Sensory Systems/Computer Models/Auditory System Simulation< Sensory Systems | Computer Models(Redirected from Sensory Systems/Simulation Auditory System)
- 1 Computer Simulations of the Auditory System
- 1.1 Working with Sound
- 1.2 Reminder of Fourier Transformations
- 1.3 Spectral Analysis of Biological Signals
- 1.4 Sound Transduction by Pinna and Outer Ear
- 1.5 Simulation of the Inner Ear
- 2 Human Speech
- 3 References
Computer Simulations of the Auditory SystemEdit
Working with SoundEdit
Audio signals can be stored in a variety of formats. They can be uncompressed or compressed, and the encoding can be open or proprietary. On Windows systems, the most common format is the WAV-format. It contains a header with information about the number of channels, sample rate, bits per sample etc. This header is followed by the data themselves. The usual bitstream encoding is the linear pulse-code modulation (LPCM) format.
Many programing languages provide commands for reading and writing WAV-files. When working with data in other formats, you have two options:
- You can either you convert them into WAV-format, and go on from there. A very comprehensive free cross-platform solution to record, convert and stream audio and video is ffmpeg (http://www.ffmpeg.org/).
- Or you can obtain special programs moduls for reading/writing the desired format.
Reminder of Fourier TransformationsEdit
To transform a continuous function, one uses the Fourier Integral:
where k represents frequency. Note that F(k) is a complex value: its absolute value gives us the amplitude of the function, and its phase defines the phase-shift between cosine and sine components.
The inverse transform is given by
If the data are sampled with a constant sampling frequency and there are N data points,
The coefficients Fn can be obtained by
Since there are a discrete, limited number of data points and with a discrete, limited number of waves, this transform is referred to as Discrete Fourier Transform (DFT). The Fast Fourier Transform (FFT) is just a special case of the DFT, where the number of points is a power of 2: .
Note that each is a complex number: its magnitude defines to the amplitude of the corresponding frequency component in the signal; and the phase of defines the corresponding phase (see illustration). If the signal in the time domain "f(t)" is real valued, as is the case with most measured data, this puts a constraint on the corresponding frequency components: in that case we have
A frequent source of confusion is the question: “Which frequency corresponds to ?” If there are N data points and the sampling period is , the frequency is given by
In other words, the lowest frequency is [in Hz], while the highest independent frequency is due to the Nyquist-Shannon theorem. Note that in MATLAB, the first return value corresponds to the offset of the function, and the second value to n=1!
Spectral Analysis of Biological SignalsEdit
Power Spectrum of Stationary SignalsEdit
Most FFT functions and algorithms return the complex Fourier coefficients . If we are only interested in the magnitude of the contribution at the corresponding frequency, we can obtain this information by
This is the power spectrum of our signal, and tells us how big the contribution of the different frequencies is.
Power Spectrum of Non-stationary SignalsEdit
Often one has to deal with signals that are changing their characteristics over time. In that case, one wants to know how the power spectrum changes with time. The simplest way is to take only a short segment of data at a time, and calculate the corresponding power spectrum. This approach is called Short Time Fourier Transform (STFT). However in that case edge effects can significantly distort the signals, since we are assuming that our signal is periodic.
To eliminate edge artifacts, the signals can be filtered, or "windowed". An examples of such a window is shown in the figure above. While some windows provide better frequency resolution (e.g. the rectangular window), others exhibit fewer artifacts such as spectral leakage (e.g. Hanning window). For a selected section of the signal, the data resulting from windowing are obtained by multiplying the signal with the window (left Figure):
An example can show how cutting a signal, and applying a window to it, can affect the spectral power distribution, is shown in the right figure above. (The corrsponding Python code can be found at  ) Note that decreasing the width of the sample window increases the width of the corresponding powerspectrum!
Stimulation strength for one time windowEdit
To obtain the power spectrum for one selected time window, the first step is to calculate the power spectrum through the Fast Fourier Transform (FFT) of the time signal. The result is the sound intensity in frequency domain, and the corresponding frequencies. The second step is to concentrate those intensities on a few distinct frequencies ("binning"). The result is a sound signal consisting of a few distinct frequencies - the location of the electrodes in the simulated cochlea. Back conversion into the time domain gives the simulated sound signal for that time window.
The following Python function does sound processing on a given signal.
import numpy as np def pSpect(data, rate): '''Calculation of power spectrum and corresponding frequencies, using a Hamming window''' nData = len(data) window = np.hamming(nData) fftData = np.fft.fft(data*window) PowerSpect = fftData * fftData.conj() / nData freq = np.arange(nData) * float(rate) / nData return (np.real(PowerSpect), freq) def calc_stimstrength(sound, rate=1000, sample_freqs=[100, 200, 400]): '''Calculate the stimulation strength for a given sound''' # Calculate the powerspectrum Pxx, freq = pSpect(sound, rate) # Generate matrix to sum over the requested bins num_electrodes = len(sample_freqs) sample_freqs = np.hstack((0, sample_freqs)) average_freqs = np.zeros([len(freq), num_electrodes]) for jj in range(num_electrodes): average_freqs[((freq>sample_freqs[jj]) * (freq<sample_freqs[jj+1])),jj] = 1 # Calculate the stimulation strength (the square root has to be taken, to get the amplitude) StimStrength = np.sqrt(Pxx).dot(average_freqs) return StimStrength
Sound Transduction by Pinna and Outer EarEdit
The outer ear is divided into two parts: the visible part on the side of the head (the pinna), and the external auditory meatus (outer ear canal) leading to the eardrum, as shown in the figure below. With such a structure, the outer ear contributes the ‘spectral cues’ for people’s sound localization abilities, making people not only have the ability to detect and identify a sound, but also have the ability to localize a sound source. 
The Pinna’s cone shape enables it to gather sound waves and funnel them into the out ear canal. On top of that, its various folds make the pinna a resonant cavity which amplifies certain frequencies. Furthermore, the interference effects resulting from the sound reflection caused by the pinna are directionally dependent and will attenuate other frequencies. Therefore, the pinna could be simulated as a filter function applied to the incoming sound, modulating its amplitude and phase spectra.
The resonance of the pinna cavity can be approximated well by 6 normal modes . Among these normal modes, the first mode, which mainly depends on the concha depth (i.e. the depth of the bowl-shaped part of the pinna nearest the ear canal), is the dominant one.
The cancellation of certain frequencies caused by the pinna reflection is called “pinna notch”.  As shown in the right figure , sound transmitted by the pinna goes through two paths, a direct path and a longer reflected path. The different paths have different length, and thereby produce phase differences. When the frequency of incoming sound signal reaches certain criterion, which is that the path difference is half of the sound wavelength, the interference of sounds via direct and reflected paths will be destructive. This phenomenon is called “pinna notch”. Normally the notch frequency could happen in the range from 6k Hz to 16k Hz depending on the pinna shape. It is also seen that the frequency response of pinna is directionally dependent. This makes the pinna contribute to the spatial cues for sound localization.
Ear Canal FunctionEdit
The outer ear canal is approximately 25 mm long and 8 mm in diameter, with a tortuous path from the entrance of the canal to the eardrum. The outer ear canal can be modeled as a cylinder closed at one end which leads to a resonant frequency around 3k Hz. This way the outer ear canal amplifies sounds in a frequency range important for human speech. 
Simulation of Outer EarEdit
Based on the main functions of the outer ear, it is easy to simulate the sound transduction by the pinna and outer ear canal with a filter, or a filter bank, if we know the characteristics of the filter.
Many researchers are working on the simulation of human auditory system, which includes the simulation of the outer ear. In the next chapter, a Pinna-Related Transfer Function model is first introduced, followed by two MATLAB toolboxes developed by Finnish and British research groups, respectively.
Model of Pinna-Related Transfer Function by SpagnolEdit
This part is entirely from the paper published by S.Spagnol, M.Geronazzo, and F.Avanzini.  In order to model the functions of the pinna, Spagnol developed a reconstruction model of the Pinna-Related Transfer Function (PRTF), which is a frequency response characterizing how sound is transduced by the pinna. This model is composed by two distinct filter blocks, accounting for resonance function and reflection function of the pinna respectively, as shown in the figure below.
and is the sampling frequency, the central frequency, and the notch depth.
For the reflection part, three second-order notch filters of the form  are designed with the parameters including center frequency , notch depth , and bandwidth .
where is the same as previously defined for the resonance function, and
each accounting for a different spectral notch.
By cascading the three in-series placed notch filters after the parallel two peak filters, an eighth-order filter is designed to model the PRTF.
By comparing the synthetic PRTF with the original one, as shown in the figures below, Spagnol concluded that the synthesis model for PRTF was overall effective. This model may have missing notches due to the limitation of cutoff frequency. Approximation errors may also be brought in due to the possible presence of non-modeled interfering resonances.
HUTear MATLAB ToolboxEdit
HUTear is a MATLAB Toolbox for auditory modeling developed by Lab of Acoustics and Audio Signal Processing at Helsinki University of Technology . This open source toolbox could be downloaded from here. The structure of the toolbox is shown in the right figure.
In this model, there is a block for “Outer and Middle Ear” (OME) simulation. This OME model is developed on the basis of Glassberg and Moor . The OME filter is usually a linear filter. Auditory filter is generated with taking the "Equal Loudness Curves at 60 dB"(ELC)/"Minimum Audible Field"(MAF)/"Minimum Audible Pressure at ear canal"(MAP) correction into account. This model accounts for the outer ear simulation. By specifying different parameters with the "OEMtool", you may compare the MAP IIR approximation and MAP data, as shown in the figure below.
MATLAB Model of the Auditory Periphery (MAP)Edit
MAP is developed by researchers in the Hearing Research Lab at University of Essex, England . Being a computer model of physiological basis of human hearing, MAP is an open-source code package for testing, developing the model, which could be downloaded from here. Its model structure is shown in the right figure.
Within the MAP model, there is the “Outer Middle Ear (OME)” sub-model, allowing the user to test and create an OME model. In this OME model, the function of the outer ear is modeled as a resonance function. The resonances are composed by two parallel bandpass filters, respectively, representing concha resonance and outer ear canal resonance. These two filters are specified by the pass frequency range, gain and order. By adding the output of resonance filters to the original sound pressure wave, the output of the outer ear model is obtained.
To test the OME model, run the function named “testOME.m”. A figure plotting the external ear resonances and stapes peak displacement will be displayed. (as shown in the figure below)
The outer ear, including pinna and outer ear canal, can be simulated as a linear filter, or a filter bank. This reflects its resonance and reflection effect to incoming sound. It is worth noting that since the pinna shape varies from person to person, the model parameters, like the resonant frequencies, depend on the subject.
One aspect not included in the models described above is the Head-Related Transfer Function(HRTF). The HRTF describes how an ear receives a sound from a point sound source in space. It is not introduced here because it goes beyond the effect of the outer ear (pinna and outer ear canal) as it is also influenced by the effects of head and torso. There are plenty of literature and publications for HRTF for the interested reader.(wiki, tutorial 1,2, reading list for spatial audio research including HRTF)
Simulation of the Inner EarEdit
The shape and organisation of the basilar membrane means that different frequencies resonate particularly strongly at different points along the membrance. This leads to a tonotopic organisation of the sensitivity to frequency ranges along the membrane, which can be modeled as being an array of overlapping band-pass filters known as "auditory filters". The auditory filters are associated with points along the basilar membrane and determine the frequency selectivity of the cochlea, and therefore the listener’s discrimination between different sounds. They are non-linear, level-dependent and the bandwidth decreases from the base to apex of the cochlea as the tuning on the basilar membrane changes from high to low frequency. The bandwidth of the auditory filter is called the critical bandwidth, as first suggested by Fletcher (1940). If a signal and masker are presented simultaneously then only the masker frequencies falling within the critical bandwidth contribute to masking of the signal. The larger the critical bandwidth the lower the signal-to-noise ratio (SNR) and the more the signal is masked.
Another concept associated with the auditory filter is the "equivalent rectangular bandwidth" (ERB). The ERB shows the relationship between the auditory filter, frequency, and the critical bandwidth. An ERB passes the same amount of energy as the auditory filter it corresponds to and shows how it changes with input frequency. At low sound levels, the ERB is approximated by the following equation according to Glasberg and Moore:
where the ERB is in Hz and F is the centre frequency in kHz.
One filter type used to model the auditory filters is the "gammatone filter". It provides a simple linear filter for describing the movement of one location of the basilar membrane for a given sound input, which is therefore easy to implement. Linear filters are popular for modeling different aspects of the auditory system. In general, they are IIR-filters (infinite impulse response) incorporating feedforward and feedback, which are defined by
where a1=1. In other words, the coefficients ai and bj uniquely determine this type of filter. The feedback-character of these filters can be made more obvious by re-shuffling the equation
(In contrast, FIR-filters, or finite impulse response filters, only involve feedforward: for them for i>1.)
Linear filters cannot account for nonlinear aspects of the auditory system. They are nevertheless used in a variety of models of the auditory system. The gammatone impulse response is given by
where is the frequency, is the phase of the carrier, is the amplitude, is the filter's order, is the filter's bandwidth, and is time.
This is a sinusoid with an amplitude envelope which is a scaled gamma distribution function.
Variations and improvements of the gammatone model of auditory filtering include the gammachirp filter, the all-pole and one-zero gammatone filters, the two-sided gammatone filter, and filter cascade models, and various level-dependent and dynamically nonlinear versions of these.
For computer simulations, efficient implementations of gammatone models are availabel for Matlab and for Python .
When working with gammatone filters, we can elegantly exploit Parseval's Theorem to determine the energy in a given frequency band:
The intensity of sound is typically expressed in deciBel (dB), defined as
where SPL = “sound pressure level” (in dB), and the reference pressure is . Note that this is much smaller than the air pressure (ca. 105 N/m2)! Also watch out, because sound is often expressed relative to "Hearing Level" instead of SPL.
- 0 - 20 dB SPL ... hearing level (0 dB for sinusoidal tones, from 1 kHz – 4 kHz)
- 60 dB SPL ... medium loud tone, conversational speech
Fundamental frequency, from the vibrations of the vocal cords in the larynx, is about 120 Hz for adult male, 250 Hz for adult female, and up to 400 Hz for children.
Formants are the dominant frequencies in human speech, and are caused by resonances of the signals from the vocal cord in our mouth etc. Formants show up as distinct peaks of energy in the sound's frequency spectrum. They are numbered in ascending order starting with the format at the lowest frequency.
Speech is often considered to consist of a sequence of acoustic units called phons, which correspond to linguistic units called phonemes. Phonemes are the smallest units of sound that allows different words to be distinguished. The word "dog", for example, contains three phonemes. Changes to the first, second, and third phoneme respectively produce the words "log", "dig", and "dot". English is said to contain 40 different phonemes, specified as in /d/, /o/, /g/ for the word "dog".
The ability of humans to decode speech signals still easily exceeds that of any algorithm developed so far. While automatic speech recognition has become fairly successful in recognizing clearly spoken speech in environments with high Signal-to-noise ratio, once the conditions become a bit less than ideal, recognition algorithms tend to perform vary poorly compared to humans. It seems from this that our computer speech recognition algorithms have not yet come close to capturing the underlying algorithm that humans use to recognize speech.
Evidence has shown that the perception of speech takes quite a different route than the perception of other sounds in the brain. While studies on non-speech sound responses have generally found response to be graded with stimulus, speech studies have repeatedly found a discretization of response when a graded stimulus is presented. For instance, Lisker and Abramson, played a pre-voiced 'b/p' sound. Whether the sound is interpreted as a /b/ or a /p/ depends on the voice onset time (VOT). They found that when smoothly varying the VOT, there was a sharp change (at ~20ms after the consonant is played) where subjects switched their identification from /b/ to /p/. Furthermore, subjects had a great deal of difficulty differentiating between two sounds in the same category (e.g. pairs of sounds with a VOTs of -10ms to 10m, which would both be /b/'s, than sounds with a 10ms to 30ms, which would be identified as a b and a p). This shows that some type of categorization scheme is going on. One of the main problems encountered when trying to build a model of speech perception is the so-called 'Lack of Invariance', which could more straightforwardly just be stated as the 'variance'. This term refers to the fact that a single phoneme (e.g. /p/ as in sPeech or Piety), has a great variety of waveforms that map to it, and that the mapping between an acoustic waveform and a phoneme is far from obvious and heavily context-dependent, yet human listeners reliably give the correct result. Even when the context is similar, a waveform will show a great deal of variance due to factors such as the pace of speech, the identity of the speaker and the tone in which he is speaking. So while there is no agreed-upon model of speech perception, the existing models can be split into two classes: Passive Perception and Active perception.
Passive Perception ModelsEdit
Passive perception theories generally describe the problem of speech perception in the same way that most sensory signal-processing algorithms do: Some raw input signal goes in, and is processed though a hierarchy where each subsequent step extracts some increasingly abstract signal from the input. One of the early examples of a passive model was distinctive feature theory. The idea is to identify the presence of sets of binary values for certain features. For example, 'nasal/oral', 'vocalic/non-vocalic'. The theory is that a phoneme is interpreted as a binary vector of the presence or absence of these features. These features can be extracted from the spectrogram data. Other passive models, such as those described by Selfridge and Uttley, involve a kind of template-matching, where a hierarchy of processing layers extract features that are increasingly abstract and invariant to certain irrelevant features (such as identity of the speaker when classifying phonemes).
Active Perception ModelsEdit
An entirely different take on speech perception are active-perception theories. These theories make the point that it would be redundant for the brain to have two parallel systems for speech perception and speech production, given that the ability produce a sound is so closely tied with the ability to identify it - proponents of these theories argue that it would be wasteful and complicated to maintain two separate databases-one containing the programs to identify phonemes, and another to produce them. They argue that speech perception is actually done by attempting to replicate the incoming signal, and thus using the same circuits for phoneme production as for identification. The Motor Theory of speech perception (Liberman et al., 1967), states that speech sounds are identified not by any sort of template matching, but by using the speech-generating mechanisms to try and regenerate a copy of the speech signal. It states that phonemes should not be seen as hidden signals within the speech, but as “cues” that the generating mechanism attempts to reproduce in a pre-speech signal. The theory states that speech-generating regions of the brain learn which speech-precursor signals will produce which sounds by the constant feedback loop of always hearing one's own speech. The babbling of babies, it is argued, is a way of learning this how to generate these “cue” sounds from pre-motor signals.
A similar idea is proposed in the analysis-by-synthesis model, by Stevens and Halle. This describes a generative model which attempts to regenerate a similar signal to the incoming sound. It essentially takes advantage of the fact that speech-generating mechanisms are similar between people, and that the characteristic features that one hears in speech can be reproduced by the speaker. As the speaker hears the sound, the speech centers attempt to generate the signal that's coming in. Comparators give constant feedback on the quality of the regeneration. The 'units of perception', are therefore not so much abstractions of the incoming sound, as pre-motor commands for generating the same speech.
Motor theories took a serious hit when a series of studies on what is now known as Broca's Aphasia were published. This condition impairs one's ability to produce speech sounds, without impairing the ability to comprehend them, whereas motor theory, taken in its original form, states that production and comprehension are done by the same circuits, so impaired speech production should imply impaired speech comprehension. The existence of Broca's aphasia appears to contradicts this prediction.
One of the most influential computational models of speech perception is called TRACE. TRACE is a neural-network-like model, with three layers and a recurrent connection scheme. The first layer extracts features from an input spectrogram in temporal order, basically simulating the cochlea. The second layer extracts phonemes from the feature information, and the third layer extracts words from the phoneme information. The model contains feed-forward (bottom-up) excitatory connections, lateral inhibitory connections, and feedback (top-down) excitatory connections. In this model, each computational unit corresponds to some unit of perception (e.g. the phoneme /p/ or the word "preposterous"). The basic idea is that, based on their input, units within a layer will compete to have the strongest output. The lateral inhibitory connections result in a sort of winner-takes-all circuit, in which the unit with the strongest input will inhibit its neighbors and become the clear winner. The feedback connections allow us to explain the effect of context-dependent comprehension - for example, suppose the phoneme layer, based on its bottom-up inputs, could not decide whether it had heard a /g/ or a /k/, but that the phoneme was preceded by 'an', and followed by 'ry'. Both the /g/ and /k/ units would initially be equally activated, sending inputs up to the word level, which would already contain excited units corresponding to words such as 'anaconda', 'angry', and 'ankle', which had been activated by the preceding 'an'. The excitement of the /g/ or /k/
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