gurix
23dcfdba1d
- NewBank creates 11 layers from ClassFreqConfigs, one per TrafficClass - RenderWindow transforms WindowSnapshot into SamplesPerWindow stereo frames - GainPerLayer (1/11) applied per layer guarantees no clipping with all layers at max - PanGains applied per layer for constant-power stereo positioning - EMA amplitude smoothing provides temporal convergence across windows - 7 bank tests: layer count, output length, silence, non-zero, no-clip, stereo pan, EMA convergence
59 lines
1.8 KiB
Go
59 lines
1.8 KiB
Go
package synth
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import "github.com/netsynth/netsynth/classify"
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// OscillatorBank holds 11 synthesis layers, one per TrafficClass.
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// It consumes WindowSnapshot data and renders stereo PCM frames.
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type OscillatorBank struct {
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layers map[classify.TrafficClass]*Layer
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tau float64
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}
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// NewBank creates an OscillatorBank with one Layer per TrafficClass.
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// tau is the EMA time constant in seconds (use 1.0 for D-07's "1-2 second" feel).
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func NewBank(tau float64) *OscillatorBank {
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b := &OscillatorBank{
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layers: make(map[classify.TrafficClass]*Layer, NumLayers),
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tau: tau,
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}
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for _, class := range classify.AllClasses() {
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cfg := ClassFreqConfigs[class]
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b.layers[class] = NewLayer(cfg, SampleRate, tau)
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}
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return b
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}
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// RenderWindow updates amplitude targets from snap, then renders SamplesPerWindow
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// stereo frames. Each frame is [2]float64{left, right} with values in [-1, 1].
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// Per D-10: each layer gets GainPerLayer (1/11) so 11 max-amplitude layers sum to 1.0 (no clipping).
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func (b *OscillatorBank) RenderWindow(snap classify.WindowSnapshot) [][2]float64 {
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// Find max count for normalization
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var maxCount int64
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for _, count := range snap.Counts {
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if count > maxCount {
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maxCount = count
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}
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}
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// Update target amplitudes for all layers
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for _, class := range classify.AllClasses() {
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count := snap.Counts[class]
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b.layers[class].UpdateTarget(count, maxCount)
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}
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// Render frames
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frames := make([][2]float64, SamplesPerWindow)
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for i := range frames {
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var sumL, sumR float64
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for _, class := range classify.AllClasses() {
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layer := b.layers[class]
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sample := layer.AdvanceSample()
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gainL, gainR := PanGains(layer.Config.Pan)
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sumL += sample * GainPerLayer * gainL
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sumR += sample * GainPerLayer * gainR
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}
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frames[i] = [2]float64{sumL, sumR}
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}
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return frames
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}
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