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all-float FFT and convolution

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@@ -1,5 +1,3 @@
*Terrarum*
Copyright (C) 2013-2023 Minjae Song ("CuriousTorvald") Copyright (C) 2013-2023 Minjae Song ("CuriousTorvald")
This program is free software: you can redistribute it and/or modify This program is free software: you can redistribute it and/or modify
@@ -14,598 +12,3 @@ GNU General Public License for more details.
You should have received a copy of the GNU General Public License You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>. along with this program. If not, see <http://www.gnu.org/licenses/>.
----
*Terrarum Sans Bitmap*
Copyright (c) 2017-2022 Minjae Song ("CuriousTorvald") and the contributors
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
----
*Joise*
Copyright (C) 2013 Jason Taylor
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*Accidental Noise Library*
Joise is a derivative work based on Josua Tippetts' C++ library:
<http://accidentalnoise.sourceforge.net/index.html>
Copyright (C) 2011 Joshua Tippetts
This software is provided 'as-is', without any express or implied
warranty. In no event will the authors be held liable for any damages
arising from the use of this software.
Permission is granted to anyone to use this software for any purpose,
including commercial applications, and to alter it and redistribute it
freely, subject to the following restrictions:
1. The origin of this software must not be misrepresented; you must not
claim that you wrote the original software. If you use this software
in a product, an acknowledgment in the product documentation would be
appreciated but is not required.
2. Altered source versions must be plainly marked as such, and must not be
misrepresented as being the original software.
3. This notice may not be removed or altered from any source distribution.
----
*Vector2.java*, *Epsilon.java*
Copyright (c) 2010-2015 William Bittle <http://www.dyn4j.org/>
All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted
provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this list of conditions
and the following disclaimer.
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distribution.
* Neither the name of dyn4j nor the names of its contributors may be used to endorse or
promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Kotlin translated and modified code Copyright (C) 2016 Minjae Song ("CuriousTorvald")
----
*PRTree*, a Priority R-Tree, a spatial index for java code
Copyright (c) 2008-2012 Robert Olofsson.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
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3. Neither the name of the authors nor the names of its contributors
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THIS SOFTWARE IS PROVIDED BY THE AUTHORS AND CONTRIBUTORS ``AS IS'' AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHORS OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
SUCH DAMAGE.
----
*HUSLColorConverter.java*
Copyright (c) 2016 Alexei Boronine
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
----
Ambient sound recordings:
ambient_forest_01.ogg
ambient_meadow_01.ogg
ambient_windy_01.ogg
ambient_woods_01.ogg
crickets_01.ogg
crickets_02.ogg
Copyright (C) 2012, 2013, 2015, 2016, 2017 Klankbeeld
Sound from <http://www.freesound.org/people/klankbeeld/>
----
Product License - GraalVM Community Edition
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License with the “Classpath” Exception.
The text of the foregoing licenses is reproduced below.
Copyright (c) 2015, 2019, Oracle and/or its affiliates. All rights reserved.
===========================================================================
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===========================================================================

61
src/net/torvald/terrarum/CreditSingleton.kt
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@@ -221,6 +221,67 @@ as well as the JVMCI and VisualVM modules, are released under version 2 of the
GNU General Public License with the “Classpath” Exception. GNU General Public License with the “Classpath” Exception.
Copyright (c) 2015, 2019, Oracle and/or its affiliates. All rights reserved. Copyright (c) 2015, 2019, Oracle and/or its affiliates. All rights reserved.
$BULLET Jdsp
Copyright (c) 2019 Sambit Paul
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
$BULLET Apache Commons Codec
Copyright 2002-2023 The Apache Software Foundation
This product includes software developed at
The Apache Software Foundation (https://www.apache.org/).
$BULLET Apache Commons CSV
Copyright 2005-2023 The Apache Software Foundation
This product includes software developed at
The Apache Software Foundation (https://www.apache.org/).
$BULLET Apache Commons Math
Copyright 2001-2022 The Apache Software Foundation
This product includes software developed at
The Apache Software Foundation (http://www.apache.org/).
This product includes software developed for Orekit by
CS Systèmes d'Information (http://www.c-s.fr/)
Copyright 2010-2012 CS Systèmes d'Information
""").split('\n') """).split('\n')

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src/net/torvald/terrarum/audio/FFT.kt Normal file
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@@ -0,0 +1,304 @@
package net.torvald.terrarum.audio
import org.apache.commons.math3.exception.MathIllegalStateException
import org.apache.commons.math3.transform.DftNormalization
import org.apache.commons.math3.transform.TransformType
import org.apache.commons.math3.util.FastMath
data class FComplex(var real: Float = 0f, var imaginary: Float = 0f) {
operator fun times(other: FComplex) = FComplex(
this.real * other.real - this.imaginary * other.imaginary,
this.real * other.imaginary + this.imaginary * other.real
)
}
/**
* Modification of the code form JDSP and Apache Commons Math
*
* Created by minjaesong on 2023-11-25.
*/
object FFT {
// org.apache.commons.math3.transform.FastFouriesTransformer.java:370
fun fft(signal: FloatArray): Array<FComplex> {
val dataRI = arrayOf(signal.copyOf(), FloatArray(signal.size))
transformInPlace(dataRI, DftNormalization.STANDARD, TransformType.FORWARD)
val output = dataRI.toComplexArray()
return getComplex(output, false)
}
// org.apache.commons.math3.transform.FastFouriesTransformer.java:404
fun ifftAndGetReal(y: Array<FComplex>): FloatArray {
val dataRI = Array<FloatArray>(2) { FloatArray(y.size) }
for (i in y.indices) {
dataRI[0][i] = y[i].real
dataRI[1][i] = y[i].imaginary
}
transformInPlace(dataRI, DftNormalization.STANDARD, TransformType.INVERSE)
return dataRI[0]
}
private fun Array<FloatArray>.toComplexArray(): Array<FComplex> {
return Array(this[0].size) {
FComplex(this[0][it], this[1][it])
}
}
// com.github.psambit9791.jdsp.transform.FastFourier.java:190
/**
* Returns the complex value of the fast fourier transformed sequence
* @param onlyPositive Set to True if non-mirrored output is required
* @throws java.lang.ExceptionInInitializerError if called before executing transform() method
* @return Complex[] The complex FFT output
*/
@Throws(ExceptionInInitializerError::class)
fun getComplex(output: Array<FComplex>, onlyPositive: Boolean): Array<FComplex> {
val dftout: Array<FComplex> = if (onlyPositive) {
val numBins: Int = output.size / 2 + 1
Array<FComplex>(numBins) { FComplex() }
}
else {
Array<FComplex>(output.size) { FComplex() }
}
System.arraycopy(output, 0, dftout, 0, dftout.size)
return dftout
}
// org.apache.commons.math3.transform.FastFouriesTransformer.java:214
/**
* Computes the standard transform of the specified complex data. The
* computation is done in place. The input data is laid out as follows
* <ul>
* <li>{@code dataRI[0][i]} is the real part of the {@code i}-th data point,</li>
* <li>{@code dataRI[1][i]} is the imaginary part of the {@code i}-th data point.</li>
* </ul>
*
* @param dataRI the two dimensional array of real and imaginary parts of the data
* @param normalization the normalization to be applied to the transformed data
* @param type the type of transform (forward, inverse) to be performed
* @throws DimensionMismatchException if the number of rows of the specified
* array is not two, or the array is not rectangular
* @throws MathIllegalArgumentException if the number of data points is not
* a power of two
*/
private fun transformInPlace(dataRI: Array<FloatArray>, normalization: DftNormalization, type: TransformType) {
val dataR = dataRI[0]
val dataI = dataRI[1]
val n = dataR.size
if (n == 1) {
return
}
else if (n == 2) {
val srcR0 = dataR[0]
val srcI0 = dataI[0]
val srcR1 = dataR[1]
val srcI1 = dataI[1]
// X_0 = x_0 + x_1
dataR[0] = srcR0 + srcR1
dataI[0] = srcI0 + srcI1
// X_1 = x_0 - x_1
dataR[1] = srcR0 - srcR1
dataI[1] = srcI0 - srcI1
normalizeTransformedData(dataRI, normalization, type)
return
}
bitReversalShuffle2(dataR, dataI)
// Do 4-term DFT.
// Do 4-term DFT.
if (type == TransformType.INVERSE) {
var i0 = 0
while (i0 < n) {
val i1 = i0 + 1
val i2 = i0 + 2
val i3 = i0 + 3
val srcR0 = dataR[i0]
val srcI0 = dataI[i0]
val srcR1 = dataR[i2]
val srcI1 = dataI[i2]
val srcR2 = dataR[i1]
val srcI2 = dataI[i1]
val srcR3 = dataR[i3]
val srcI3 = dataI[i3]
// 4-term DFT
// X_0 = x_0 + x_1 + x_2 + x_3
dataR[i0] = srcR0 + srcR1 + srcR2 + srcR3
dataI[i0] = srcI0 + srcI1 + srcI2 + srcI3
// X_1 = x_0 - x_2 + j * (x_3 - x_1)
dataR[i1] = srcR0 - srcR2 + (srcI3 - srcI1)
dataI[i1] = srcI0 - srcI2 + (srcR1 - srcR3)
// X_2 = x_0 - x_1 + x_2 - x_3
dataR[i2] = srcR0 - srcR1 + srcR2 - srcR3
dataI[i2] = srcI0 - srcI1 + srcI2 - srcI3
// X_3 = x_0 - x_2 + j * (x_1 - x_3)
dataR[i3] = srcR0 - srcR2 + (srcI1 - srcI3)
dataI[i3] = srcI0 - srcI2 + (srcR3 - srcR1)
i0 += 4
}
}
else {
var i0 = 0
while (i0 < n) {
val i1 = i0 + 1
val i2 = i0 + 2
val i3 = i0 + 3
val srcR0 = dataR[i0]
val srcI0 = dataI[i0]
val srcR1 = dataR[i2]
val srcI1 = dataI[i2]
val srcR2 = dataR[i1]
val srcI2 = dataI[i1]
val srcR3 = dataR[i3]
val srcI3 = dataI[i3]
// 4-term DFT
// X_0 = x_0 + x_1 + x_2 + x_3
dataR[i0] = srcR0 + srcR1 + srcR2 + srcR3
dataI[i0] = srcI0 + srcI1 + srcI2 + srcI3
// X_1 = x_0 - x_2 + j * (x_3 - x_1)
dataR[i1] = srcR0 - srcR2 + (srcI1 - srcI3)
dataI[i1] = srcI0 - srcI2 + (srcR3 - srcR1)
// X_2 = x_0 - x_1 + x_2 - x_3
dataR[i2] = srcR0 - srcR1 + srcR2 - srcR3
dataI[i2] = srcI0 - srcI1 + srcI2 - srcI3
// X_3 = x_0 - x_2 + j * (x_1 - x_3)
dataR[i3] = srcR0 - srcR2 + (srcI3 - srcI1)
dataI[i3] = srcI0 - srcI2 + (srcR1 - srcR3)
i0 += 4
}
}
var lastN0 = 4
var lastLogN0 = 2
while (lastN0 < n) {
val n0 = lastN0 shl 1
val logN0 = lastLogN0 + 1
val wSubN0R = W_SUB_N_R[logN0]
var wSubN0I = W_SUB_N_I[logN0]
if (type == TransformType.INVERSE) {
wSubN0I = -wSubN0I
}
// Combine even/odd transforms of size lastN0 into a transform of size N0 (lastN0 * 2).
var destEvenStartIndex = 0
while (destEvenStartIndex < n) {
val destOddStartIndex = destEvenStartIndex + lastN0
var wSubN0ToRR = 1f
var wSubN0ToRI = 0f
for (r in 0 until lastN0) {
val grR = dataR[destEvenStartIndex + r]
val grI = dataI[destEvenStartIndex + r]
val hrR = dataR[destOddStartIndex + r]
val hrI = dataI[destOddStartIndex + r]
// dest[destEvenStartIndex + r] = Gr + WsubN0ToR * Hr
dataR[destEvenStartIndex + r] = grR + wSubN0ToRR * hrR - wSubN0ToRI * hrI
dataI[destEvenStartIndex + r] = grI + wSubN0ToRR * hrI + wSubN0ToRI * hrR
// dest[destOddStartIndex + r] = Gr - WsubN0ToR * Hr
dataR[destOddStartIndex + r] = grR - (wSubN0ToRR * hrR - wSubN0ToRI * hrI)
dataI[destOddStartIndex + r] = grI - (wSubN0ToRR * hrI + wSubN0ToRI * hrR)
// WsubN0ToR *= WsubN0R
val nextWsubN0ToRR = wSubN0ToRR * wSubN0R - wSubN0ToRI * wSubN0I
val nextWsubN0ToRI = wSubN0ToRR * wSubN0I + wSubN0ToRI * wSubN0R
wSubN0ToRR = nextWsubN0ToRR
wSubN0ToRI = nextWsubN0ToRI
}
destEvenStartIndex += n0
}
lastN0 = n0
lastLogN0 = logN0
}
normalizeTransformedData(dataRI, normalization, type)
}
/**
* Applies the proper normalization to the specified transformed data.
*
* @param dataRI the unscaled transformed data
* @param normalization the normalization to be applied
* @param type the type of transform (forward, inverse) which resulted in the specified data
*/
private fun normalizeTransformedData(
dataRI: Array<FloatArray>,
normalization: DftNormalization, type: TransformType
) {
val dataR = dataRI[0]
val dataI = dataRI[1]
val n = dataR.size
assert(dataI.size == n)
when (normalization) {
DftNormalization.STANDARD -> if (type == TransformType.INVERSE) {
val scaleFactor = 1f / n.toFloat()
var i = 0
while (i < n) {
dataR[i] *= scaleFactor
dataI[i] *= scaleFactor
i++
}
}
DftNormalization.UNITARY -> {
val scaleFactor = (1.0 / FastMath.sqrt(n.toDouble())).toFloat()
var i = 0
while (i < n) {
dataR[i] *= scaleFactor
dataI[i] *= scaleFactor
i++
}
}
else -> throw MathIllegalStateException()
}
}
/**
* Performs identical index bit reversal shuffles on two arrays of identical
* size. Each element in the array is swapped with another element based on
* the bit-reversal of the index. For example, in an array with length 16,
* item at binary index 0011 (decimal 3) would be swapped with the item at
* binary index 1100 (decimal 12).
*
* @param a the first array to be shuffled
* @param b the second array to be shuffled
*/
private fun bitReversalShuffle2(a: FloatArray, b: FloatArray) {
val n = a.size
assert(b.size == n)
val halfOfN = n shr 1
var j = 0
for (i in 0 until n) {
if (i < j) {
// swap indices i & j
var temp = a[i]
a[i] = a[j]
a[j] = temp
temp = b[i]
b[i] = b[j]
b[j] = temp
}
var k = halfOfN
while (k in 1..j) {
j -= k
k = k shr 1
}
j += k
}
}
private val W_SUB_N_R = FFTConsts.W_SUB_N_R.map { it.toFloat() }.toFloatArray()
private val W_SUB_N_I = FFTConsts.W_SUB_N_I.map { it.toFloat() }.toFloatArray()
}

55
src/net/torvald/terrarum/audio/FFTConsts.java Normal file
View File

@@ -0,0 +1,55 @@
package net.torvald.terrarum.audio;
/**
* Created by minjaesong on 2023-11-25.
*/
public class FFTConsts {
// org.apache.commons.math3.transform.FastFouriesTransformer.java:58
/**
* {@code W_SUB_N_R[i]} is the real part of
* {@code exp(- 2 * i * pi / n)}:
* {@code W_SUB_N_R[i] = cos(2 * pi/ n)}, where {@code n = 2^i}.
*/
public static final double[] W_SUB_N_R =
{ 0x1.0p0, -0x1.0p0, 0x1.1a62633145c07p-54, 0x1.6a09e667f3bcdp-1
, 0x1.d906bcf328d46p-1, 0x1.f6297cff75cbp-1, 0x1.fd88da3d12526p-1, 0x1.ff621e3796d7ep-1
, 0x1.ffd886084cd0dp-1, 0x1.fff62169b92dbp-1, 0x1.fffd8858e8a92p-1, 0x1.ffff621621d02p-1
, 0x1.ffffd88586ee6p-1, 0x1.fffff62161a34p-1, 0x1.fffffd8858675p-1, 0x1.ffffff621619cp-1
, 0x1.ffffffd885867p-1, 0x1.fffffff62161ap-1, 0x1.fffffffd88586p-1, 0x1.ffffffff62162p-1
, 0x1.ffffffffd8858p-1, 0x1.fffffffff6216p-1, 0x1.fffffffffd886p-1, 0x1.ffffffffff621p-1
, 0x1.ffffffffffd88p-1, 0x1.fffffffffff62p-1, 0x1.fffffffffffd9p-1, 0x1.ffffffffffff6p-1
, 0x1.ffffffffffffep-1, 0x1.fffffffffffffp-1, 0x1.0p0, 0x1.0p0
, 0x1.0p0, 0x1.0p0, 0x1.0p0, 0x1.0p0
, 0x1.0p0, 0x1.0p0, 0x1.0p0, 0x1.0p0
, 0x1.0p0, 0x1.0p0, 0x1.0p0, 0x1.0p0
, 0x1.0p0, 0x1.0p0, 0x1.0p0, 0x1.0p0
, 0x1.0p0, 0x1.0p0, 0x1.0p0, 0x1.0p0
, 0x1.0p0, 0x1.0p0, 0x1.0p0, 0x1.0p0
, 0x1.0p0, 0x1.0p0, 0x1.0p0, 0x1.0p0
, 0x1.0p0, 0x1.0p0, 0x1.0p0 };
/**
* {@code W_SUB_N_I[i]} is the imaginary part of
* {@code exp(- 2 * i * pi / n)}:
* {@code W_SUB_N_I[i] = -sin(2 * pi/ n)}, where {@code n = 2^i}.
*/
public static final double[] W_SUB_N_I =
{ 0x1.1a62633145c07p-52, -0x1.1a62633145c07p-53, -0x1.0p0, -0x1.6a09e667f3bccp-1
, -0x1.87de2a6aea963p-2, -0x1.8f8b83c69a60ap-3, -0x1.917a6bc29b42cp-4, -0x1.91f65f10dd814p-5
, -0x1.92155f7a3667ep-6, -0x1.921d1fcdec784p-7, -0x1.921f0fe670071p-8, -0x1.921f8becca4bap-9
, -0x1.921faaee6472dp-10, -0x1.921fb2aecb36p-11, -0x1.921fb49ee4ea6p-12, -0x1.921fb51aeb57bp-13
, -0x1.921fb539ecf31p-14, -0x1.921fb541ad59ep-15, -0x1.921fb5439d73ap-16, -0x1.921fb544197ap-17
, -0x1.921fb544387bap-18, -0x1.921fb544403c1p-19, -0x1.921fb544422c2p-20, -0x1.921fb54442a83p-21
, -0x1.921fb54442c73p-22, -0x1.921fb54442cefp-23, -0x1.921fb54442d0ep-24, -0x1.921fb54442d15p-25
, -0x1.921fb54442d17p-26, -0x1.921fb54442d18p-27, -0x1.921fb54442d18p-28, -0x1.921fb54442d18p-29
, -0x1.921fb54442d18p-30, -0x1.921fb54442d18p-31, -0x1.921fb54442d18p-32, -0x1.921fb54442d18p-33
, -0x1.921fb54442d18p-34, -0x1.921fb54442d18p-35, -0x1.921fb54442d18p-36, -0x1.921fb54442d18p-37
, -0x1.921fb54442d18p-38, -0x1.921fb54442d18p-39, -0x1.921fb54442d18p-40, -0x1.921fb54442d18p-41
, -0x1.921fb54442d18p-42, -0x1.921fb54442d18p-43, -0x1.921fb54442d18p-44, -0x1.921fb54442d18p-45
, -0x1.921fb54442d18p-46, -0x1.921fb54442d18p-47, -0x1.921fb54442d18p-48, -0x1.921fb54442d18p-49
, -0x1.921fb54442d18p-50, -0x1.921fb54442d18p-51, -0x1.921fb54442d18p-52, -0x1.921fb54442d18p-53
, -0x1.921fb54442d18p-54, -0x1.921fb54442d18p-55, -0x1.921fb54442d18p-56, -0x1.921fb54442d18p-57
, -0x1.921fb54442d18p-58, -0x1.921fb54442d18p-59, -0x1.921fb54442d18p-60 };
}

77
src/net/torvald/terrarum/audio/TerrarumAudioFilter.kt
View File

@@ -1,7 +1,5 @@
package net.torvald.terrarum.audio package net.torvald.terrarum.audio
import com.github.psambit9791.jdsp.transform.FastFourier
import com.github.psambit9791.jdsp.transform.InverseFastFourier
import com.jme3.math.FastMath import com.jme3.math.FastMath
import com.jme3.math.FastMath.sin import com.jme3.math.FastMath.sin
import net.torvald.terrarum.audio.AudioMixer.SPEED_OF_SOUND import net.torvald.terrarum.audio.AudioMixer.SPEED_OF_SOUND
@@ -9,7 +7,6 @@ import net.torvald.terrarum.audio.TerrarumAudioMixerTrack.Companion.BUFFER_SIZE
import net.torvald.terrarum.audio.TerrarumAudioMixerTrack.Companion.SAMPLING_RATED import net.torvald.terrarum.audio.TerrarumAudioMixerTrack.Companion.SAMPLING_RATED
import net.torvald.terrarum.audio.TerrarumAudioMixerTrack.Companion.SAMPLING_RATEF import net.torvald.terrarum.audio.TerrarumAudioMixerTrack.Companion.SAMPLING_RATEF
import net.torvald.terrarum.roundToFloat import net.torvald.terrarum.roundToFloat
import org.apache.commons.math3.complex.Complex
import java.io.File import java.io.File
import kotlin.math.absoluteValue import kotlin.math.absoluteValue
import kotlin.math.roundToInt import kotlin.math.roundToInt
@@ -298,12 +295,11 @@ class Reverb(val delayMS: Float = 36f, var feedback: Float = 0.92f, var lowpass:
} }
} }
class Convolv(ir: File, val gain: Float = decibelsToFullscale(-12.0).toFloat()): TerrarumAudioFilter() { class Convolv(ir: File, val gain: Float = decibelsToFullscale(-24.0).toFloat()): TerrarumAudioFilter() {
private val fftLen: Int private val fftLen: Int
private val convFFT: Array<Array<Complex>> private val convFFT: Array<Array<FComplex>>
private val inbuf: Array<DoubleArray> private val inbuf: Array<FloatArray>
// private val outbuf: Array<DoubleArray>
private val BLOCKSIZE = BUFFER_SIZE / 4 private val BLOCKSIZE = BUFFER_SIZE / 4
@@ -317,8 +313,8 @@ class Convolv(ir: File, val gain: Float = decibelsToFullscale(-12.0).toFloat()):
println("IR Sample Count = $sampleCount; FFT Length = $fftLen") println("IR Sample Count = $sampleCount; FFT Length = $fftLen")
val conv = Array(2) { DoubleArray(fftLen) } val conv = Array(2) { FloatArray(fftLen) }
inbuf = Array(2) { DoubleArray(fftLen) } inbuf = Array(2) { FloatArray(fftLen) }
// outbuf = Array(2) { DoubleArray(fftLen) } // outbuf = Array(2) { DoubleArray(fftLen) }
ir.inputStream().let { ir.inputStream().let {
@@ -331,8 +327,8 @@ class Convolv(ir: File, val gain: Float = decibelsToFullscale(-12.0).toFloat()):
it.read().and(255).shl(8) or it.read().and(255).shl(8) or
it.read().and(255).shl(16) or it.read().and(255).shl(16) or
it.read().and(255).shl(24)) it.read().and(255).shl(24))
conv[0][i] = f1.toDouble() conv[0][i] = f1
conv[1][i] = f2.toDouble() conv[1][i] = f2
} }
it.close() it.close()
@@ -340,7 +336,7 @@ class Convolv(ir: File, val gain: Float = decibelsToFullscale(-12.0).toFloat()):
// fourier-transform the 'conv' // fourier-transform the 'conv'
convFFT = Array(2) { convFFT = Array(2) {
FastFourier(conv[it]).let { it.transform(); it.getComplex(false) } FFT.fft(conv[it])
} }
println("convFFT Length = ${convFFT[0].size}") println("convFFT Length = ${convFFT[0].size}")
@@ -354,72 +350,33 @@ class Convolv(ir: File, val gain: Float = decibelsToFullscale(-12.0).toFloat()):
val t1 = System.nanoTime() val t1 = System.nanoTime()
for (ch in outbuf1.indices) { for (ch in outbuf1.indices) {
push(inbuf1[ch].applyGain(gain), inbuf[ch])
push(inbuf1[ch].toDoubleArray(gain), inbuf[ch]) val inputFFT = FFT.fft(inbuf[ch])
val inputFFT = FastFourier(inbuf[ch]).let { it.transform(); it.getComplex(false) }
val Ny = inputFFT.size// + convFFT[ch].size - 1
// println("inputFFT.size=${inputFFT.size} convFFT[ch].size=${convFFT[ch].size} Ny=$Ny")
val Y = multiply(inputFFT, convFFT[ch]) val Y = multiply(inputFFT, convFFT[ch])
val y = real(ifft(Y)) val y = FFT.ifftAndGetReal(Y)
// val u = y.sliceArray(Ny - BLOCKSIZE until Ny).toFloatArray(gain) // y size == Ny val u = y.takeLast(BLOCKSIZE).toFloatArray()
val u = y.takeLast(BLOCKSIZE).map { it.toFloat() }.toFloatArray()
// println("y size: ${y.size}; Ny=$Ny")
System.arraycopy(u, 0, outbuf1[ch], 0, BLOCKSIZE) System.arraycopy(u, 0, outbuf1[ch], 0, BLOCKSIZE)
} }
val t2 = System.nanoTime() val t2 = System.nanoTime()
val ptime = (t2 - t1).toDouble() val ptime = (t2 - t1).toDouble()
val realtime = BLOCKSIZE / SAMPLING_RATED * 1000000000L val realtime = BLOCKSIZE / SAMPLING_RATED * 1000000000L
if (realtime >= ptime) { println("Processing speed: ${realtime / ptime}x")
// println("Processing speed: ${realtime / ptime}x FASTER than realtime")
}
else {
// println("Processing speed: ${ptime / realtime}x SLOWER than realtime")
}
} }
private fun real(cs: Array<Complex>): DoubleArray { private fun multiply(X: Array<FComplex>, H: Array<FComplex>): Array<FComplex> {
return cs.map { it.real }.toDoubleArray() return Array(X.size) { X[it] * H[it] }
} }
private fun ifft(y: Array<Complex>): Array<Complex> { private fun push(samples: FloatArray, buf: FloatArray) {
return InverseFastFourier(y, false).let { it.transform(); it.complex }
}
private fun multiply(X: Array<Complex>, H: Array<Complex>): Array<Complex> {
if (X.size != H.size) throw IllegalArgumentException()
return Array(X.size) {
//X[it].multiply(H[it])
// following is a snippet of the code from org.apache.commons.math3.complex.multiply,
// to remove the non-necessary sanity checks
val a = X[it]
val b = H[it]
val re = a.real * b.real - a.imaginary * b.imaginary
val im = a.real * b.imaginary + a.imaginary * b.real
Complex(re, im)
}
}
private fun push(sample: Double, buf: DoubleArray) {
System.arraycopy(buf, 1, buf, 0, buf.size - 1)
buf[buf.lastIndex] = sample
}
private fun push(samples: DoubleArray, buf: DoubleArray) {
System.arraycopy(buf, samples.size, buf, 0, buf.size - samples.size) System.arraycopy(buf, samples.size, buf, 0, buf.size - samples.size)
System.arraycopy(samples, 0, buf, buf.size - samples.size, samples.size) System.arraycopy(samples, 0, buf, buf.size - samples.size, samples.size)
} }
private fun FloatArray.toDoubleArray(gain: Float = 1f) = this.map { it.toDouble() * gain }.toDoubleArray() private fun FloatArray.applyGain(gain: Float = 1f) = this.map { it * gain }.toFloatArray()
private fun DoubleArray.toFloatArray(gain: Float = 1f) = this.map { it.toFloat() * gain }.toFloatArray()
} }
object XYtoMS: TerrarumAudioFilter() { object XYtoMS: TerrarumAudioFilter() {

3
src/net/torvald/terrarum/audio/TerrarumAudioMixerTrack.kt
View File

@@ -21,9 +21,6 @@ class TerrarumAudioMixerTrack(val name: String, val isMaster: Boolean = false, v
const val SAMPLING_RATEF = 48000f const val SAMPLING_RATEF = 48000f
const val SAMPLING_RATED = 48000.0 const val SAMPLING_RATED = 48000.0
const val BUFFER_SIZE = 512 // n ms -> 384 * n const val BUFFER_SIZE = 512 // n ms -> 384 * n
const val INDEX_BGM = 0
const val INDEX_AMB = 1
} }
val hash = getHashStr() val hash = getHashStr()