LAMPIRAN LISTING PROGRAM DAN DATA SUARA

LAMP IRAN LAMPIRAN LISTING PROGRAM DAN DATA SUARA « SPEECH.DPR » program Speech; uses Forms, main in 'main.pas' {Forml}, Fourier in 'fourier.pas'...
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LAMP IRAN

LAMPIRAN LISTING PROGRAM DAN DATA SUARA «

SPEECH.DPR

»

program Speech; uses Forms, main in 'main.pas' {Forml}, Fourier in 'fourier.pas', windowing in 'windowing.pas', lpc in 'lpc.pas', backpro in 'backpro.pas', paramtrain in 'paramtrain.pas'

{Parameter};

{$R *.RES} begin Application.Initialize; Application.CreateForm(TForml, Forml); Application.CreateForm(TParameter, Parameter); Application.Run; end.

«

MAIN. PAS

»

unit main; interface uses Windows, Messages, Sysutils, Classes, Graphics, Controls, Forms, Dialogs, ComCtrls, StdCtrls, ExtCtrls, Buttons, TeEngine, Series~ TeeProcs, Chart, Menus, IniFiles, MsAcm, MMSystem, Fourier,math; var Forml: TForml; panjang,sequen:integer; ulang:byte; jj--------------data preprocessing sinyal -------------------cep,potong : Tdatabobot: jj---------

data untuk backpropagation

iterasi,iunit,jdata,pandata : integer: alpha,eror_mak double: hunit array of integer; masuk,wbobot Tdatabobot: vbobot array of Tdatabobot; identitas array of string:

implementation uses paramtrain,windowing,lpc,backpro; {$R

* .DFM)

const SectionWindow KeyTop KeyLeft KeyWidth KeyHeight

'RecDemo' ; 'Top' ; 'Left'; 'Width'; 'Height';

procedure tforml.frontEndi var a,awal,akhir:integer; temp: Tldimensii begin awal:==Oi akhir:=O; for a:==O to high(realdata) do if abs(realdata[a]»==confe*maksval then begin awal:=a; break; end; for a:=high(realdata) downto 0 do if abs(realdata[a]»=confe*maksval then begin akhir:=a; break; end; setlength (temp, akhir-awal+l) ; for a:=awal to akhir do temp [a-awal] ;=realdata[a]; setlength(realdata,O) ; set length (realdata, akhir-awal+l) ; for a:=O to high (temp) do realdata[a] :=temp[a]; end; procedure Tforml.PengolahanSinyal(sinyal:array of double); var p,i,j integer; win,aut array of double; frekdata array of double; Iidata dalam domain frek realtime,imgtime: Tdatabobot; II data terframing domain time realfrek,imgfrek: Tdatabobot; II data terframing domain frek jumframe integer; begin jumframe:=FrameCount(UkuranFrame,UkuranFrame div 3,high(sinyal)+1) ; setlength(realtime,jumframe); setlength(imgtime,jumframe); setlength(realfrek,jumframe) i setlength(imgfrek,jumframe); setlength(cep,jumframe) ;

pre_emphasis (O.94,sinyal) ; setlength(realtime,jumframe); for i:=O to jumframe-l do begin setlength(realtime[i),UkuranFrame) ; setlength(imgtime[i),UkuranFrame) ; setlength(realfrek[i),UkuranFrame); setlength(imgfrek[i],UkuranFrame); end; framing(UkuranFrame,UkuranFrame div 3,sinyal,realtime); setlength(win,UkuranFrame); win sinyal(O,hanning,win); for i:=O to jumframe-l do for j:=O to UkuranFrame-l do realtime[i,j) :=realtime[i,j)*win[j); for i:=O to jumframe-l do fft(UkuranFrame,realtime[ij,imgtime[ij,realfrek[ij,imgfrek[ij); for i:=O to jumframe-l do for j:=O to UkuranFrame-l do realfrek[i,j) :=loglO(sqrt(sqr(realfrek[i,j))+sqr(imgfrek[i,j)) )); for i:=O to jumframe-l do setlength(realfrek[i),length(realfrek[i)) div 2);

II p:=MakeOrder(frek); p:=15; setlength(aut,p+l); for i:=O to jumframe-l do begin setlength(cep[i],p+l); LPCAnalisis(realfrek[i],length(realfrek[i]),p,aut); Ipc2cepstral(p,p,aut,cep[i]) ; weightingcepstral(p,cep(i)); end; normalisasi; end; procedure tforml.normalisasi; var a,b:integer; kecil,besar:double; begin kecil:=lOO; besar:=-lOO; for a:=O to high(cep) do for b:=O to high(cep[a]) do begin kecil:=min(kecil,cep(a,b]); besar:=max(besar,cep[a,b]) ; end; if kecil0 then for i:=high(hunit) downto 1 do UpdateBobot(alpha,eror_j[i],hiden[i-l],vbobot[i]); end; end; result:=l;//-------normalyexit end; function GetDecision(output:tldimensi;target:Tdatabobot) : integer var i,j : integer; sum,min_e :double; begin min e:=lOOO: result:=O; for i:=O to high(target) do begin sum:=O; for j:=l to high(target[ij) do sum:=sum+abs(output[jj-target[i,j]); if min e>sum then begin result:=i; min e:=sum; end; end; end; function tforml.Kenali(var isiData:Tldimensi) :integer; var output,out in Tldimensi; hiden,hiden in Tdatabobot; target Tdatabobot; i,j,k,jhiden,curr integer; begin curr:=length(isiData); if currpandata then begin stretchsuara(isidata) ;

end; setlength(masuk,l); setlength(cep,O); PengolahanSinyal(IsiData); setlength(masuk[Oj,iunit) ; masuk[O,Oj :=1; k:=l; for i:=O to high(cep) do for j:=O to high(cep[i]) do begin masuk[O,k] :=cep[i,j]; inc(k); end; InitRead(hiden in,hiden,out in,output); InitTarget(target); jhiden:=length(hunit); LayerIn(masuk[O],hiden_in[O],vbobot[O]); FungsiAktivasi(hiden_in[O],hiden[O]); if high(hunit»O then for i:=O to high(hunit)-l do begin Layerln(hiden[i],hiden_in[i+1],vbobot[i+1]); FungsiAktivasi(hiden_in[i+l],hiden[i+l]); end; LayerIn(hiden[jhiden-l],out_in,wbobot); FungsiAktivasi(out_in,output) ; i:=GetDecision(output, target) ; result:=i; end;

«

FOURIER.PAS » {$N+,E+} (* Allows code to use type 'double' and run on any iX86 machine *) {$R-} (* Turn off range checking ... we violate array bounds rules *) unit Fourier; interface procedure fft ( NumSamples: var var var var

Realln: Imagln: RealOut: ImagOut:

word; array array array array

must be a positive integer power of 2 of of of of

double; double; double; double ) ;

implementation function IsPowerOfTwo ( x: word ): boolean; var i, y: word; begin y

for i

2;

:= 1 to 15 do begin

if x = y then begin IsPowerOfTwo .- TRUE; exit; end; y :=

y SHL 1;

end; IsPowerOfTwo .- FALSE; end; procedure FourierTransform ( AngleNumerator: double; NumSamples: word; var Realln: array of double; var Imagln: array of double; array of double; var RealOut: var ImagOut: array of double ) ; var NumBits, i, j, k, n, BlockSize, BlockEnd: word; delta_angle, delta_ar: double; alpha, beta: double; tr, ti, ar, ai: double; begin if not IsPowerOf~wo(NumSamples) or (NumSamples=panjang then hasil[b,aj:=O else hasil[b,a] :=sinyal[posisi+a]; end; inc(posisi,n-m); inc (b) ; until posisi>panjang; end;

prosedur pre emphasis koefisien -> nilai dari penguatan berkisar antara 0.9 - 1 sinya1 -> sinyal yang akan dilakukan proses pre emhasis }

procedure pre_emphasis (koefisien:double;var sinyal:array of double); var temp:array of double; a : integer; begin setlength (temp, high (sinyal) ); for a:=l to high (sinyal) do temp[aj :=sinyal[a]-koefisien*sinyal[a-1]; for a:=l to high (sinyal) do sinyal[a] :=temp[a]; end; procedure hanning_win(var win:array of double); var arg:double; a : integer; panjang:integeri begin panjang:=high(win); arg:= M_2PI /panjang; for a:=O to panjang do win[a]:= 0.5 * (1 - cos (a * arg));

end;

prosedur windowing sinyal panjang -> panjang dari daerah yang akan di window nflg -> normalisasi flag o -> tidak dinormalisasi 1 -> normalisasi oleh power 2 -> normalisasi oleh magnitude kode -> kode tipe window yang dipakai win -> koefisien hasil windowing; }

procedure win_sinyal(nflg:integer;kode:twindow;var win:array of double); var a:integer; g:double; panjang:integer; begin g:=l; panjang:=high(win); for a:=O to panjang do win[a] :=0; hanning:hanning_win(win) ; case nflg of O:g:=l; l:begin g:=O; for a:=O to panjang do g:=g+sqr(win[a]); g:=sqrt(g); end; 2:begin g:=O; for a:=O to panjang do g:=g+win [aJ; end; end; for a:=O to panjang do win[aJ:=win[aJ/g; end; end.

«

LPC. PAS

{$N+,E+} machine *) {$R-} rules *)

» (* Allows code to use type 'double' and run on any iX86 (* Turn off range checking ... we violate array bounds

unit lpc; interface uses math, main;

function LPCAnalisis(sinyal:array of double;framelength,p:integer;var a:array of double) : integer; function MakeOrder (BandWith: integer) :integer; procedure Ipc2cepstral(pl,p2:integer;a:array of double;var c:array of double); procedure weightingcepstral(p;integer;var c:array of double);

const M PI:double=3.l4159265358979323846; implementation function MakeOrder(BandWith:integer) :integer; begin result:=2*(BandWith div 1000+1); end;

//-----------------------------------------// { fungsi autokorelasi untuk meminimalisasi mse dari LPC p -> order dari prediksi r -> koefisien autokorelasi frame_length -> panjang frame sinyal -> data sinyal hasil procedure adalah : koefisien autokorelasi }

//-----------------------------------------// procedure autocorelation(sinyal:array of double;frame_length,p:integer;var r:array of double); var a,b:integer; temp :double; begin for a:=O to p do begin temp:=O; for b:=O to frame_length-1-a do temp:=temp+sinyal[b]*sinyal[b+a]; r[a] :=temp; end; end;

//-----------------------------------------// { fungsi untuk mencari koefisien prediksi dari LPC r -> koefisien autokorelasi p -> order dari prediksi eps -> singular check kp -> koefisien prediksi hasil fungsi adalah : o -> normaly completed 1 -> abnormaly completed 2 -> unstable LPC }

//-----------------------------------------//

function CariKoefisienPrediksi(r:array of double;p:integer;eps:double;var kp:array of double) : integer; var rmd,mue :double; a,b,flag:integer; c : array of double; begin flag:=O; setlength(c,p+I); if eps order dari Ipc a -> koefisien dari lpc hasilnya adalah apakah Ipc kita stabil atau nggak }

//-----------------------------------------// function LPCAnalisis(sinyal:array of double;framelength,p:integer;var a:array of double) : integer; var r :array of double; flag,b,c:integer;

temp :double; sinpred :array of double; begin setlength(r,p+1); setlength(sinpred,framelength) ; autoeorelation(sinyal,framelength,p,r) ; flag:=CariKoefisienPrediksi(r,p,-l,a) ; for b:=l to framelength-1 do begin temp:=O; for e:==l to p do if b-e>=O then temp:=temp+sinyal(b-e]*a[e]; sinpred(b] :=temp; end; result:==flag; end;

//-----------------------------------------// {

prosedur untuk meneari koefisien eepstral pI -> order dari lpe p2 -> order dari eepstral a -> koefisien dari lpe e -> koefisien dari cepstral hasilnya adalah koefisien eepstral -> e )

//-----------------------------------------// procedure Ipc2eepstral(p1,p2:integer;a:array of double;var e:array of double); var i,j,k : integer; temp :double; begin e[O] :==log10(a[O]); e [1] : =-a [1) ; for i:=2 to p2 do begin j : =i; if i>p1 then k:=i-p1 else k:=l; temp:=O; repeat temp:=temp+k*e[k)*a[i-k); inc (k) ; until k>=j; e[i] :=-temp/i; if i order dari cepstral

C -> koefisien dari cepstral hasilnya adalah koefisien cepstral yang telah di boboti -> c )

//-----------------------------------------// procedure weightingcepstral(p:integer;var c:array of double); var a:integer; w:array of double; arg:double; begin setlength(w,p+l)i arg:=M_PI!p; for a:=l to p do w[aJ :=1+(p/2)*sin(a*arg)i for a:=l to p do c[aJ:=c[aJ*w[aJ; end; end.

«

BACKPRO.PAS » {$N+,E+} (* Allows code to use type 'double' and run on any iX86 machine *) {$R-) (* Turn off range checking ... we violate array bounds rules *) unit backproi interface uses math,main;

implementation procedure RandomBobot(var bobot:Tdatabobot): var a,b:integer: begin for a:=O to high (bobot) do for b:=O to high(bobot[a]) do bobot[a,b] :=random-O.5; end; procedure NguyenWidrow (var,bobot: tdatabobot) : var beta:double: a,b :integer; old :double: Unitlnput,UnitHiden:integer; begin Unitlnput:=high(bobot) ; UnitHiden:=high(bobot[O)); beta:=O.7*(power(UnitHiden,1/Unitlnput)) ; for a:=l to UnitHiden do begin old:=O: for b:=l to Unitlnput-l do old:=old+sqr(bobot[b,a)); old:=sqrt (old) ; for b:=l to UnitInput-l do

bobot[b,a] :=beta*bobot[b,a]/old; bobot[O,a] :=beta*(1-2*random); end; end; function sigmoid(nilai:real) :real begin result:=l/(l+(exp(-nilai)) ); end; function TurunanSigmoid(nilai:real) :real i begin result:= sigmoid(nilai) * (l-sigmoid(nilai) ); end; procedure InisialisasiBobot(var vbobot:array of Tdatabobot;var wbobot:Tdatabobot); var a:integer; begin RandomBobot(vbobot[O]); NguyenWidrow(vbobot[O]); if high (vbobot)>0 then for a:=l to high (vbobot) do RandomBobot(vbobot[a]); RandomBobot(wbobot) : end: procedure Layerln(prev:array of double;var next:Tldimensi:bobot:Tdatabobot): var a,b:integer: begin for a:=l to high (next) do begin next[a] :=bobot[O,a]: for b:=l to high (prev) do next[a] :=next[a]+bobot[b,a]*prev[b]; e~;

~d;

procedure FungsiAktivasi(inp:array of double;var hasil:array of double); var a:integer; begin for a:=l to high(hasil) do hasil[a] :=sigmoid(inp[a]): end; procedure CalculateOutputEror(target,output,out in:array of double;var eror_k:array of double); var a:integer; begin for a:=l to high(target) do eror_k[a] :=(target[a]-output[a])*TurunanSigmoid(out_in[a]); end: procedure CalculateHidenEror(eror_next,hiden_in:array of double;bobot:Tdatabobot;var eror_j:array of double); var a,b:integer;

eror in:double; begin for a:=l to high (bobot) do begin eror in:=O; for b:=l to high(bobot[a]) do eror_in:=eror_in+eror_next[b]*bobot[a,b]; eror j[a]:=eror in*TurunanSigmoid(hiden in[a])i end; end; procedure UpdateBobot(alpha:double;eror_next,prev_data:array of double;var bobot:Tdatabobot); var a,b:integer; begin for a:=O to high (bobot) do for b:=O to high(bobot[a]) do bobot[a,b] :=bobot[a,b]+alpha*eror_next[b]*prev_data[a]; end; end.

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