yoloyolo/synth/bank.go

package synth

import (
	"math"

	"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
}

// maxTremoloDepth is the highest tremolo depth across all groups.
// Used to compute headroom so tremolo doesn't cause clipping.
const maxTremoloDepth = 0.20

// 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 accounts for tremolo headroom: 1 / (N * (1 + maxTremoloDepth)).
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)) * (1.0 + maxTremoloDepth)),
	}
	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 gain with tremolo headroom. A soft limiter prevents any residual clipping.
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{softLimit(sumL), softLimit(sumR)}
	}
	return frames
}

// softLimit applies a tanh-based soft limiter to prevent clipping.
// Values within [-0.9, 0.9] pass nearly linearly; beyond that, they compress smoothly.
func softLimit(x float64) float64 {
	return math.Tanh(x)
}