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TAV/TAD doc update

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minjaesong
2025-11-10 17:01:44 +09:00
parent edb951fb1a
commit c1d6a959f5
18 changed files with 512 additions and 423 deletions
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36
tsvm_core/src/net/torvald/tsvm/peripheral/AudioAdapter.kt
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@@ -419,7 +419,7 @@ class AudioAdapter(val vm: VM) : PeriBase(VM.PERITYPE_SOUND) {
}
// Lambda-based decompanding decoder (inverse of Laplacian CDF-based encoder)
// Converts quantized index back to normalized float in [-1, 1]
// Converts quantised index back to normalised float in [-1, 1]
private fun lambdaDecompanding(quantVal: Byte, maxIndex: Int): Float {
// Handle zero
if (quantVal == 0.toByte()) {
@@ -432,11 +432,11 @@ class AudioAdapter(val vm: VM) : PeriBase(VM.PERITYPE_SOUND) {
// Clamp to valid range
if (absIndex > maxIndex) absIndex = maxIndex
// Map index back to normalized CDF [0, 1]
val normalizedCdf = absIndex.toFloat() / maxIndex
// Map index back to normalised CDF [0, 1]
val normalisedCdf = absIndex.toFloat() / maxIndex
// Map from [0, 1] back to [0.5, 1.0] (CDF range for positive half)
val cdf = 0.5f + normalizedCdf * 0.5f
val cdf = 0.5f + normalisedCdf * 0.5f
// Inverse Laplacian CDF for x >= 0: x = -(1/λ) * ln(2*(1-F))
// For F in [0.5, 1.0]: x = -(1/λ) * ln(2*(1-F))
@@ -698,13 +698,13 @@ class AudioAdapter(val vm: VM) : PeriBase(VM.PERITYPE_SOUND) {
val msbBitplane = bs.readBits(8)
val count = bs.readBits(16)
// Initialize coefficient array to zero
// Initialise coefficient array to zero
coeffs.fill(0)
// Track coefficient significance
val states = Array(count) { TadCoeffState() }
// Initialize queues
// Initialise queues
val insignificantQueue = TadBlockQueue()
val nextInsignificant = TadBlockQueue()
val significantQueue = TadBlockQueue()
@@ -822,11 +822,11 @@ class AudioAdapter(val vm: VM) : PeriBase(VM.PERITYPE_SOUND) {
// Calculate DWT levels from sample count
val dwtLevels = calculateDwtLevels(sampleCount)
// Dequantize to Float32
// Dequantise to Float32
val dwtMid = FloatArray(sampleCount)
val dwtSide = FloatArray(sampleCount)
dequantizeDwtCoefficients(0, quantMid, dwtMid, sampleCount, maxIndex, dwtLevels)
dequantizeDwtCoefficients(1, quantSide, dwtSide, sampleCount, maxIndex, dwtLevels)
dequantiseDwtCoefficients(0, quantMid, dwtMid, sampleCount, maxIndex, dwtLevels)
dequantiseDwtCoefficients(1, quantSide, dwtSide, sampleCount, maxIndex, dwtLevels)
// Inverse DWT using CDF 9/7 wavelet (produces Float32 samples in range [-1.0, 1.0])
dwt97InverseMultilevel(dwtMid, sampleCount, dwtLevels)
@@ -891,20 +891,20 @@ class AudioAdapter(val vm: VM) : PeriBase(VM.PERITYPE_SOUND) {
}
// Simplified spectral reconstruction for wavelet coefficients
// Conservative approach: only add light dither to reduce quantization grain
// Conservative approach: only add light dither to reduce quantisation grain
private fun spectralInterpolateBand(c: FloatArray, start: Int, len: Int, Q: Float, lowerBandRms: Float) {
if (len < 4) return
xorshift32State = 0x9E3779B9u xor len.toUInt() xor (Q * 65536.0f).toUInt()
val ditherAmp = 0.05f * Q // Very light dither (~-60 dBFS)
// Just add ultra-light TPDF dither to reduce quantization grain
// Just add ultra-light TPDF dither to reduce quantisation grain
for (i in 0 until len) {
c[start + i] += tpdf() * ditherAmp
}
}
private fun dequantizeDwtCoefficients(channel: Int, quantized: ByteArray, coeffs: FloatArray, count: Int,
private fun dequantiseDwtCoefficients(channel: Int, quantised: ByteArray, coeffs: FloatArray, count: Int,
maxIndex: Int, dwtLevels: Int) {
// Calculate sideband boundaries dynamically
val firstBandSize = count shr dwtLevels
@@ -915,7 +915,7 @@ class AudioAdapter(val vm: VM) : PeriBase(VM.PERITYPE_SOUND) {
sidebandStarts[i] = sidebandStarts[i - 1] + (firstBandSize shl (i - 2))
}
// Dequantize all coefficients with stochastic reconstruction for deadzoned values
// Dequantise all coefficients with stochastic reconstruction for deadzoned values
val quantiserScale = 1.0f
for (i in 0 until count) {
var sideband = dwtLevels
@@ -927,7 +927,7 @@ class AudioAdapter(val vm: VM) : PeriBase(VM.PERITYPE_SOUND) {
}
// Check for deadzone marker
/*if (quantized[i] == DEADZONE_MARKER_QUANT) {
/*if (quantised[i] == DEADZONE_MARKER_QUANT) {
// Stochastic reconstruction: generate Laplacian noise in deadband range
val deadbandThreshold = DEADBANDS[channel][sideband]
@@ -942,13 +942,13 @@ class AudioAdapter(val vm: VM) : PeriBase(VM.PERITYPE_SOUND) {
// Apply scalar (but not quantiser weight - noise is already in correct range)
coeffs[i] = noise * TAD32_COEFF_SCALARS[sideband]
} else {*/
// Normal dequantization using lambda decompanding
val normalizedVal = lambdaDecompanding(quantized[i], maxIndex)
// Normal dequantisation using lambda decompanding
val normalisedVal = lambdaDecompanding(quantised[i], maxIndex)
// Denormalize using the subband scalar and apply base weight + quantiser scaling
// Denormalise using the subband scalar and apply base weight + quantiser scaling
// CRITICAL: Use channel-specific weights (Mid=0, Side=1)
val weight = BASE_QUANTISER_WEIGHTS[channel][sideband] * quantiserScale
coeffs[i] = normalizedVal * TAD32_COEFF_SCALARS[sideband] * weight
coeffs[i] = normalisedVal * TAD32_COEFF_SCALARS[sideband] * weight
// }
}