness of the rate-map. The feature value is low for signals with a flat spectrum and high for a rate-map with a distinct spectral peak [Peeters2011], [Lerch2012].
6. 'decrease' : The spectral decrease describes the average spectral slope of the rate-map representation, putting a stronger emphasis on the low frequencies [Peeters2011].
7. 'entropy' : The entropy can be used to capture the peakiness of the spectral representation [Misra2004]. The resulting feature is low for a rate-map with many distinct spectral peaks and high for a flat rate-map spectrum.
8. 'flatness' : The SFM is defined as the ratio of the geometric mean to the arithmetic mean and can be used to distinguish between harmonic (SFM is close to zero) and a noisy signals (SFM is close to one) [Peeters2011].
9. 'irregularity' : The spectral irregularity quantifies the variations of the logarithmically-scaled rate-map across frequencies [Jensen2004].
10. 'kurtosis' : The excess kurtosis measures whether the spectrum can be characterised by a Gaussian distribution [Lerch2012]. This feature will be zero for a Gaussian distribution.
11. 'skewness' : The spectral skewness measures the symmetry of the spectrum around its arithmetic mean [Lerch2012]. The feature will be zero for silent segments and high for voiced speech where substantial energy is present around the fundamental frequency.
12. 'roll-off' : Determines the frequency in Hz below which a pre-defined percentage sf_ro_perc of the total spectral energy is concentrated. Common values for this threshold are between sf_ro_perc = 0.85 [Tzanetakis2002] and sf_ro_perc = 0.95 [Scheirer1997], [Peeters2011]. The roll-off feature is normalised by the highest rate-map centre frequency and ranges between zero and one. This feature can be useful to distinguish voiced from unvoiced signals.
13. 'flux' : The spectral flux evaluates the temporal variation of the logarithmically-scaled rate-map across adjacent frames [Lerch2012]. It has been suggested to be useful for the distinction of music and speech signals, since music has a higher rate of change [Scheirer1997].
14. 'variation' : The spectral variation is defined as one minus the normalised correlation between two adjacent time frames of the rate-map [Peeters2011].

A list of all parameters is presented in Table 11.

Table 11 List of parameters related to 'spectral_features'.
Parameter Default Description
sf_requests 'all'

List of requested spectral features (e.g. 'flux'). Type

help spectralFeaturesProc in the Matlab command window

to display the full list of supported spectral features.

sf_br_cf 1500 Cut-off frequency in Hz for brightness feature
sf_ro_perc 0.85 Threshold (re. 1) for spectral roll-off feature

The extraction of spectral features is demonstrated by the script Demo_SpectralFeatures.m, which produces the plots shown in Fig. 15. The complete set of 14 spectral features is computed for the speech signal shown in the top left panel. Whenever the unit of the spectral feature was given in frequency, the feature is shown in black in combination with the corresponding rate-map representation.

## Onset strength (onsetProc.m)¶

According to [Bregman1990], common onsets and offsets across frequency are important grouping cues that are utilised by the human auditory system to organise and integrate sounds originating from the same source. The onset processor is based on the rate-map representation, and therefore, the choice of the rate-map parameters, as listed in Table 10, will influence the output of the onset processor. The temporal resolution is controlled by the window size rm_wSizeSec and the step size rm_hSizeSec, respectively. The amount of temporal smoothing can be adjusted by the leaky integrator time constant rm_decaySec, which reduces the amount of temporal fluctuations in the rate-map. Onset are detected by measuring the frame-based increase in energy of the rate-map representation. This detection is performed based on the logarithmically-scaled energy, as suggested by [Klapuri1999]. It is possible to limit the strength of individual onsets to an upper limit, which is by default set to ons_maxOnsetdB = 30. A list of all parameters is presented in Table 12.

Table 12 List of parameters related to 'onset_strength'
Parameter Default Description
ons_maxOnsetdB 30 Upper limit for onset strength in dB

The resulting onset strength expressed in decibel, which is a function of time frame and frequency channel, is shown in Fig. 16. The two figures can be replicated by running the script DEMO_OnsetStrength.m. When considering speech as an input signal, it can be seen that onsets appear simultaneously across a broad frequency range and typically mark the beginning of an auditory event.

## Offset strength (offsetProc.m)¶

Similarly to onsets, the strength of offsets can be estimated by measuring the frame-based decrease in logarithmically-scaled energy. As discussed in the previous section, the selected rate-map parameters as listed in Table 10 will influence the offset processor. Similar to the onset strength, the offset strength can be constrained to a maximum value of ons_maxOffsetdB = 30. A list of all parameters is presented in Table 12.

Table 13 List of parameters related to 'offset_strength'.
Parameter Default Description
ofs_maxOffsetdB 30 Upper limit for offset strength in dB

The offset strength is demonstrated by the script DEMO_OffsetStrength.m and the corresponding figures are depicted in Fig. 17. It can be seen that the overall magnitude of the offset strength is lower compared to the onset strength. Moreover, the detected offsets are less synchronised across frequency.

## Binary onset and offset maps (transientMapProc.m)¶

The information about sudden intensity changes, as represented by onsets or offsets, can be combined in order to organise and group the acoustic input according to individual auditory events. The required processing is similar for both onsets and offsets, and is summarised by the term transient detection. To apply this transient detection based on the onset strength or offset strength, the user should use the request name ’onset_map’ or ’offset_map’, respectively. Based on the transient strength which is derived from the corresponding onset strength and offset strength processor (described in Onset strength (onsetProc.m) and Offset strength (offsetProc.m), a binary decision about transient activity is formed, where only the most salient information is retained. To achieve this, temporal and across-frequency constraints are imposed for the transient information. Motivated by the observation that two sounds are perceived as separated auditory events when the difference in terms of their onset time is in the range of 20 ms – 40 ms [Turgeon2002], transients are fused if they appear within a pre-defined time context. If two transients appear within this time context, only the stronger one will be considered. This time context can be adjusted by trm_fuseWithinSec. Moreover, the minimum across-frequency context can be controlled by the parameters trm_minSpread. To allow for this selection, individual transients which are connected across multiple TF units are extracted using Matlab’s image labelling tool bwlabel . The binary transient map will only retain those transients which consists of at least trm_minSpread connected TF units. The salience of the cue can be specified by the detection thresholds trm_minStrengthdB. Whereas this thresholds control the required relative change, a global threshold excludes transient activity if the corresponding rate-map level is below a pre-defined threshold, as determined by trm_minValuedB. A summary of all parameters is given in Table 14.

Table 14 List of parameters related to 'onset_map' and 'offset_map'.
Parameter Default Description
trm_fuseWithinSec 30E-3 Time constant below which transients are fused
trm_minSpread 5 Minimum number of connected TF units
trm_minStrengthdB 3 Minimum onset strength in dB
trm_minValuedB -80 Minimum rate-map level in dB

To illustrate the benefit of selecting onset and offset information, a rate-map representation is shown in Fig. 18 (left panel), where the corresponding onsets and offsets detected by the transientMapProc, through two individual requests ’onset_map’ and ’offset_map’, and without applying any temporal or across-frequency constraints are overlaid (respectively in black and white). It can be seen that the onset and offset information is quite noisy. When only retaining the most salient onsets and offsets by applying temporal and across-frequency constraints (right panel), the remaining onsets and offsets can be used as temporal markers, which clearly mark the beginning and the end of individual auditory events.

## Pitch (pitchProc.m)¶

Following [Slaney1990], [Meddis2001], [Meddis1997], the sub-band periodicity analysis obtained by the ACF can be integrated across frequency by giving equal weight to each frequency channel. The resulting SACF reflects the strength of periodicity as a function of the lag period for a given time frame, as illustrated in Fig. 13. Based on the SACF representation, the most salient peak within the plausible pitch frequency range p_pitchRangeHz is detected for each frame in order to obtain an estimation of the fundamental frequency. In addition to the peak position, the corresponding amplitude of the SACF is used to reflect the confidence of the underlying pitch estimation. More specifically, if the SACF magnitude drops below a pre-defined percentage p_confThresPerc of its global maximum, the corresponding pitch estimate is considered unreliable and set to zero. The estimated pitch contour is smoothed across time frames by a median filter of order p_orderMedFilt, which aims at reducing the amount of octave errors. A list of all parameters is presented in Table 15. In the context of pitch estimation, it will be useful to experiment with the settings related to the non-linear pre-processing of the ACF, as described in Auto-correlation (autocorrelationProc.m).

Table 15 List of parameters related to 'pitch'.
Parameter Default Description
p_pitchRangeHz [80 400] Plausible pitch frequency range in Hz
p_confThresPerc 0.7 Confidence threshold related to the SACF magnitude
p_orderMedFilt 3 Order of the median filter

The task of pitch estimation is demonstrated by the script DEMO_Pitch and the corresponding SACF plots are presented in Fig. 19. The pitch is estimated for an anechoic speech signal (top left panel). The corresponding is presented in the top right panel, where each black cross represents the most salient lag period per time frame. The plausible pitch range is indicated by the two white dashed lines. The confidence measure of each individual pitch estimates is shown in the bottom left panel, which is used to set the estimated pitch to zero if the magnitude of the SACF is below the threshold. The final pitch contour is post-processed with a median filter and shown in the bottom right panel. Unvoiced frames, where no pitch frequency was detected, are indicated by NaN‘s.

## Medial Olivo-Cochlear (MOC) feedback (mocProc.m)¶

It has now been a well known fact that in the auditory system, an efferent pathway of fibers exists, originating from the auditory neurons in the olivary complex to the outer hair cells  [Guinan2006]. This operates as a top-down feedback path, as opposed to the bottom-up peripheral signal transmission towards the brain, affecting the movement of the basilar membrane in response to the input stimulus. The MOC processor mimics this feedback, particularly originating from the medial part of the olivary complex. In Auditory front-end, this feedback is realised by monitoring the output from the ratemap processor which corresponds to the auditory neurons’ firing rate, and by controlling accordingly the nonlinear path gain of the DRNL processor which corresponds to the basilar membrane’s nonlinear operation. This approach is based on the work of [Clark2012], except that the auditory nerve processing model is simplified as the ratemap processor in Auditory front-end.

The input to the MOC processor is the time frame-frequency representation from the ratemap processor. This is then converted into an attenuation factor per each frequency channel. The constants for this rate-to-attenuation conversion are internal parameters of the processor, which can be set in accordance with various physiological findings such as those of [Liberman1988]. The amplitude relationship was adopted from the work of [Clark2012]. The time course and delay of the feedback activity, such as in the work of [Backus2006], can be approximated by adjusting the leaky integrator time constant rm_decaySec and the window step size rm_hSizeSec of the ratemap processor.

In addition to this so-called reflexive feedback, realised as a closed-loop operation, the reflective feedback is realised by means of additional control parameters that can be modified externally in an open-loop manner. The two parameters moc_mocIpsi and moc_mocContra are included for this purpose. Depending on applications, these two can be accessed and adjusted via the Blackboard system, and applied jointly with the reflexive feedback to the nonlinear path as the final multiplicative gain factor. Table 16 lists the parameters for the processor, including the above-mentioned two. The other two parameters moc_mocThresholdRatedB and moc_mocMaxAttenuationdB are specified such that the input level- attenuation relationship is fitted best to the data of [Liberman1988] which is scaled within a range of 0 dB to 40 dB by [Clark2012].

Table 16 List of parameters related to the auditory representation ’moc’.
Parameter Default Description
moc_mocIpsi 1 Ipsilateral MOC feedback factor (0 to 1)
moc_mocContra 1 Contralateral MOC feedback factor (0 to 1)
moc_mocThresholdRatedB -180 Threshold ratemap value for MOC activation in dB
moc_mocMaxAttenuationdB 40 Maximum possible MOC attenuation in dB

Fig. 20 shows, firstly on the left panel, the input-output characteristics of the MOC processor, using on-frequency stimulation from tones at 520 Hz and 3980 Hz, same as in the work of [Liberman1988]. As mentioned above, the relationship between the input level and the MOC attenuation activity through the ratemap representation was derived through curve fitting to the available data set of [Liberman1988], which is also shown on the plot. An example of input signal-DRNL output pair at 40 dB input level is shown on the right panel. The feedback applies an attenuation at the later part of the tone. These plots can be generated by running the script DEMO_MOC.m.

## Amplitude modulation spectrogram (modulationProc.m)¶

The detection of envelope fluctuations is a very fundamental ability of the human auditory system which plays a major role in speech perception. Consequently, computational models have tried to exploit speech- and noise specific characteristics of amplitude modulations by extracting so-called amplitude modulation spectrogram (AMS)features with linearly-scaled modulation filters [Kollmeier1994], [Tchorz2003], [Kim2009], [May2013a], [May2014a], [May2014b]. The use of linearly-scaled modulation filters is, however, not consistent with psychoacoustic data on modulation detection and masking in humans [Bacon1989], [Houtgast1989], [Dau1997a], [Dau1997b], [Ewert2000]. As demonstrated by [Ewert2000], the processing of envelope fluctuations can be described effectively by a second-order band-pass filter bank with logarithmically-spaced centre frequencies. Moreover, it has been shown that an AMS feature representation based on an auditory-inspired modulation filter bank with logarithmically-scaled modulation filters substantially improved the performance of computational speech segregation in the presence of stationary and fluctuating interferers [May2014c]. In addition, such a processing based on auditory-inspired modulation filters has recently also been successful in speech intelligibility prediction studies [Joergensen2011], [Joergensen2013]. To investigate the contribution of both AMS feature representations, the amplitude modulation processor can be used to extract linearly- and logarithmically-scaled AMS features. Therefore, each frequency channel of the IHC representation is analysed by a bank of modulation filters. The type of modulation filters can be controlled by setting the parameter ams_fbType to either ’lin’ or ’log’. To illustrate the difference between linear linearly-scaled and logarithmically-scaled modulation filters, the corresponding filter bank responses are shown in Fig. 21. The linear modulation filter bank is implemented in the frequency domain, whereas the logarithmically-scaled filter bank is realised by a band of second-order IIR Butterworth filters with a constant-Q factor of 1. The modulation filter with the lowest centre frequency is always implemented as a low-pass filter, as illustrated in the right panel of Fig. 21.

Similarly to the gammatone processor described in Gammatone (gammatoneProc.m), there are different ways to control the centre frequencies of the individual modulation filters, which depend on the type of modulation filters

• ams_fbType = 'lin'
1. Specify ams_lowFreqHz, ams_highFreqHz and ams_nFilter. The requested number of filters ams_nFilter will be linearly-spaced between ams_lowFreqHz and ams_highFreqHz. If ams_nFilter is omitted, the number of filters will be set to 15 by default.
• ams_fbType = 'log'
1. Directly define a vector of centre frequencies, e.g. ams_cfHz = [4 8 16 ...]. In this case, the parameters ams_lowFreqHz, ams_highFreqHz, and ams_nFilter are ignored.
2. Specify ams_lowFreqHz and ams_highFreqHz. Starting at ams_lowFreqHz, the centre frequencies will be logarithmically-spaced at integer powers of two, e.g. 2^2, 2^3, 2^4 ... until the higher frequency limit ams_highFreqHz is reached.
3. Specify ams_lowFreqHz, ams_highFreqHz and ams_nFilter. The requested number of filters ams_nFilter will be spaced logarithmically as power of two between ams_lowFreqHz and ams_highFreqHz.

The temporal resolution at which the AMS features are computed is specified by the window size ams_wSizeSec and the step size ams_hSizeSec. The window size is an important parameter, because it determines how many periods of the lowest modulation frequencies can be resolved within one individual time frame. Moreover, the window shape can be adjusted by ams_wname. Finally, the IHC representation can be downsampled prior to modulation analysis by selecting a downsampling ratio ams_dsRatio larger than 1. A full list of AMS feature parameters is shown in Table 17.

Table 17 List of parameters related to 'ams_features'.
Parameter Default Description
ams_fbType 'log' Filter bank type ('lin' or 'log')
ams_nFilter [] Number of modulation filters (integer)
ams_lowFreqHz 4 Lowest modulation filter centre frequency in Hz
ams_highFreqHz 1024 Highest modulation filter centre frequency in Hz
ams_cfHz [] Vector of modulation filter centre frequencies in Hz
ams_dsRatio 4 Downsampling ratio of the IHC representation
ams_wSizeSec 32E-3 Window duration in s
ams_hSizeSec 16E-3 Window step size in s
ams_wname 'rectwin' Window name

The functionality of the AMS feature processor is demonstrated by the script DEMO_AMS and the corresponding four plots are presented in Fig. 22. The time domain speech signal (top left panel) is transformed into a IHC representation (top right panel) using 23 frequency channels spaced between 80 and 8000 Hz. The linear and the logarithmic AMS feature representations are shown in the bottom panels. The response of the modulation filters are stacked on top of each other for each IHC frequency channel, such that the AMS feature representations can be read like spectrograms. It can be seen that the linear AMS feature representation is more noisy in comparison to the logarithmically-scaled AMS features. Moreover, the logarithmically-scaled modulation pattern shows a much higher correlation with the activity reflected in the IHC representation.

## Spectro-temporal modulation spectrogram¶

Neuro-physiological studies suggest that the response of neurons in the primary auditory cortex of mammals are tuned to specific spectro-temporal patterns [Theunissen2001], [Qiu2003]. This response characteristic of neurons can be described by the so-called STRF. As suggested by [Qiu2003], the STRF can be effectively modelled by two-dimensional (2D) Gabor functions. Based on these findings, a spectro-temporal filter bank consisting of 41 Gabor filters has been designed by [Schaedler2012]. This filter bank has been optimised for the task of ASR, and the respective real parts of the 41 Gabor filters is shown in Fig. 23.

The input is a log-compressed rate-map with a required resolution of 100 Hz, which corresponds to a step size of 10 ms. To reduce the correlation between individual Gabor features and to limit the dimensions of the resulting Gabor feature space, a selection of representative rate-map frequency channels will be automatically performed for each Gabor filter [Schaedler2012]. For instance, the reference implementation based on 23 frequency channels produces a 311 dimensional Gabor feature space.

The Gabor feature processor is demonstrated by the script DEMO_GaborFeatures.m, which produces the two plots shown in Fig. 24. A log-compressed rate-map with 25 ms time frames and 23 frequency channels spaced between 124 and 3657 Hz is shown in the left panel for a speech signal. These rate-map parameters have been adjusted to meet the specifications as recommended in the ETSI standard [ETSIES]. The corresponding Gabor feature space with 311 dimension is presented in the right panel, where vowel transition (e.g. at time frames around 0.2 s) are well captured. This aspect might be particularly relevant for the task of ASR.

## Cross-correlation (crosscorrelationProc.m)¶

The IHC representations of the left and the right ear signals is used to compute the normalised CCF in the FFT domain for short time frames of cc_wSizeSec duration with a step size of cc_hSizeSec. The CCF is normalised by the auto-correlation sequence at lag zero. This normalised CCF is then evaluated for time lags within cc_maxDelaySec (e.g., [-1 ms, 1 ms]) and is thus a three-dimensional function of time frame, frequency channel and lag time. An overview of all CCF parameters is given in Table 18. Note that the choice of these parameters will influence the computation of the ITD and the IC processors, which are described in Interaural time differences (itdProc.m) and Interaural coherence (icProc.m), respectively.

Table 18 List of parameters related to 'crosscorrelation'.
Parameter Default Description
cc_wname 'hann' Window type
cc_wSizeSec 0.02 Window duration in s
cc_hSizeSec 0.01 Window step size in s
cc_maxDelaySec 0.0011 Maximum delay in s considered in CCF computation

The script DEMO_Crosscorrelation.m demonstrates the functionality of the CCF function and the resulting plots are shown in Fig. 25. The left panel shows the ear signals for a speech source that is located closer to the right ear. As result, the left ear signal is smaller in amplitude and is delayed in comparison to the right ear signal. The corresponding CCF is shown in the right panel for 32 auditory channels, where peaks are centred around positive time lags, indicating that the source is closer to the right ear. This is even more evident by looking at the SCCF, as shown in the bottom right panel.

## Interaural time differences (itdProc.m)¶

The ITD between the left and the right ear signal is estimated for individual frequency channels and time frames by locating the time lag that corresponds to the most prominent peak in the normalised CCF. This estimation is further refined by a parabolic interpolation stage [May2011], [May2013b]. The ITD processor does not have any adjustable parameters, but it relies on the CCF described in Cross-correlation (crosscorrelationProc.m) and its corresponding parameters (see Table 18). The ITD representation is computed by using the request entry ’itd’.

The ITD processor is demonstrated by the script DEMO_ITD.m, which produces two plots as shown in Fig. 26. The ear signals for a speech source that is located closer to the right ear are shown in the left panel. The corresponding ITD estimation is presented for each individual TF unit (right panel). Apart from a few estimation errors, the estimated ITD between both ears is in the range of 0.5 ms for the majority of TF units.

## Interaural level differences (ildProc.m)¶

The ILD is estimated for individual frequency channels by comparing the frame-based energy of the left and the right-ear IHC representations. The temporal resolution can be controlled by the frame size ild_wSizeSec and the step size ild_hSizeSec. Moreover, the window shape can be adjusted by the parameter ild_wname. The resulting ILD is expressed in dB and negative values indicate a sound source positioned at the left-hand side, whereas a positive ILD corresponds to a source located at the right-hand side. A full list of parameters is shown in Table 19.

Table 19 List of parameters related to 'ild'.
Parameter Default Description
ild_wSizeSec 20E-3 Window duration in s
ild_hSizeSec 10E-3 Window step size in s
ild_wname 'hann' Window name

The ILD processor is demonstrated by the script DEMO_ILD.m and the resulting plots are presented in Fig. 27. The ear signals are shown for a speech source that is more closely located to the right ear (left panel). The corresponding ILD estimates are presented for individual TF units. It is apparent that the change considerably as a function of the centre frequency. Whereas hardly any ILDs are observed for low frequencies, a strong influence can be seen at higher frequencies where ILDs can be as high as 30 dB.

## Interaural coherence (icProc.m)¶

The IC is estimated by determining the maximum value of the normalised CCF. It has been suggested that the IC can be used to select TF units where the binaural cues (ITDs and ILDs) are dominated by the direct sound of an individual sound source, and thus, are likely to reflect the true location of one of the active sources [Faller2004]. The IC processor does not have any controllable parameters itself, but it depends on the settings of the CCF processor, which is described in Cross-correlation (crosscorrelationProc.m). The IC representation is computed by using the request entry ’ic’.

The application of the IC processor is demonstrated by the script DEMO_IC, which produces the following four plots shown in Fig. 28. The top left and bottom left panels show the anechoic and reverberant speech signal, respectively. It can be seen that the time domain signal is smeared due to the influence of the reverberation. The IC for the anechoic signal is close to one for most of the individual TF units, which indicates that the corresponding binaural cues are reliable. In contrast, the IC for the reverberant signal is substantially lower for many TF units, suggesting that the corresponding binaural cues might be unreliable due to the impact of the reverberation.

## Precedence effect (precedenceProc.m)¶

The precedence effect describes the ability of humans to fuse and localize the sound based on the first-arriving parts, in the presence of its successive version with a time delay below an echo-generating threshold [Wallach1949]. The effect of the later-arriving sound is suppressed by the first part in the localization process. The precedence effect processor in Auditory front-end models this, with the strategy based on the work of [Braasch2013]. The processor detects and removes the lag from a binaural input signal with a delayed repetition, by means of an autocorrelation mechanism and deconvolution. Then it derives the ITD and ILD based on these lag-removed signals.

The input to the precedence effect processor is a binaural time-frequency signal chunk from the gammatone filterbank. Then for each chunk a pair of ITD and ILD values is calculated as the output, by integrating the ITDs and ILDs across the frequency channels according to the weighted-image model [Stern1988], and through amplitude-weighted summation. Since these ITD/ILD calculation methods of the precedence effect processor are different from what are used for the Auditory front-end ITD and ILD processors, the Auditory front-end ITD and ILD processors are not connected to the precedence effect processor. Instead the steps for the correlation analyses and the ITD/ILD calculation are coded inside the processor as its own specific techniques. Table 20 lists the parameters needed to operate the precedence effect processor.

Table 20 List of parameters related to the auditory representation ’precedence’.
Parameter Default Description
prec_wSizeSec 20E-3 Window duration in s
prec_hSizeSec 10E-3 Window step size in s
prec_maxDelaySec 10E-3 Maximum delay in s for autocorrelation computation

Fig. 29 shows the output from a demonstration script DEMO_precedence.m. The input signal is a 800-Hz wide bandpass noise of 400 ms length, centered at 500 Hz, mixed with a reflection that has a 2 ms delay, and made binaural with an ITD of 0.4 ms and a 0-dB ILD. During the processing, windowed chunks are used as the input, with the length of 20 ms. It can be seen that after some initial confusion, the processor estimates the intended ITD and ILD values as more chunks are analyzed.

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