Topic: Sounds of Gox (Read 1069 times)

require(tuneR)

#Import headerless data from csv in working directory and convert to matrix
data<-read.csv("datafile.csv", header = FALSE)
data<-as.matrix(data)

#Selects column of matrix to transform into Hz
columnName <- 1
MaxHz = 1000
MinHz = 80
Freqs<-data[,columnName]/max(data[,columnName])
Freqs<-Freqs*MaxHz
Freqs<-Freqs + MinHz

#Function to create waveforms with frequencies of input data
MakeWave <-function (x) {

Waveform <- sine  #Desired waveform: can be sine, sawtooth, square
TotalDuration = 60  #Desired duration of output file (in seconds)
SampleRate = 44100  #Desired Sample Rate
Bitdepth = 16  #Desired Bit Depth
d=TotalDuration/length(Freqs)  #Sets the duration of note derived from each data point
Waveform(x, bit = Bitdepth, duration = d, samp.rate = SampleRate, xunit = "time")
}

#Generate output wave object
waves<-lapply(Freqs, MakeWave)  #Generate Waves from each datapoint
waves<-lapply(waves, prepComb)  #Smooth Transitions
output<-do.call(bind,waves)  #Concatenate Waves
rm(waves)  #Clear individual waves from memory
output

### Uncomment to play/save ###

#play(output)
#writeWave(output, "output.wav")

0.04951
0.08584
0.0808
0.07474
0.07921
0.0505
0.06262
0.05454
0.0505
0.056
0.06
0.0589
0.0699
0.0627
0.06785
0.0611
0.06
0.06
0.057
0.061
0.0623
0.059
0.0609
0.071
0.07
0.067
0.07
0.0645
0.067
0.06529
0.0655
0.07
0.068
0.0667
0.0655
0.0664
0.066
0.06491
0.065
0.0648
0.064
0.065
0.0641
0.064
0.06497
0.06
0.0629
0.0634
0.06085
0.06238
0.0616
0.0616
0.061
0.062
0.06111
0.0618
0.06366
0.0615
0.06219
0.06199
0.0604
0.0619
0.059
0.061
0.0627
0.0621
0.06265
0.0622
0.06231
0.0622
0.06202
0.062
0.0622
0.0619
0.06191
0.0619
0.06197
0.0614
0.06111
0.0613
0.0614
0.06281
0.067
0.08685
0.0938
0.0965
0.095
0.0949
0.105
0.102
0.105
0.101
0.102
0.1024
0.097
0.099
0.107
0.1025
0.1055
0.11501
0.132
0.1503
0.1877
0.1731
0.19
0.1989
0.1925
0.1955
0.1938
0.1931
0.23
0.26
0.39
0.34
0.243
0.21
0.24
0.2231
0.2682
0.276
0.27904
0.2682
0.223
0.2299
0.2678
0.28
0.28301
0.27675
0.2879
0.28295
0.28299
0.28
0.2844
0.283
0.27
0.2299
0.2082
0.2275
0.255
0.25105
0.205
0.19
0.204
0.233
0.2388
0.2
0.204
0.228
0.22
0.2299
0.24669
0.23801
0.24996
0.24
0.241
0.2401
0.267
0.24
0.25
0.25
0.248
0.2499
0.265
0.265
0.281
0.3
0.3
0.3
0.3
0.29997
0.295
0.29895
0.299
0.298
0.32
0.3229
0.323
0.32659
0.32659
0.3188
0.3176
0.4
0.386
0.38679
0.3495
0.31299
0.31299
0.39
0.41991
0.4443
0.4424
0.4199
0.41
0.417
0.4212
0.446
0.439
0.4799
0.52001
0.7
0.716
0.69
0.811
0.92
0.89968
0.89
0.918
1.09
0.98031
1.07001
1.0799
1.05
1.07
1.05
1.045
1.04
0.8989
0.94898
0.85
0.8345
0.87023
0.9
0.99743
0.9111
0.958
0.89
0.86
0.92021
0.9399
0.93909
0.901
0.9103
0.8999
0.88502
0.86999
0.86449
0.9329
0.88
0.918
0.89249
0.8949
0.87
0.86
0.82542
0.81648
0.765
0.74108
0.75897
0.80901
0.84971
0.86688
0.88377
0.8552
0.82
0.79898
0.7925
0.7897
0.78461
0.77411
0.78199
0.779
0.68
0.71
0.74
0.7538
0.74999
0.73
0.7369
0.77
0.86
0.9225
1
0.98991
1.0499
1.1123
1.16199
1.1979
1.1421
1.21
1.40901
1.7001
1.63011
1.559
1.7949
1.9
2.21059
2.88
3.5
3.03311
3.2
3.41
3.40609
3.333
3.45
3.641
3.8659
3.8
5.81
5.5
6.30019
8.198
7.19769
6.98701
8.03388
7.19
6.88
6.805
5.59039
6.11971
6.6901
7.14991
7.42
8.3997
8.798
8.5002
8.3001
8.4299
8.8
8.741
9.57
10.6
14.29
18.89
16.7
18.5499
23.9234
29.6
28.919
23.9497
14.6511
18.5464
19.84
19.28
19.49
17
15.681
16.89
17.51
0.00
0.00
0.00
0.00
0.00
0.00
16.45001
16.75004
16.9498
16.845
16.10098
15.397
15.4
15.44049
13.86
12.90691
14.78347
14.77609
14.31399
14.38
14.9
14.20912
14.00943
13.95099
13.99
13.81
13.7191
13.16
13.48
13.85024
13.68943
13.61
13.69542
13.68
13.98001
14.0478
13.88214
13.9394
13.49011
13.49832
13.53034
13.35
13.0946
12.05
9.26
10.75
9.7999
6.55
7.9
7.79991
9.99
9.98
9.46248
9.46051
10.131
10.7957
11.14979
10.96409
10.94555
10.83001
11.65
11.453
11.31125
10.895
10.94001
10.8506
9.65702
8.17939
8.5902
9.07011
8.969
8.79134
8.19951
8.21
8.64
8.48
8.17798
7.61101
6.8628
7.1864
6.53
5.03
4.7739
5.86435
6.078
5.8
5.61932
4.84
4.82
4.77
5.2
5.46001
6.11191
5.6114
5.42781
5.54514
5.46829
5.33
4.86969
4.9156
4.77247
4.77935
5.14009
5.03241
5.02701
5.02401
4.96
4.87
4.73443
4.2728
4.00753
4.10288
4.1
3.9308
4.15
4.04615
3.98791
3.84214
3.55701
2.55998
2.41901
2.27
2.34801
2.57
3.1588
3.17
2.545
2.77
2.77302
3.04001
3.18999
3.58113
3.27
3.24801
3.15
3.25429
3.152
3.109
2.97002
2.95959
3.00677
3.03501
2.95001
2.83993
3.08001
3.03099
2.99686
2.22
2.32896
2.56035
2.25
2.04999
2.19585
2.2
2.294
2.3289
2.33209
2.4321
2.50612
2.47003
2.47991
2.55
2.75019
2.9701
3.06
3.1151
2.794
2.82809
2.8798
3.03
2.99001
2.98
2.96999
3.05
3.2511
3.135
3.2499
3.15
3.2
3.2
3.2001
3.193
3.5202
3.95
3.89
3.8901
3.94678
3.94
4.225
4.018
4.06997
4.18552
4.166
4.248
4.72202
5.26766
5.21678
4.8808
5.57383
6.9476
6.69693
6.81
7.11358
6.3257
6.36
6.9
6.79999
6.41
6.75001
7.00177
6.68254
5.59998
5.92
6.35979
6.48979
6.18
6.3097
6.356
6.28978
5.75
5.34
5.29199
5.62667
5.3809
5.49048
5.48379
6.07561
6.1
5.9593
5.87343
5.68881
5.45345
5.69
5.59998
5.83
5.9126
5.6005
5.51468
5.26
4.46292
4.3251
4.2739
4.41
4.22201
4.38669
4.36146
4.272
4.42474
5.015
5.0288
4.77302
4.922
4.95598
4.86798
4.86001
4.9213
4.70499
4.61436
4.82001
4.98427
4.9901
4.93752
4.93016
4.86112
4.83315
4.91
4.89005
5.27
5.38
5.32656
5.34388
5.2159
5.27943
4.6939
4.8379
4.81488
4.7043
4.68596
4.676
4.55001
4.61911
4.81125
4.788
4.80838
4.86
4.90873
4.827
4.974
4.952
4.91008
4.919
4.94991
4.68715
4.79252
4.87191
4.83668
4.9279
4.91968
4.93999
4.9597
4.96892
4.93201
4.97587
5.1178
5.13766
5.35
5.26008
5.20352
4.95999
5.09822
5.13182
5.09762
5.10947
4.9794
4.90441
4.9491
5
5.07367
5.13438
5.067
5.077
5.04991
5.07001
5.04997
5.0437
4.85
4.96
4.94619
4.92996
5.00594
5.035
5.0887
5.0998
5.11801
5.0995
5.09002
5.09977
5.09877
5.1397
5.11904
5.14546
5.10324
5.13896
5.1358
5.14997
5.135
5.18011
5.27481
5.24898
5.2051
5.26599
5.44001
5.46001
5.591
5.633
5.55997
5.46829
5.57471
5.70003
5.929
5.9541
6.5
6.4
6.16382
6.30998
6.49876
6.67
6.68008
6.54781
6.4285
6.35002
6.30482
6.4195
6.647
6.60588
6.65
6.68999
6.62898
6.75999
6.44993
6.5101
6.67
6.64811
6.76207

require(tuneR)
data<-read.csv("mtgox.csv", header = F)
data<-data/max(data)
data<-data*1000
data<-data + 80
data<-as.matrix(data)
data<-round(data)

w1<-sine(data[1], bit =16, duration = 1, xunit = "time")
w2<-sine(data[2], bit =16, duration = 1, xunit = "time")
w3<-sine(data[3], bit =16, duration = 1, xunit = "time")
w4<-sine(data[4], bit =16, duration = 1, xunit = "time")
w5<-sine(data[5], bit =16, duration = 1, xunit = "time")
w6<-sine(data[6], bit =16, duration = 1, xunit = "time")
w7<-sine(data[7], bit =16, duration = 1, xunit = "time")
w8<-sine(data[8], bit =16, duration = 1, xunit = "time")
w9<-sine(data[9], bit =16, duration = 1, xunit = "time")
w10<-sine(data[10], bit =16, duration = 1, xunit = "time")
w11<-sine(data[11], bit =16, duration = 1, xunit = "time")
w12<-sine(data[12], bit =16, duration = 1, xunit = "time")
w13<-sine(data[13], bit =16, duration = 1, xunit = "time")
w14<-sine(data[14], bit =16, duration = 1, xunit = "time")
w15<-sine(data[15], bit =16, duration = 1, xunit = "time")
w16<-sine(data[16], bit =16, duration = 1, xunit = "time")
w17<-sine(data[17], bit =16, duration = 1, xunit = "time")
w18<-sine(data[18], bit =16, duration = 1, xunit = "time")
w19<-sine(data[19], bit =16, duration = 1, xunit = "time")
w20<-sine(data[20], bit =16, duration = 1, xunit = "time")
w21<-sine(data[21], bit =16, duration = 1, xunit = "time")
w22<-sine(data[22], bit =16, duration = 1, xunit = "time")
w23<-sine(data[23], bit =16, duration = 1, xunit = "time")
w24<-sine(data[24], bit =16, duration = 1, xunit = "time")
w25<-sine(data[25], bit =16, duration = 1, xunit = "time")
w26<-sine(data[26], bit =16, duration = 1, xunit = "time")
w27<-sine(data[27], bit =16, duration = 1, xunit = "time")
w28<-sine(data[28], bit =16, duration = 1, xunit = "time")
w29<-sine(data[29], bit =16, duration = 1, xunit = "time")
w30<-sine(data[30], bit =16, duration = 1, xunit = "time")
w31<-sine(data[31], bit =16, duration = 1, xunit = "time")
w32<-sine(data[32], bit =16, duration = 1, xunit = "time")
w33<-sine(data[33], bit =16, duration = 1, xunit = "time")
w34<-sine(data[34], bit =16, duration = 1, xunit = "time")
w35<-sine(data[35], bit =16, duration = 1, xunit = "time")
w36<-sine(data[36], bit =16, duration = 1, xunit = "time")
w37<-sine(data[37], bit =16, duration = 1, xunit = "time")
w38<-sine(data[38], bit =16, duration = 1, xunit = "time")
w39<-sine(data[39], bit =16, duration = 1, xunit = "time")
w40<-sine(data[40], bit =16, duration = 1, xunit = "time")
w41<-sine(data[41], bit =16, duration = 1, xunit = "time")
w42<-sine(data[42], bit =16, duration = 1, xunit = "time")
w43<-sine(data[43], bit =16, duration = 1, xunit = "time")
w44<-sine(data[44], bit =16, duration = 1, xunit = "time")
w45<-sine(data[45], bit =16, duration = 1, xunit = "time")
w46<-sine(data[46], bit =16, duration = 1, xunit = "time")
w47<-sine(data[47], bit =16, duration = 1, xunit = "time")
w48<-sine(data[48], bit =16, duration = 1, xunit = "time")
w49<-sine(data[49], bit =16, duration = 1, xunit = "time")
w50<-sine(data[50], bit =16, duration = 1, xunit = "time")
w51<-sine(data[51], bit =16, duration = 1, xunit = "time")

# ...etc to w722

Gox<-bind(w1,w2,w3,w4,w5,w6,w7,w8,w9,w10,w11,w12,w13,w14,w15,w16,w17,w18,w19,w20,w21,w22,w23,w24,w25,w26,w27,w28,w29,w30,w31,w32,w33,w34,w35,w36,w37,w38,w39,w40,w41,w42,w43,w44,w45,w46,w47,w48,w49,w50,w51,w52,w53,w54,w55,w56,w57,w58,w59,w60,w61,w62,w63,w64,w65,w66,w67,w68,w69,w70,w71,w72,w73,w74,w75,w76,w77,w78,w79,w80,w81,w82,w83,w84,w85,w86,w87,w88,w89,w90,w91,w92,w93,w94,w95,w96,w97,w98,w99,w100,w101,w102,w103,w104,w105,w106,w107,w108,w109,w110,w111,w112,w113,w114,w115,w116,w117,w118,w119,w120,w121,w122,w123,w124,w125,w126,w127,w128,w129,w130,w131,w132,w133,w134,w135,w136,w137,w138,w139,w140,w141,w142,w143,w144,w145,w146,w147,w148,w149,w150,w151,w152,w153,w154,w155,w156,w157,w158,w159,w160,w161,w162,w163,w164,w165,w166,w167,w168,w169,w170,w171,w172,w173,w174,w175,w176,w177,w178,w179,w180,w181,w182,w183,w184,w185,w186,w187,w188,w189,w190,w191,w192,w193,w194,w195,w196,w197,w198,w199,w200,w201,w202,w203,w204,w205,w206,w207,w208,w209,w210,w211,w212,w213,w214,w215,w216,w217,w218,w219,w220,w221,w222,w223,w224,w225,w226,w227,w228,w229,w230,w231,w232,w233,w234,w235,w236,w237,w238,w239,w240,w241,w242,w243,w244,w245,w246,w247,w248,w249,w250,w251,w252,w253,w254,w255,w256,w257,w258,w259,w260,w261,w262,w263,w264,w265,w266,w267,w268,w269,w270,w271,w272,w273,w274,w275,w276,w277,w278,w279,w280,w281,w282,w283,w284,w285,w286,w287,w288,w289,w290,w291,w292,w293,w294,w295,w296,w297,w298,w299,w300,w301,w302,w303,w304,w305,w306,w307,w308,w309,w310,w311,w312,w313,w314,w315,w316,w317,w318,w319,w320,w321,w322,w323,w324,w325,w326,w327,w328,w329,w330,w331,w332,w333,w334,w335,w336,w337,w338,w339,w340,w341,w342,w343,w344,w345,w346,w347,w348,w349,w350,w351,w352,w353,w354,w355,w356,w357,w358,w359,w360,w361,w362,w363,w364,w365,w366,w367,w368,w369,w370,w371,w372,w373,w374,w375,w376,w377,w378,w379,w380,w381,w382,w383,w384,w385,w386,w387,w388,w389,w390,w391,w392,w393,w394,w395,w396,w397,w398,w399,w400,w401,w402,w403,w404,w405,w406,w407,w408,w409,w410,w411,w412,w413,w414,w415,w416,w417,w418,w419,w420,w421,w422,w423,w424,w425,w426,w427,w428,w429,w430,w431,w432,w433,w434,w435,w436,w437,w438,w439,w440,w441,w442,w443,w444,w445,w446,w447,w448,w449,w450,w451,w452,w453,w454,w455,w456,w457,w458,w459,w460,w461,w462,w463,w464,w465,w466,w467,w468,w469,w470,w471,w472,w473,w474,w475,w476,w477,w478,w479,w480,w481,w482,w483,w484,w485,w486,w487,w488,w489,w490,w491,w492,w493,w494,w495,w496,w497,w498,w499,w500,w501,w502,w503,w504,w505,w506,w507,w508,w509,w510,w511,w512,w513,w514,w515,w516,w517,w518,w519,w520,w521,w522,w523,w524,w525,w526,w527,w528,w529,w530,w531,w532,w533,w534,w535,w536,w537,w538,w539,w540,w541,w542,w543,w544,w545,w546,w547,w548,w549,w550,w551,w552,w553,w554,w555,w556,w557,w558,w559,w560,w561,w562,w563,w564,w565,w566,w567,w568,w569,w570,w571,w572,w573,w574,w575,w576,w577,w578,w579,w580,w581,w582,w583,w584,w585,w586,w587,w588,w589,w590,w591,w592,w593,w594,w595,w596,w597,w598,w599,w600,w601,w602,w603,w604,w605,w606,w607,w608,w609,w610,w611,w612,w613,w614,w615,w616,w617,w618,w619,w620,w621,w622,w623,w624,w625,w626,w627,w628,w629,w630,w631,w632,w633,w634,w635,w636,w637,w638,w639,w640,w641,w642,w643,w644,w645,w646,w647,w648,w649,w650,w651,w652,w653,w654,w655,w656,w657,w658,w659,w660,w661,w662,w663,w664,w665,w666,w667,w668,w669,w670,w671,w672,w673,w674,w675,w676,w677,w678,w679,w680,w681,w682,w683,w684,w685,w686,w687,w688,w689,w690,w691,w692,w693,w694,w695,w696,w697,w698,w699,w700,w701,w702,w703,w704,w705,w706,w707,w708,w709,w710,w711,w712,w713,w714,w715,w716,w717,w718,w719,w720,w721,w722)

plot(data)
data
#play(Gox)