yoloyolo/synth/bank.go

package synth

import "github.com/netsynth/netsynth/classify"

// OscillatorBank holds synthesis layers, one per TrafficClass in the injected config map.
// It consumes WindowSnapshot data and renders stereo PCM frames.
type OscillatorBank struct {
	layers       map[classify.TrafficClass]*Layer
	tau          float64
	gainPerLayer float64
}

// NewBank creates an OscillatorBank with one Layer per entry in cfgs.
// tau is the EMA time constant in seconds (use 1.0 for D-07's "1-2 second" feel).
// gainPerLayer is computed dynamically as 1/len(cfgs) so that all layers at max
// amplitude sum to exactly 1.0 (no clipping), regardless of how many classes are active.
func NewBank(tau float64, cfgs map[classify.TrafficClass]FreqConfig) *OscillatorBank {
	b := &OscillatorBank{
		layers:       make(map[classify.TrafficClass]*Layer, len(cfgs)),
		tau:          tau,
		gainPerLayer: 1.0 / float64(len(cfgs)),
	}
	for class, cfg := range cfgs {
		b.layers[class] = NewLayer(cfg, SampleRate, tau)
	}
	return b
}

// RenderWindow updates amplitude targets from snap, then renders SamplesPerWindow
// stereo frames. Each frame is [2]float64{left, right} with values in [-1, 1].
// Each layer gets 1/N of the total gain where N is the number of layers.
func (b *OscillatorBank) RenderWindow(snap classify.WindowSnapshot) [][2]float64 {
	// Find max count for normalization
	var maxCount int64
	for _, count := range snap.Counts {
		if count > maxCount {
			maxCount = count
		}
	}

	// Update target amplitudes for all layers
	for class, layer := range b.layers {
		count := snap.Counts[class]
		layer.UpdateTarget(count, maxCount)
	}

	// Render frames
	frames := make([][2]float64, SamplesPerWindow)
	for i := range frames {
		var sumL, sumR float64
		for _, layer := range b.layers {
			sample := layer.AdvanceSample()
			gainL, gainR := PanGains(layer.Config.Pan)
			sumL += sample * b.gainPerLayer * gainL
			sumR += sample * b.gainPerLayer * gainR
		}
		frames[i] = [2]float64{sumL, sumR}
	}
	return frames
}