代做cochlear implant strategies代写留学生Matlab语言

cochlear implants being essentially an aid to lip-reading and finding their place in human achievement as a means of truly“restoring”hearing (at least for speech in quiet conditions). Practically speaking, the transition from the formant-based processing strategies to the spectral peak strategies allowed cochlear implant recipients to (among other important things) regularly converse on the telephone.

For those of us who can hear well, it may be useful in relating this to our own experience by considering the challenges of learning a new language. In the early stages of learning, one might expect to detect the occasional word in a new language 一 akin to the 3M/House single electrode implants of the 1970s. With a bit of practice, some, perhaps several very basic words can be understood or recognised, but the full scope of sentences using these words are only occasionally understood because the missing parts of the sentence have not yet been learned. This is akin to the state of the art in the 1980s with the formant- based strategies, where only a few words were able to be successfully represented in the cochlea using electrical pulses. When the spectral peak approaches came as we entered the 1990s, it was as if overnight the learning phase of the new language was almost over, and suddenly more than 75% of everything became understandable.

Notice that for the past two decades though, the advances have become small and, in some cases, less successful than the preceding strategies. For example, in the device manufactured by Cochlear Ltd, the ACE strategy, adopted in 2002, is still the strategy used today as it gives  many people almost  100% speech intelligibility in quiet conditions. There is still significant amount of work to be done though in enabling cochlear implant recipients to hear well in noisy conditions and achieve better pitch perception for hearing music. Perhaps your role in the future will be to deliver that remaining 25% of sound that will make the difference between being able to understand, to being fluent in the language of hearing.

Each of you, through the associated lecture in week 3, the introductory laboratory in week 2 and this assignment, will study and implement into practice, three sound processing strategies: F0F1F2, SPEAK, and CIS to simulate what a cochlear implantee may possibly hear.

Note that the  latter two  are  similar strategies that vary  primarily  in  their  presentation  rather  than processing, but they have a separate history, and are both essential components of modern cochlear implant therapy.  If  you  wish  to  learn  about  cochlear  implant  strategies,  one  of  the  best  resources available freely  on  the  web  is  one  by  Dr Philipos Loizou (Signal-processing  techniques  for  cochlear implants | IEEE Journals & Magazine | IEEE Xplore). Sadly, Prof Loizou is not alive today, but his legacy in cochlear implants will certainly live on!

Expectations

The Assignment is in five parts and constitutes 10% of the total assessment for this course. The first four parts are sub-sections of code, and the final part is the full code along with a maximum 2-page reflection report. All parts are unique and individual MATLAB code must be written by you to implement the sound processing strategies listed above. The first four parts carry 0.5 marks each and are marked in the labs (these are to  help you  develop  the  code  in  bite  sizes).  The final  submission carries 8  marks  which constitutes 5 marks for the full code and 3 marks for the reflection report.

The coding MUST be done using the provided skeleton template. You will have access to Matlab through your university email address. To gain access, all you need to do is create or login to your Mathworks account (http://www.mathworks.com/login) using your  UNSW  email address and can then access the latest  version  of  Matlab  either  online (https://matlab.mathworks.com/) or  download  it  onto your computer.

Once logged in to Mathworks, you should also be able to access several Matlab learning courses if you are not familiar with Matlab. You are free to take up any of the available courses, however to be able to complete this assignment, the Matlab Onramp course (2 hours) is compulsory if you have not previously completed  it AND if  you  didn,t  attend  the   Iab  where   MatIab  was   introduCed  to  the  CIass.  See

https://matlabacademy.mathworks.com/to access the training course. Note, also that there is a plethora of YouTube tutorials for Matlab, several of which will be ultra-helpful to you (particularly ones that teach you how to write functions).

You must strictly adhere to the coding template available on Moodle. The template provides a skeleton of the six short functions that you must write, a standard test method to test your working code and some standardised output files. Refer to Appendix A in this document for additional information on the code skeleton, installation, testing and submission. Detailed comments should be included in your code to guide the marker through what is taking place. If the marker cannot understand the code, the marker cannot provide marks - help yourself by helping them.

The primary aim of your code is to input an original unprocessed sound and using either of three known sound processing strategies (F0F1F2, SPEAK and CIS), process the sound into a vocoded version which will simulate how a sound may be perceived by a cochlear implantee. To achieve this aim, you will need to write six short Matlab functions, the objectives of each of which are as follows:

1. getwav()

In this function, the code must be able to input any one of the sound files from the above-mentioned database. The function must then determine the original sampling frequency of the sound and resample if it is not 16 kHz. The tolerance rate for the final sampling rate is set to 10%. This means the final sampling rate must be within 10% of 16 kHz. The resampled sound must then be passed on to the next function. HINT: There is a line at the end of each function which states“resuIt = []”. use this Iine to send the output of one function to the next throughout the code. At present, the line will return a null output. I already heard you say thank you ࢫ .

CODE UP TO HERE CONSTITUTES PART 1 OF THIS ASSIGNMENT, MUST BE WRITTEN BY THE RELEVANT DUE DATE AND SHOWN/MARKED IN CLASS. SEE FIRST PAGE OF THIS DOCUMENT FOR DUE DATES.

2. getFTM()

The  objective  of  this  function  is  to  construct  a  frequency-time  matrix  (FTM) representation  of  the resampled original sound. The FTM must be formatted into a 2D matrix with:

o     Electrodes 1-22 making up 22 rows of the matrix representing frequency, where 1 is the apical electrode

o     Epochs 1-x (where x corresponds to the number of epochs) making up the columns of the matrix representing time

o     Each cell of the matrix containing a number representing loudness of any given electrode for any given epoch

To construct the FTM, the sound should first be split into epochs and then each epoch analysed. The epoching process will be slightly different from the one done in the introductory lab of week 2 so careful attention must be paid. Each epoch must be 8 ms (128 samples) long with its centre point shifted by 2 ms from the previous epoch (therefore each epoch will overlap the previous epoch by 6 ms, 3 ms before and 3  ms after the centre  point). See  Fig.2  below for  an illustration showing  how to create overlapping epochs. Thus, the key difference to the lab is that here, epochs will be overlapping whereas in the week 2 lab, you will have created non-overlapping epochs. Each created epoch must also be smoothed using a Hann functionhttps://en.wikipedia.org/wiki/Hann_function. The purpose of the Hann function is so that abrupt epoch edges do not create unnecessary edges (high frequency content) in your processed sound. See Fig.3 for an illustration on how Hann windowing works and how the overlapping smoothed epochs

Figure 2 - Illustration of processing sound into epochs.

Figure 3 - Illustration of applying the Hann window to epochs.

Each smoothed epoch is to be then further processed according to the three sound processing strategies. This part of the code will find the frequency content of each epoch either through finding formants or through fourier transform.

F0F1F2:

For  the  F0F1F2  strategy,  epochs  can  be processed  for  formant analysis using  the linear prediction coefficients that you made use of in the week 2 lab. We are only interested in the first two formants (F1 and F2). Once you determine F1 and F2 in each epoch, you must assign appropriate electrodes in the FTM to be“stimuIated”, based on the formant frequency values in the epoch and the frequency range of each electrode.

To  know  what  frequency  range  corresponds  to  each  of  the  22  electrodes, use   the   function “getBandlnfo()” that has been written for you. When you call upon this function, it will return a 22x4 matrix where each row represents an electrode. Column 1 of this matrix has the centre frequency of the band, column 2 has the lower cutoff frequency, column 3 has the higher cutoff frequency and column 4 has the width of the band. The higher and lower cutoff frequency information should help you determine which electrode to stimulate for a particular formant. Once you assign two electrodes to be stimulated (for  F1 and  F2), you need to assign a loudness value for each.  For simplicity, you can  use the  peak amplitude of the signal in the time domain for that epoch although you could also average all the samples in the epoch. Essentially, you want one amplitude value for each epoch and assign that amplitude value to both electrodes chosen for the formants. For the unused electrodes in each epoch, set the FTM value to zero. Therefore, the FTM for the F0F1F2 strategy will essentially have 22 rows, number of columns equalling the number of epochs and each column containing only two non-zero values. Note again, that all this time we have been using the name F0F1F2, but we know F0 represents the rate of stimulation in cochlear implants and therefore is not relevant to the FTM. So we could have essentially called this strategy the F1F2 strategy!

CODE UP TO HERE CONSTITUTES PART 2 OF THIS ASSIGNMENT, MUST BE WRITTEN BY THE RELEVANT DUE DATE AND SHOWN/MARKED IN CLASS. SEE FIRST PAGE OF THIS DOCUMENT FOR DUE DATES.

CIS and SPEAK:

For the SPEAK and CIS strategies, processing is much simpler (weII … depends ). Once you form. the epochs and smooth them with the Hann function, you will need to use the fast fourier transform. (FFT) to analyse frequency content in each epoch. For each epoch, the FFT output will be 128 samples long since each epoch is already that length (see above section on epoch creation). To combine the FFT output into the frequency bands simpIY caII upon the function “combineBands(data)” where (data, represents Your FFT output variable.

Once you have analysed all epochs for all strategies, the result will be your final FTM which will be passed on to the next function.

3. process()

The objective of this function is to further process the FTM values and only choose the ones of interest. In the case of the F0F1F2 strategy, in each epoch you will have already assigned two electrodes to non-zero values (peak amplitude of the signal in that epoch). In the case of the CIS and SPEAK strategy, the FTM initially will have all 22 electrodes with non-zero values.

.     The first step in the‘process, function (for all three strategies) will be to normalise the FTM from zero to one, this can be done by dividing every element of the FTM by the maximum of the whole FTM.

.     The second step should be to remove all small values by applying a threshold. This is to remove insignificant content from the FTM and can be considered as background noise. A suggested threshold to use is 0.01 i.e. all values equal to or less than the threshold should be set to zero.

.    The final step in the process function will be applied to  the  SPEAK  strategy  only. In the SPEAK strategy, only electrodes with the largest 8 FTM values should be retained and the remaining electrodes set to zero. You can now pass the processed FTMs to the next function.

Therefore, after the process function has been applied, the FTMs for the different strategies should have:

1.   F0F1F2 一 Maximum 2 non-zero elements per epoch

2.   CIS 一 Up to 22 non-zero elements per epoch

3.   SPEAK 一 Maximum 8 non-zero elements per epoch

CODE UP TO HERE CONSTITUTES PART 3 OF THIS ASSIGNMENT, MUST BE WRITTEN BY THE RELEVANT DUE DATE AND SHOWN/MARKED IN CLASS. SEE FIRST PAGE OF THIS DOCUMENT FOR DUE DATES.

4. applyDR()

The FTM should then be further processed using a dynamic range procedure, where the outputs of sound processing are converted to current levels that will be used for each electrode. The stimulus amplitude range will be 324-1024 microamps for all strategies. Note that  an  output  of  zero  from  the  FTM  will receive zero current. The maximum output in the FTM (AMAX  which should be equal to 1 provided you normalised the FTM) should be re-assigned to 1024 microamps (also called the C level). The‘T, IeveI WiII be the lowest (AMIN) non-zero output in the FTM and should be re-assigned to 324 microamps. In this manner, the useful dynamic range will be limited to 10 dB - that is, the difference between the T and C levels will represent a 10 dB dynamic range. You are free to use a linear function or logarithmic function to choose the values in between AMAX and AMIN. See Fig. 5 which applies a linear function.

Figure 5 - Mapping amplitudes into current levels using a linear function.

CODE UP TO HERE CONSTITUTES PART 4 OF THIS ASSIGNMENT, MUST BE WRITTEN BY THE RELEVANT DUE DATE AND SHOWN/MARKED IN CLASS. SEE FIRST PAGE OF THIS DOCUMENT FOR DUE DATES.

5. plotSignal()

This is a simple function to plot the original sound you have chosen to process in the time domain. Ensure that you label both your axes and plot appropriately as labels also carry marks!

6. plotElectrodogram()

This function will plot the FTM into a heat map with electrodes on the y-axis and time on the x-axis. The apical electrode must be located on the top of the plot. Ensure that you label both your axes and plot appropriately as labels also carry marks! For this plot, you will also need a legend for the z-axis (i.e. the colour map).

CODE UP TO HERE CONSTITUTES THE FINAL CODE, MUST BE WRITTEN BY THE RELEVANT DUE DATE AND SUBMlTTED lN MOODLE AS A MATLAB “.m” FlLE. SEE FIRST PAGE OF THIS DOCUMENT FOR DUE

DATES.

YOU MUST USE THE “cIassCochIear.m” SKELETON FlLE TO WRlTE YOUR FUNCTlONS lN THE SPACE

PROVIDED. ONCE YOU HAVE WRITTEN ANY PART, RUN THE CODE USlNG THE “cochIearProc.m” fiIe. See BELOW FOR DETAILS ON HOW TO RUN AND TEST YOUR CODE.