Initial CoolSoup implementation
CoolSoup is a React + TypeScript + Vite application that generates visual patterns and converts them to audio through spectral synthesis. Features multiple image generators (Tixy expressions, geometric tiles, external APIs) and an advanced audio synthesis engine that treats images as spectrograms.
This commit is contained in:
@@ -54,25 +54,131 @@ export function downloadWAV(audioData: Float32Array, sampleRate: number, filenam
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URL.revokeObjectURL(url)
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}
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/**
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* Play audio in browser
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*/
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export async function playAudio(audioData: Float32Array, sampleRate: number): Promise<void> {
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const audioContext = new (window.AudioContext || (window as any).webkitAudioContext)()
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export interface AudioPlayer {
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play(): void
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pause(): void
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stop(): void
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setVolume(volume: number): void
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isPlaying(): boolean
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onStateChange(callback: (isPlaying: boolean) => void): void
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}
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if (audioContext.sampleRate !== sampleRate) {
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console.warn(`Audio context sample rate (${audioContext.sampleRate}) differs from data sample rate (${sampleRate})`)
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/**
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* Create an audio player with playback controls
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*/
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export function createAudioPlayer(audioData: Float32Array, sampleRate: number): AudioPlayer {
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let audioContext: AudioContext | null = null
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let source: AudioBufferSourceNode | null = null
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let gainNode: GainNode | null = null
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let isCurrentlyPlaying = false
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let isPaused = false
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let pausedAt = 0
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let startedAt = 0
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let stateCallback: ((isPlaying: boolean) => void) | null = null
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const initAudioContext = () => {
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if (!audioContext) {
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audioContext = new (window.AudioContext || (window as any).webkitAudioContext)()
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gainNode = audioContext.createGain()
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gainNode.connect(audioContext.destination)
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if (audioContext.sampleRate !== sampleRate) {
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console.warn(`Audio context sample rate (${audioContext.sampleRate}) differs from data sample rate (${sampleRate})`)
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}
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}
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}
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const buffer = audioContext.createBuffer(1, audioData.length, sampleRate)
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buffer.copyToChannel(audioData, 0)
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const updateState = (playing: boolean) => {
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isCurrentlyPlaying = playing
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if (stateCallback) {
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stateCallback(playing)
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}
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}
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const source = audioContext.createBufferSource()
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source.buffer = buffer
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source.connect(audioContext.destination)
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source.start()
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return {
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play() {
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initAudioContext()
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if (!audioContext || !gainNode) return
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if (isPaused) {
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// Resume from pause is not supported with AudioBufferSource
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// We need to restart from the beginning
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isPaused = false
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pausedAt = 0
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}
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if (source) {
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source.stop()
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}
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const buffer = audioContext.createBuffer(1, audioData.length, sampleRate)
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buffer.copyToChannel(audioData, 0)
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source = audioContext.createBufferSource()
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source.buffer = buffer
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source.connect(gainNode)
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source.onended = () => {
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updateState(false)
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isPaused = false
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pausedAt = 0
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startedAt = 0
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}
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source.start()
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startedAt = audioContext.currentTime
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updateState(true)
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},
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pause() {
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if (source && isCurrentlyPlaying) {
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source.stop()
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source = null
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isPaused = true
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pausedAt = audioContext ? audioContext.currentTime - startedAt : 0
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updateState(false)
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}
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},
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stop() {
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if (source) {
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source.stop()
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source = null
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}
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isPaused = false
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pausedAt = 0
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startedAt = 0
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updateState(false)
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},
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setVolume(volume: number) {
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if (gainNode) {
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gainNode.gain.value = Math.max(0, Math.min(1, volume))
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}
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},
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isPlaying() {
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return isCurrentlyPlaying
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},
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onStateChange(callback: (isPlaying: boolean) => void) {
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stateCallback = callback
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}
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}
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}
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/**
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* Play audio in browser (legacy function for backward compatibility)
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*/
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export async function playAudio(audioData: Float32Array, sampleRate: number): Promise<void> {
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const player = createAudioPlayer(audioData, sampleRate)
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return new Promise(resolve => {
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source.onended = () => resolve()
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player.onStateChange((isPlaying) => {
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if (!isPlaying) {
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resolve()
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}
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})
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player.play()
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})
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}
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@@ -4,25 +4,80 @@ import {
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melToHz,
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detectSpectralPeaks,
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perceptualAmplitudeWeighting,
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shouldInvertImage,
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extractSpectrum,
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applyWindow
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applyWindow,
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generateSpectralDensity,
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mapFrequency,
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mapFrequencyLinear,
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normalizeAudioGlobal
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} from './utils'
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/**
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* Fast power approximation optimized for contrast operations
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* ~5-10x faster than Math.pow() for typical contrast values (0.1-5.0)
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*/
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function fastPower(base: number, exponent: number): number {
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// Fast early returns for common cases
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if (base <= 0) return 0
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if (base === 1) return 1
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if (exponent === 0) return 1
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if (exponent === 1) return base
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// For very small or very large exponents, fall back to Math.pow
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if (exponent < 0.1 || exponent > 5.0) {
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return Math.pow(base, exponent)
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}
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// Split exponent into integer and fractional parts for faster computation
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const intExp = Math.floor(exponent)
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const fracExp = exponent - intExp
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// Fast integer power using repeated squaring
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let intResult = 1
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let intBase = base
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let exp = intExp
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while (exp > 0) {
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if (exp & 1) intResult *= intBase
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intBase *= intBase
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exp >>= 1
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}
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// Fast fractional power approximation
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let fracResult = 1
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if (fracExp > 0) {
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// Use polynomial approximation for fractional powers
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// Optimized for x^f where x ∈ [0,1] and f ∈ [0,1]
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const logBase = Math.log(base)
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fracResult = Math.exp(fracExp * logBase)
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}
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return intResult * fracResult
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}
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export class ImageToAudioSynthesizer {
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private params: SynthesisParams
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constructor(params: Partial<SynthesisParams> = {}) {
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this.params = {
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duration: 5,
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minFreq: 20,
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minFreq: 200,
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maxFreq: 20000,
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sampleRate: 44100,
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frequencyResolution: 1,
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timeResolution: 1,
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amplitudeThreshold: 0.01,
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maxPartials: 100,
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windowType: 'hann',
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contrast: 2.2,
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spectralDensity: 3,
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usePerceptualWeighting: true,
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frequencyMapping: 'linear',
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synthesisMode: 'direct',
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invert: false,
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fftSize: 2048,
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frameOverlap: 0.5,
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disableNormalization: false,
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disableContrast: false,
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exactBinMapping: false,
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...params
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}
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}
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@@ -31,58 +86,100 @@ export class ImageToAudioSynthesizer {
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* Synthesize audio from image data
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*/
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synthesize(imageData: ImageData): SynthesisResult {
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const { width, height, data } = imageData
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if (this.params.synthesisMode === 'direct') {
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return this.synthesizeDirect(imageData)
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} else {
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return this.synthesizeCustom(imageData)
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}
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}
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/**
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* Custom synthesis mode - sophisticated audio processing
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*/
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private synthesizeCustom(imageData: ImageData): SynthesisResult {
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const { width, height } = imageData
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const {
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duration,
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minFreq,
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maxFreq,
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sampleRate,
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frequencyResolution,
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timeResolution,
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amplitudeThreshold,
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maxPartials,
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windowType
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windowType,
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contrast,
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spectralDensity,
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usePerceptualWeighting,
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frequencyMapping,
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invert = false
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} = this.params
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// Detect image type
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const invert = shouldInvertImage(imageData)
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// Calculate synthesis parameters
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const totalSamples = Math.floor(duration * sampleRate)
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const effectiveWidth = Math.floor(width / timeResolution)
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const effectiveWidth = width
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const effectiveHeight = Math.floor(height / frequencyResolution)
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const samplesPerColumn = totalSamples / effectiveWidth
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const audio = new Float32Array(totalSamples)
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// Pre-calculate mel-scale frequency mapping
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const minMel = hzToMel(minFreq)
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const maxMel = hzToMel(maxFreq)
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// Pre-calculate frequency mapping based on selected mode
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let minMapped: number, maxMapped: number
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if (frequencyMapping === 'mel') {
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minMapped = hzToMel(minFreq)
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maxMapped = hzToMel(maxFreq)
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} else {
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minMapped = minFreq
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maxMapped = maxFreq
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}
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// Storage for temporal smoothing
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const previousAmplitudes = new Float32Array(effectiveHeight)
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const smoothingFactor = 0.3
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const smoothingFactor = 0.2 // Reduced for sharper transients
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// Process each time slice
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for (let col = 0; col < effectiveWidth; col++) {
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const sourceCol = col * timeResolution
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const sourceCol = col
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const startSample = Math.floor(col * samplesPerColumn)
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const endSample = Math.floor((col + 1) * samplesPerColumn)
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// Extract spectrum for this time slice
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const spectrum = extractSpectrum(imageData, sourceCol, effectiveHeight, frequencyResolution, invert)
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// Extract spectrum for this time slice with improved amplitude mapping
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const spectrum = extractSpectrum(
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imageData,
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sourceCol,
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effectiveHeight,
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frequencyResolution,
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invert,
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usePerceptualWeighting || false
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)
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// Advanced mode: convert to dB scale for more accurate spectrogram interpretation
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const processedSpectrum = spectrum.map(amp => {
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const db = 20 * Math.log10(Math.max(amp, 0.001))
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const normalizedDb = Math.max(0, (db + 60) / 60)
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return normalizedDb
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})
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// Detect spectral peaks
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const peaks = detectSpectralPeaks(spectrum, Math.min(amplitudeThreshold, 0.01))
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const peaks = detectSpectralPeaks(processedSpectrum, Math.min(amplitudeThreshold, 0.01), false)
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// Generate partials from peaks
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// Generate partials from peaks with spectral density
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const partials: SpectralPeak[] = []
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for (const peakRow of peaks) {
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// Mel-scale frequency mapping (high freq at top)
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const melValue = maxMel - (peakRow / (effectiveHeight - 1)) * (maxMel - minMel)
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const frequency = melToHz(melValue)
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// Frequency mapping based on selected mode
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let frequency: number
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if (frequencyMapping === 'mel') {
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const melValue = maxMapped - (peakRow / (effectiveHeight - 1)) * (maxMapped - minMapped)
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frequency = melToHz(melValue)
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} else if (frequencyMapping === 'linear') {
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frequency = mapFrequencyLinear(peakRow, effectiveHeight, minFreq, maxFreq)
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} else {
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frequency = mapFrequency(peakRow, effectiveHeight, minFreq, maxFreq, frequencyMapping || 'mel')
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}
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let amplitude = spectrum[peakRow]
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let amplitude = processedSpectrum[peakRow]
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// Apply temporal smoothing
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if (col > 0) {
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@@ -90,14 +187,19 @@ export class ImageToAudioSynthesizer {
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}
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previousAmplitudes[peakRow] = amplitude
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// Apply perceptual weighting
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amplitude = perceptualAmplitudeWeighting(frequency, amplitude)
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// Use zero phase for simplicity
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const phase = 0
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// Apply perceptual weighting with contrast
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amplitude = perceptualAmplitudeWeighting(frequency, amplitude, contrast || 2.2)
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// Check final amplitude threshold
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if (amplitude > Math.min(amplitudeThreshold, 0.005)) {
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partials.push({ frequency, amplitude, phase })
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// Advanced mode: Generate spectral density (multiple tones per peak)
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const denseTones = generateSpectralDensity(
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frequency,
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amplitude,
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spectralDensity || 3,
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Math.max(20, frequency * 0.02)
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)
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partials.push(...denseTones)
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}
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}
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@@ -112,6 +214,7 @@ export class ImageToAudioSynthesizer {
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for (const { frequency, amplitude, phase } of limitedPartials) {
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for (let i = 0; i < chunkLength; i++) {
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const t = (startSample + i) / sampleRate
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// Use sine waves for our advanced synthesis (more flexible for complex timbres)
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audioChunk[i] += amplitude * Math.sin(2 * Math.PI * frequency * t + phase)
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}
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}
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@@ -125,18 +228,187 @@ export class ImageToAudioSynthesizer {
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}
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}
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// Normalize to prevent clipping
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let maxAmplitude = 0
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for (let i = 0; i < audio.length; i++) {
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const absValue = Math.abs(audio[i])
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if (absValue > maxAmplitude) {
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maxAmplitude = absValue
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// Griffin-Lim removed due to crashes and incomplete implementation
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// Use improved global normalization (alexadam style)
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const normalizedAudio = normalizeAudioGlobal(audio, 0.8)
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return {
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audio: normalizedAudio,
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sampleRate,
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duration
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}
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}
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/**
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* Direct synthesis mode - high fidelity spectrogram synthesis
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* Maps image pixels directly to FFT-aligned frequencies for maximum accuracy
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*/
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private synthesizeDirect(imageData: ImageData): SynthesisResult {
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const { width, height } = imageData
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const {
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duration,
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minFreq,
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maxFreq,
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sampleRate,
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fftSize = 2048,
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frameOverlap = 0.5,
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disableNormalization = false,
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disableContrast = false,
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exactBinMapping = true,
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invert = false
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} = this.params
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const totalSamples = Math.floor(duration * sampleRate)
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const audio = new Float32Array(totalSamples)
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// FFT analysis parameters - exactly matching what spectrograms use
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const hopSize = Math.floor(fftSize * (1 - frameOverlap))
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const numFrames = Math.floor((totalSamples - fftSize) / hopSize) + 1
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const nyquist = sampleRate / 2
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const binWidth = nyquist / (fftSize / 2)
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// Map image dimensions to FFT parameters
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const framesPerColumn = numFrames / width
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// Calculate exact frequency bins if using exact mapping
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let freqBins: number[]
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if (exactBinMapping) {
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freqBins = []
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for (let bin = 0; bin < fftSize / 2; bin++) {
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const freq = bin * binWidth
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if (freq >= minFreq && freq <= maxFreq) {
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freqBins.push(freq)
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}
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}
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// Map image rows to these exact bins
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console.log(`Ultra-precise mode: Using ${freqBins.length} exact FFT bins from ${minFreq}Hz to ${maxFreq}Hz`)
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} else {
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// Linear frequency mapping
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freqBins = []
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for (let row = 0; row < height; row++) {
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const freq = maxFreq - (row / (height - 1)) * (maxFreq - minFreq)
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freqBins.push(freq)
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}
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}
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if (maxAmplitude > 1) {
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// Pre-calculate optimization arrays to avoid redundant calculations
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const precomputedFreqs = new Float32Array(freqBins.length)
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for (let i = 0; i < freqBins.length; i++) {
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precomputedFreqs[i] = 2 * Math.PI * freqBins[i]
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}
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// Reusable buffers to avoid memory allocations
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let columnSpectrum = new Float32Array(fftSize) // Max possible size
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let columnAmplitudes = new Float32Array(height) // Cache amplitudes per column
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// Synthesize each frame exactly
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for (let col = 0; col < width; col++) {
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// Calculate exact frame timing
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const frameIndex = col * framesPerColumn
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const startSample = Math.floor(frameIndex * hopSize)
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const endSample = Math.min(startSample + fftSize, totalSamples)
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const frameLength = endSample - startSample
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if (frameLength <= 0) continue
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// Clear the reused buffer
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columnSpectrum.fill(0, 0, frameLength)
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// Pre-calculate intensities and amplitudes for this column to eliminate redundant calculations
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const effectiveHeight = exactBinMapping ? Math.min(height, freqBins.length) : height
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columnAmplitudes.fill(0, 0, effectiveHeight) // Clear amplitude cache
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let hasVisiblePixels = false
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for (let row = 0; row < effectiveHeight; row++) {
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const pixelIndex = (row * width + col) * 4
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const r = imageData.data[pixelIndex]
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const g = imageData.data[pixelIndex + 1]
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const b = imageData.data[pixelIndex + 2]
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// Raw pixel intensity - no perceptual weighting
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let intensity = (r + g + b) / (3 * 255)
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if (invert) intensity = 1 - intensity
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if (intensity >= 0.001) {
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// Apply contrast only if not disabled
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let amplitude: number
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if (disableContrast) {
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amplitude = intensity
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} else {
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const contrast = this.params.contrast || 1.0
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// Fast power optimization for common cases
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if (contrast === 1.0) {
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amplitude = intensity // No contrast
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} else if (contrast === 2.0) {
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amplitude = intensity * intensity // Square is much faster than Math.pow
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} else if (contrast === 0.5) {
|
||||
amplitude = Math.sqrt(intensity) // Square root is faster than Math.pow
|
||||
} else if (contrast === 3.0) {
|
||||
amplitude = intensity * intensity * intensity // Cube
|
||||
} else if (contrast === 4.0) {
|
||||
const sq = intensity * intensity
|
||||
amplitude = sq * sq // Fourth power
|
||||
} else {
|
||||
// Fast power approximation for arbitrary values
|
||||
// Uses bit manipulation + lookup for ~10x speedup over Math.pow
|
||||
amplitude = fastPower(intensity, contrast)
|
||||
}
|
||||
}
|
||||
|
||||
if (amplitude >= 0.001) {
|
||||
columnAmplitudes[row] = amplitude
|
||||
hasVisiblePixels = true
|
||||
} else {
|
||||
columnAmplitudes[row] = 0
|
||||
}
|
||||
} else {
|
||||
columnAmplitudes[row] = 0
|
||||
}
|
||||
}
|
||||
|
||||
// Skip entirely black columns
|
||||
if (!hasVisiblePixels) continue
|
||||
|
||||
// Process each frequency bin using cached amplitudes
|
||||
for (let row = 0; row < effectiveHeight; row++) {
|
||||
const amplitude = columnAmplitudes[row]
|
||||
if (amplitude < 0.001) continue
|
||||
|
||||
// Use pre-calculated frequency coefficient
|
||||
const freqCoeff = precomputedFreqs[row]
|
||||
|
||||
// Phase increment method - mathematically identical but much faster
|
||||
// Eliminates array lookups and multiplications in tight loop
|
||||
let phase = freqCoeff * startSample / sampleRate // Initial phase
|
||||
const phaseIncrement = freqCoeff / sampleRate // Phase per sample
|
||||
for (let i = 0; i < frameLength; i++) {
|
||||
columnSpectrum[i] += amplitude * Math.sin(phase)
|
||||
phase += phaseIncrement
|
||||
}
|
||||
}
|
||||
|
||||
// Add frame to audio with NO windowing (preserves exact amplitudes)
|
||||
for (let i = 0; i < frameLength; i++) {
|
||||
if (startSample + i < totalSamples) {
|
||||
audio[startSample + i] += columnSpectrum[i]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Apply normalization only if not disabled
|
||||
if (!disableNormalization) {
|
||||
let maxAmp = 0
|
||||
for (let i = 0; i < audio.length; i++) {
|
||||
audio[i] /= maxAmplitude
|
||||
const absAmp = Math.abs(audio[i])
|
||||
if (absAmp > maxAmp) maxAmp = absAmp
|
||||
}
|
||||
|
||||
if (maxAmp > 0) {
|
||||
const scale = 0.95 / maxAmp // Slightly higher than 0.8 to preserve dynamics
|
||||
for (let i = 0; i < audio.length; i++) {
|
||||
audio[i] *= scale
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -172,4 +444,84 @@ export function synthesizeFromImage(
|
||||
const synthesizer = new ImageToAudioSynthesizer(params)
|
||||
const result = synthesizer.synthesize(imageData)
|
||||
return result.audio
|
||||
}
|
||||
|
||||
/**
|
||||
* Create direct synthesis parameters for high fidelity
|
||||
*/
|
||||
export function createDirectParams(overrides: Partial<SynthesisParams> = {}): SynthesisParams {
|
||||
return {
|
||||
duration: 5,
|
||||
minFreq: 200,
|
||||
maxFreq: 20000,
|
||||
sampleRate: 44100,
|
||||
frequencyResolution: 1,
|
||||
amplitudeThreshold: 0,
|
||||
maxPartials: 0,
|
||||
windowType: 'rectangular',
|
||||
contrast: 2.2,
|
||||
spectralDensity: 0,
|
||||
usePerceptualWeighting: false,
|
||||
frequencyMapping: 'linear',
|
||||
synthesisMode: 'direct',
|
||||
invert: false,
|
||||
fftSize: 2048,
|
||||
frameOverlap: 0.75,
|
||||
disableNormalization: false,
|
||||
disableContrast: false,
|
||||
exactBinMapping: false,
|
||||
...overrides
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Create parameters for custom synthesis mode with advanced processing
|
||||
*/
|
||||
export function createCustomParams(overrides: Partial<SynthesisParams> = {}): SynthesisParams {
|
||||
return {
|
||||
duration: 5,
|
||||
minFreq: 200,
|
||||
maxFreq: 20000,
|
||||
sampleRate: 44100,
|
||||
frequencyResolution: 1,
|
||||
amplitudeThreshold: 0.01,
|
||||
maxPartials: 100,
|
||||
windowType: 'hann',
|
||||
contrast: 2.2,
|
||||
spectralDensity: 3,
|
||||
usePerceptualWeighting: true,
|
||||
frequencyMapping: 'mel',
|
||||
synthesisMode: 'custom',
|
||||
invert: false,
|
||||
fftSize: 2048,
|
||||
frameOverlap: 0.5,
|
||||
disableNormalization: false,
|
||||
disableContrast: false,
|
||||
exactBinMapping: false,
|
||||
...overrides
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Direct synthesis for high fidelity spectrogram reconstruction
|
||||
*/
|
||||
export function synthesizeDirect(
|
||||
imageData: ImageData,
|
||||
params: Partial<SynthesisParams> = {}
|
||||
): SynthesisResult {
|
||||
const directParams = createDirectParams(params)
|
||||
const synthesizer = new ImageToAudioSynthesizer(directParams)
|
||||
return synthesizer.synthesize(imageData)
|
||||
}
|
||||
|
||||
/**
|
||||
* Custom synthesis with advanced audio processing features
|
||||
*/
|
||||
export function synthesizeCustom(
|
||||
imageData: ImageData,
|
||||
params: Partial<SynthesisParams> = {}
|
||||
): SynthesisResult {
|
||||
const customParams = createCustomParams(params)
|
||||
const synthesizer = new ImageToAudioSynthesizer(customParams)
|
||||
return synthesizer.synthesize(imageData)
|
||||
}
|
||||
@@ -6,10 +6,20 @@ export interface SynthesisParams {
|
||||
maxFreq: number
|
||||
sampleRate: number
|
||||
frequencyResolution: number
|
||||
timeResolution: number
|
||||
amplitudeThreshold: number
|
||||
maxPartials: number
|
||||
windowType: WindowType
|
||||
contrast?: number
|
||||
spectralDensity?: number
|
||||
usePerceptualWeighting?: boolean
|
||||
frequencyMapping?: 'mel' | 'linear' | 'bark' | 'log'
|
||||
synthesisMode?: 'direct' | 'custom'
|
||||
invert?: boolean
|
||||
fftSize?: number
|
||||
frameOverlap?: number
|
||||
disableNormalization?: boolean
|
||||
disableContrast?: boolean
|
||||
exactBinMapping?: boolean
|
||||
}
|
||||
|
||||
export interface SpectralPeak {
|
||||
|
||||
@@ -13,9 +13,107 @@ export function melToHz(mel: number): number {
|
||||
}
|
||||
|
||||
/**
|
||||
* Detect spectral peaks in amplitude spectrum
|
||||
* Convert frequency from Hz to Bark scale
|
||||
*/
|
||||
export function detectSpectralPeaks(spectrum: number[], threshold: number = 0.01): number[] {
|
||||
export function hzToBark(freq: number): number {
|
||||
return 13 * Math.atan(0.00076 * freq) + 3.5 * Math.atan(Math.pow(freq / 7500, 2))
|
||||
}
|
||||
|
||||
/**
|
||||
* Convert frequency from Bark scale to Hz
|
||||
*/
|
||||
export function barkToHz(bark: number): number {
|
||||
// Approximate inverse using Newton's method for better accuracy
|
||||
let freq = 1000 // Initial guess
|
||||
for (let i = 0; i < 10; i++) {
|
||||
const barkEst = hzToBark(freq)
|
||||
const derivative = 13 * 0.00076 / (1 + Math.pow(0.00076 * freq, 2)) +
|
||||
3.5 * 2 * (freq / 7500) * (1 / 7500) / (1 + Math.pow(freq / 7500, 4))
|
||||
freq = freq - (barkEst - bark) / derivative
|
||||
if (Math.abs(hzToBark(freq) - bark) < 0.001) break
|
||||
}
|
||||
return Math.max(20, Math.min(20000, freq))
|
||||
}
|
||||
|
||||
/**
|
||||
* Apply amplitude curve transformation
|
||||
*/
|
||||
export function applyAmplitudeCurve(amplitude: number, curve: string, gamma: number = 2.2): number {
|
||||
amplitude = Math.max(0, Math.min(1, amplitude))
|
||||
|
||||
switch (curve) {
|
||||
case 'linear':
|
||||
return amplitude
|
||||
case 'logarithmic':
|
||||
return amplitude === 0 ? 0 : Math.log10(1 + amplitude * 9) / Math.log10(10)
|
||||
case 'power':
|
||||
return Math.pow(amplitude, gamma)
|
||||
case 'sqrt':
|
||||
return Math.sqrt(amplitude)
|
||||
default:
|
||||
return amplitude
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Apply soft thresholding using tanh function
|
||||
*/
|
||||
export function applySoftThreshold(amplitude: number, threshold: number, softness: number = 0.1): number {
|
||||
if (threshold <= 0) return amplitude
|
||||
|
||||
const ratio = amplitude / threshold
|
||||
if (ratio < 0.5) {
|
||||
return 0
|
||||
} else if (ratio > 2.0) {
|
||||
return amplitude
|
||||
} else {
|
||||
// Smooth transition using tanh
|
||||
const transition = Math.tanh((ratio - 1) / softness)
|
||||
return amplitude * (0.5 + 0.5 * transition)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Map frequency using specified scale
|
||||
*/
|
||||
export function mapFrequency(row: number, totalRows: number, minFreq: number, maxFreq: number, scale: string): number {
|
||||
const normalizedRow = row / (totalRows - 1)
|
||||
|
||||
switch (scale) {
|
||||
case 'mel':
|
||||
const minMel = hzToMel(minFreq)
|
||||
const maxMel = hzToMel(maxFreq)
|
||||
const melValue = maxMel - normalizedRow * (maxMel - minMel)
|
||||
return melToHz(melValue)
|
||||
|
||||
case 'bark':
|
||||
const minBark = hzToBark(minFreq)
|
||||
const maxBark = hzToBark(maxFreq)
|
||||
const barkValue = maxBark - normalizedRow * (maxBark - minBark)
|
||||
return barkToHz(barkValue)
|
||||
|
||||
case 'linear':
|
||||
return maxFreq - normalizedRow * (maxFreq - minFreq)
|
||||
|
||||
case 'log':
|
||||
const logMin = Math.log10(minFreq)
|
||||
const logMax = Math.log10(maxFreq)
|
||||
const logValue = logMax - normalizedRow * (logMax - logMin)
|
||||
return Math.pow(10, logValue)
|
||||
|
||||
default:
|
||||
return maxFreq - normalizedRow * (maxFreq - minFreq)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Detect spectral peaks in amplitude spectrum with optional smoothing
|
||||
*/
|
||||
export function detectSpectralPeaks(spectrum: number[], threshold: number = 0.01, useSmoothing: boolean = false): number[] {
|
||||
if (useSmoothing) {
|
||||
return detectSmoothSpectralPeaks(spectrum, threshold)
|
||||
}
|
||||
|
||||
const peaks: number[] = []
|
||||
|
||||
// Find significant components above threshold
|
||||
@@ -40,29 +138,223 @@ export function detectSpectralPeaks(spectrum: number[], threshold: number = 0.01
|
||||
}
|
||||
|
||||
/**
|
||||
* Apply perceptual amplitude weighting
|
||||
* Detect spectral peaks with local maxima and smoothing
|
||||
*/
|
||||
export function perceptualAmplitudeWeighting(freq: number, amplitude: number): number {
|
||||
export function detectSmoothSpectralPeaks(spectrum: number[], threshold: number = 0.01): number[] {
|
||||
const smoothedSpectrum = smoothSpectrum(spectrum, 2)
|
||||
const peaks: number[] = []
|
||||
|
||||
// Find local maxima in smoothed spectrum
|
||||
for (let i = 2; i < smoothedSpectrum.length - 2; i++) {
|
||||
const current = smoothedSpectrum[i]
|
||||
|
||||
if (current > threshold &&
|
||||
current > smoothedSpectrum[i - 1] &&
|
||||
current > smoothedSpectrum[i + 1] &&
|
||||
current > smoothedSpectrum[i - 2] &&
|
||||
current > smoothedSpectrum[i + 2]) {
|
||||
|
||||
// Find the exact peak position with sub-bin accuracy using parabolic interpolation
|
||||
const y1 = smoothedSpectrum[i - 1]
|
||||
const y2 = smoothedSpectrum[i]
|
||||
const y3 = smoothedSpectrum[i + 1]
|
||||
|
||||
const a = (y1 - 2 * y2 + y3) / 2
|
||||
const b = (y3 - y1) / 2
|
||||
|
||||
let peakOffset = 0
|
||||
if (Math.abs(a) > 1e-10) {
|
||||
peakOffset = -b / (2 * a)
|
||||
peakOffset = Math.max(-0.5, Math.min(0.5, peakOffset))
|
||||
}
|
||||
|
||||
const exactPeak = i + peakOffset
|
||||
if (exactPeak >= 0 && exactPeak < spectrum.length) {
|
||||
peaks.push(Math.round(exactPeak))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Fallback: use simple threshold detection if no peaks found
|
||||
if (peaks.length === 0) {
|
||||
for (let i = 0; i < spectrum.length; i++) {
|
||||
if (spectrum[i] > threshold) {
|
||||
peaks.push(i)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Remove duplicates and sort
|
||||
return [...new Set(peaks)].sort((a, b) => a - b)
|
||||
}
|
||||
|
||||
/**
|
||||
* Smooth spectrum using moving average
|
||||
*/
|
||||
function smoothSpectrum(spectrum: number[], windowSize: number): number[] {
|
||||
const smoothed = new Float32Array(spectrum.length)
|
||||
const halfWindow = Math.floor(windowSize / 2)
|
||||
|
||||
for (let i = 0; i < spectrum.length; i++) {
|
||||
let sum = 0
|
||||
let count = 0
|
||||
|
||||
for (let j = Math.max(0, i - halfWindow); j <= Math.min(spectrum.length - 1, i + halfWindow); j++) {
|
||||
sum += spectrum[j]
|
||||
count++
|
||||
}
|
||||
|
||||
smoothed[i] = sum / count
|
||||
}
|
||||
|
||||
return Array.from(smoothed)
|
||||
}
|
||||
|
||||
/**
|
||||
* Apply perceptual amplitude weighting with contrast control
|
||||
*/
|
||||
export function perceptualAmplitudeWeighting(freq: number, amplitude: number, contrast: number = 2.2): number {
|
||||
// Apply contrast curve first (like LeviBorodenko's approach)
|
||||
const contrastedAmplitude = Math.pow(amplitude, contrast)
|
||||
|
||||
// Gentle boost around 1kHz for perceptual accuracy
|
||||
const normalizedFreq = Math.log10(freq / 1000)
|
||||
const weight = Math.exp(-normalizedFreq * normalizedFreq * 0.5) * 0.5 + 0.5
|
||||
return amplitude * weight
|
||||
return contrastedAmplitude * weight
|
||||
}
|
||||
|
||||
|
||||
|
||||
/**
|
||||
* Generate spectral density by creating multiple tones per frequency bin
|
||||
* Inspired by LeviBorodenko's multi-tone approach
|
||||
*/
|
||||
export function generateSpectralDensity(
|
||||
centerFreq: number,
|
||||
amplitude: number,
|
||||
numTones: number = 3,
|
||||
bandwidth: number = 50
|
||||
): Array<{ frequency: number; amplitude: number; phase: number }> {
|
||||
const peaks: Array<{ frequency: number; amplitude: number; phase: number }> = []
|
||||
const toneSpacing = bandwidth / numTones
|
||||
|
||||
for (let i = 0; i < numTones; i++) {
|
||||
const freq = centerFreq + (i - numTones/2) * toneSpacing
|
||||
const toneAmplitude = amplitude * (1 - Math.abs(i - numTones/2) / numTones * 0.3) // Slight amplitude variation
|
||||
|
||||
peaks.push({
|
||||
frequency: freq,
|
||||
amplitude: toneAmplitude,
|
||||
phase: 0
|
||||
})
|
||||
}
|
||||
|
||||
return peaks
|
||||
}
|
||||
|
||||
/**
|
||||
* Auto-detect if image colors should be inverted
|
||||
* Enhanced detection with edge analysis and histogram consideration
|
||||
*/
|
||||
export function shouldInvertImage(imageData: ImageData): boolean {
|
||||
const { width, height, data } = imageData
|
||||
let totalBrightness = 0
|
||||
let edgePixels = 0
|
||||
let edgeBrightness = 0
|
||||
|
||||
for (let i = 0; i < data.length; i += 4) {
|
||||
const gray = 0.299 * data[i] + 0.587 * data[i + 1] + 0.114 * data[i + 2]
|
||||
totalBrightness += gray / 255
|
||||
// Sample edge pixels (first/last rows and columns)
|
||||
for (let y = 0; y < height; y++) {
|
||||
for (let x = 0; x < width; x++) {
|
||||
const idx = (y * width + x) * 4
|
||||
const gray = 0.299 * data[idx] + 0.587 * data[idx + 1] + 0.114 * data[idx + 2]
|
||||
const brightness = gray / 255
|
||||
|
||||
totalBrightness += brightness
|
||||
|
||||
// Check if pixel is on edge
|
||||
if (y === 0 || y === height - 1 || x === 0 || x === width - 1) {
|
||||
edgeBrightness += brightness
|
||||
edgePixels++
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
const meanBrightness = totalBrightness / (width * height)
|
||||
return meanBrightness > 0.5 // Bright background = diagram
|
||||
const meanEdgeBrightness = edgePixels > 0 ? edgeBrightness / edgePixels : meanBrightness
|
||||
|
||||
// If edges are significantly brighter than average, likely a diagram with bright background
|
||||
const edgeWeight = Math.abs(meanEdgeBrightness - meanBrightness) > 0.2 ? 0.7 : 0.3
|
||||
const finalBrightness = edgeWeight * meanEdgeBrightness + (1 - edgeWeight) * meanBrightness
|
||||
|
||||
return finalBrightness > 0.5
|
||||
}
|
||||
|
||||
/**
|
||||
* Analyze image brightness distribution
|
||||
*/
|
||||
export function analyzeImageBrightness(imageData: ImageData): {
|
||||
meanBrightness: number
|
||||
medianBrightness: number
|
||||
edgeBrightness: number
|
||||
contrast: number
|
||||
recommendation: 'invert' | 'normal' | 'ambiguous'
|
||||
} {
|
||||
const { width, height, data } = imageData
|
||||
const brightnesses: number[] = []
|
||||
let edgeBrightness = 0
|
||||
let edgePixels = 0
|
||||
|
||||
// Collect all brightness values
|
||||
for (let i = 0; i < data.length; i += 4) {
|
||||
const gray = 0.299 * data[i] + 0.587 * data[i + 1] + 0.114 * data[i + 2]
|
||||
const brightness = gray / 255
|
||||
brightnesses.push(brightness)
|
||||
|
||||
// Check if pixel is on edge
|
||||
const pixelIndex = i / 4
|
||||
const y = Math.floor(pixelIndex / width)
|
||||
const x = pixelIndex % width
|
||||
if (y === 0 || y === height - 1 || x === 0 || x === width - 1) {
|
||||
edgeBrightness += brightness
|
||||
edgePixels++
|
||||
}
|
||||
}
|
||||
|
||||
// Sort for median
|
||||
brightnesses.sort((a, b) => a - b)
|
||||
|
||||
const meanBrightness = brightnesses.reduce((sum, b) => sum + b, 0) / brightnesses.length
|
||||
const medianBrightness = brightnesses[Math.floor(brightnesses.length / 2)]
|
||||
const avgEdgeBrightness = edgePixels > 0 ? edgeBrightness / edgePixels : meanBrightness
|
||||
|
||||
// Calculate contrast (standard deviation)
|
||||
const variance = brightnesses.reduce((sum, b) => sum + Math.pow(b - meanBrightness, 2), 0) / brightnesses.length
|
||||
const contrast = Math.sqrt(variance)
|
||||
|
||||
// Make recommendation
|
||||
let recommendation: 'invert' | 'normal' | 'ambiguous'
|
||||
if (meanBrightness > 0.7 && avgEdgeBrightness > 0.6) {
|
||||
recommendation = 'invert'
|
||||
} else if (meanBrightness < 0.3 && avgEdgeBrightness < 0.4) {
|
||||
recommendation = 'normal'
|
||||
} else {
|
||||
recommendation = 'ambiguous'
|
||||
}
|
||||
|
||||
return {
|
||||
meanBrightness,
|
||||
medianBrightness,
|
||||
edgeBrightness: avgEdgeBrightness,
|
||||
contrast,
|
||||
recommendation
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Force invert image colors for synthesis
|
||||
*/
|
||||
export function forceInvertSpectrum(spectrum: number[]): number[] {
|
||||
return spectrum.map(amp => 1 - amp)
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -115,14 +407,16 @@ export function applyWindow(audioChunk: Float32Array, windowType: string): Float
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract grayscale spectrum from image column
|
||||
* Extract grayscale spectrum from image column with improved amplitude mapping
|
||||
* Incorporates alexadam's perceptual weighting approach
|
||||
*/
|
||||
export function extractSpectrum(
|
||||
imageData: ImageData,
|
||||
col: number,
|
||||
height: number,
|
||||
frequencyResolution: number,
|
||||
invert: boolean
|
||||
invert: boolean,
|
||||
usePerceptualWeighting: boolean = true
|
||||
): number[] {
|
||||
const { width, data } = imageData
|
||||
const spectrum: number[] = []
|
||||
@@ -134,10 +428,57 @@ export function extractSpectrum(
|
||||
const g = data[idx + 1]
|
||||
const b = data[idx + 2]
|
||||
|
||||
let amplitude = (0.299 * r + 0.587 * g + 0.114 * b) / 255
|
||||
let amplitude: number
|
||||
|
||||
if (usePerceptualWeighting) {
|
||||
// Use alexadam's approach: sum RGB and square for perceptual weighting
|
||||
const rgbSum = r + g + b
|
||||
amplitude = Math.pow(rgbSum / 765, 2) // 765 = 255 * 3 (max RGB sum)
|
||||
} else {
|
||||
// Original luminance-based approach
|
||||
amplitude = (0.299 * r + 0.587 * g + 0.114 * b) / 255
|
||||
}
|
||||
|
||||
if (invert) amplitude = 1 - amplitude
|
||||
|
||||
spectrum.push(amplitude)
|
||||
}
|
||||
|
||||
return spectrum
|
||||
}
|
||||
|
||||
/**
|
||||
* Alternative linear frequency mapping inspired by alexadam's approach
|
||||
*/
|
||||
export function mapFrequencyLinear(row: number, totalRows: number, minFreq: number, maxFreq: number): number {
|
||||
// Direct linear mapping from top to bottom (high freq at top)
|
||||
const normalizedRow = row / (totalRows - 1)
|
||||
return maxFreq - normalizedRow * (maxFreq - minFreq)
|
||||
}
|
||||
|
||||
/**
|
||||
* Improved normalization strategy - find global maximum first
|
||||
*/
|
||||
export function normalizeAudioGlobal(audio: Float32Array, targetLevel: number = 0.8): Float32Array {
|
||||
// Find global maximum
|
||||
let maxAmplitude = 0
|
||||
for (let i = 0; i < audio.length; i++) {
|
||||
const absValue = Math.abs(audio[i])
|
||||
if (absValue > maxAmplitude) {
|
||||
maxAmplitude = absValue
|
||||
}
|
||||
}
|
||||
|
||||
// Apply normalization
|
||||
const normalized = new Float32Array(audio.length)
|
||||
if (maxAmplitude > 0) {
|
||||
const normalizeGain = targetLevel / maxAmplitude
|
||||
for (let i = 0; i < audio.length; i++) {
|
||||
normalized[i] = audio[i] * normalizeGain
|
||||
}
|
||||
} else {
|
||||
normalized.set(audio)
|
||||
}
|
||||
|
||||
return normalized
|
||||
}
|
||||
@@ -1,5 +1,12 @@
|
||||
// Core synthesis
|
||||
export { ImageToAudioSynthesizer, synthesizeFromImage } from './core/synthesizer'
|
||||
export {
|
||||
ImageToAudioSynthesizer,
|
||||
synthesizeFromImage,
|
||||
createDirectParams,
|
||||
createCustomParams,
|
||||
synthesizeDirect,
|
||||
synthesizeCustom
|
||||
} from './core/synthesizer'
|
||||
export type { SynthesisParams, SpectralPeak, SynthesisResult, WindowType } from './core/types'
|
||||
|
||||
// Utilities
|
||||
@@ -9,14 +16,23 @@ export {
|
||||
detectSpectralPeaks,
|
||||
perceptualAmplitudeWeighting,
|
||||
shouldInvertImage,
|
||||
analyzeImageBrightness,
|
||||
forceInvertSpectrum,
|
||||
extractSpectrum,
|
||||
generateWindow,
|
||||
applyWindow
|
||||
applyWindow,
|
||||
applySoftThreshold,
|
||||
mapFrequency,
|
||||
mapFrequencyLinear,
|
||||
normalizeAudioGlobal,
|
||||
generateSpectralDensity
|
||||
} from './core/utils'
|
||||
|
||||
// Audio export
|
||||
export {
|
||||
createWAVBuffer,
|
||||
downloadWAV,
|
||||
playAudio
|
||||
} from './audio/export'
|
||||
playAudio,
|
||||
createAudioPlayer
|
||||
} from './audio/export'
|
||||
export type { AudioPlayer } from './audio/export'
|
||||
Reference in New Issue
Block a user