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npx skills add Bbeierle12/Skill-MCP-Claude --skill "audio-analysis"
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# Description
Audio analysis with Tone.js and Web Audio API including FFT, frequency data extraction, amplitude measurement, and waveform analysis. Use when extracting audio data for visualizations, beat detection, or any audio-reactive features.
# SKILL.md
name: audio-analysis
description: Audio analysis with Tone.js and Web Audio API including FFT, frequency data extraction, amplitude measurement, and waveform analysis. Use when extracting audio data for visualizations, beat detection, or any audio-reactive features.
Audio Analysis
FFT, frequency extraction, and audio data analysis.
Quick Start
import * as Tone from 'tone';
// Create analyzer
const analyser = new Tone.Analyser('fft', 256);
const player = new Tone.Player('/audio/music.mp3');
player.connect(analyser);
player.toDestination();
// Get frequency data
const frequencyData = analyser.getValue(); // Float32Array
Analyzer Types
FFT Analyzer
// FFT (Fast Fourier Transform) - frequency spectrum
const fftAnalyser = new Tone.Analyser({
type: 'fft',
size: 256, // Must be power of 2: 32, 64, 128, 256, 512, 1024, 2048
smoothing: 0.8 // 0-1, higher = smoother transitions
});
// Returns Float32Array of dB values (typically -100 to 0)
const fftData = fftAnalyser.getValue();
Waveform Analyzer
// Waveform - time domain data
const waveformAnalyser = new Tone.Analyser({
type: 'waveform',
size: 1024
});
// Returns Float32Array of amplitude values (-1 to 1)
const waveformData = waveformAnalyser.getValue();
Meter (Volume Level)
// Meter - overall volume level
const meter = new Tone.Meter({
smoothing: 0.9,
normalRange: false // true for 0-1, false for dB
});
player.connect(meter);
// Get current level
const level = meter.getValue(); // dB or 0-1
FFT Size Impact
| Size | Frequency Resolution | Time Resolution | Use Case |
|---|---|---|---|
| 32 | Low | High | Beat detection |
| 128 | Medium | Medium | General visualization |
| 256 | Good | Good | Balanced (default) |
| 1024 | High | Low | Detailed spectrum |
| 2048 | Very High | Very Low | Audio analysis tools |
Frequency Bands
Manual Band Extraction
const analyser = new Tone.Analyser('fft', 256);
function getFrequencyBands() {
const data = analyser.getValue();
const binCount = data.length;
// Define frequency band ranges (approximate for 44.1kHz sample rate)
// Each bin = (sampleRate / 2) / binCount Hz
const bands = {
sub: average(data, 0, Math.floor(binCount * 0.03)), // ~20-60 Hz
bass: average(data, Math.floor(binCount * 0.03), Math.floor(binCount * 0.08)), // ~60-250 Hz
lowMid: average(data, Math.floor(binCount * 0.08), Math.floor(binCount * 0.15)), // ~250-500 Hz
mid: average(data, Math.floor(binCount * 0.15), Math.floor(binCount * 0.3)), // ~500-2000 Hz
highMid: average(data, Math.floor(binCount * 0.3), Math.floor(binCount * 0.5)), // ~2000-4000 Hz
high: average(data, Math.floor(binCount * 0.5), binCount) // ~4000+ Hz
};
return bands;
}
function average(data, start, end) {
let sum = 0;
for (let i = start; i < end; i++) {
sum += data[i];
}
return sum / (end - start);
}
Normalized Band Values
function getNormalizedBands() {
const bands = getFrequencyBands();
// Convert dB to 0-1 range (assuming -100 to 0 dB range)
const normalize = (db) => Math.max(0, Math.min(1, (db + 100) / 100));
return {
sub: normalize(bands.sub),
bass: normalize(bands.bass),
lowMid: normalize(bands.lowMid),
mid: normalize(bands.mid),
highMid: normalize(bands.highMid),
high: normalize(bands.high)
};
}
Beat Detection
Simple Peak Detection
class BeatDetector {
constructor(threshold = 0.7, decay = 0.98) {
this.threshold = threshold;
this.decay = decay;
this.peak = 0;
this.lastBeat = 0;
this.minInterval = 200; // Minimum ms between beats
}
detect(analyser) {
const data = analyser.getValue();
// Focus on bass frequencies for beat detection
const bassEnergy = this.getBassEnergy(data);
// Decay the peak
this.peak *= this.decay;
// Update peak if higher
if (bassEnergy > this.peak) {
this.peak = bassEnergy;
}
// Detect beat
const now = Date.now();
const threshold = this.peak * this.threshold;
if (bassEnergy > threshold && now - this.lastBeat > this.minInterval) {
this.lastBeat = now;
return true;
}
return false;
}
getBassEnergy(data) {
// Average of low frequency bins
let sum = 0;
const bassRange = Math.floor(data.length * 0.1);
for (let i = 0; i < bassRange; i++) {
// Convert dB to linear and sum
sum += Math.pow(10, data[i] / 20);
}
return sum / bassRange;
}
}
// Usage
const beatDetector = new BeatDetector();
const analyser = new Tone.Analyser('fft', 256);
function update() {
if (beatDetector.detect(analyser)) {
console.log('Beat!');
// Trigger visual effect
}
requestAnimationFrame(update);
}
Energy History Beat Detection
class EnergyBeatDetector {
constructor(historySize = 43, sensitivity = 1.3) {
this.history = new Array(historySize).fill(0);
this.sensitivity = sensitivity;
this.historyIndex = 0;
}
detect(analyser) {
const data = analyser.getValue();
const currentEnergy = this.calculateEnergy(data);
// Calculate average energy from history
const avgEnergy = this.history.reduce((a, b) => a + b) / this.history.length;
// Update history
this.history[this.historyIndex] = currentEnergy;
this.historyIndex = (this.historyIndex + 1) % this.history.length;
// Beat if current energy exceeds average by sensitivity factor
return currentEnergy > avgEnergy * this.sensitivity;
}
calculateEnergy(data) {
let energy = 0;
for (let i = 0; i < data.length; i++) {
const amplitude = Math.pow(10, data[i] / 20);
energy += amplitude * amplitude;
}
return energy;
}
}
Amplitude Analysis
RMS (Root Mean Square)
function getRMS(analyser) {
const waveform = analyser.getValue(); // Waveform analyzer
let sum = 0;
for (let i = 0; i < waveform.length; i++) {
sum += waveform[i] * waveform[i];
}
return Math.sqrt(sum / waveform.length);
}
Peak Amplitude
function getPeakAmplitude(analyser) {
const waveform = analyser.getValue();
let peak = 0;
for (let i = 0; i < waveform.length; i++) {
const abs = Math.abs(waveform[i]);
if (abs > peak) peak = abs;
}
return peak;
}
Smoothing Techniques
Exponential Smoothing
class SmoothValue {
constructor(smoothing = 0.9) {
this.value = 0;
this.smoothing = smoothing;
}
update(newValue) {
this.value = this.smoothing * this.value + (1 - this.smoothing) * newValue;
return this.value;
}
}
// Usage
const smoothBass = new SmoothValue(0.85);
const bassLevel = smoothBass.update(rawBassLevel);
Moving Average
class MovingAverage {
constructor(size = 10) {
this.size = size;
this.values = [];
}
update(value) {
this.values.push(value);
if (this.values.length > this.size) {
this.values.shift();
}
return this.values.reduce((a, b) => a + b) / this.values.length;
}
}
Complete Analysis System
class AudioAnalysisSystem {
constructor() {
this.fftAnalyser = new Tone.Analyser('fft', 256);
this.waveformAnalyser = new Tone.Analyser('waveform', 1024);
this.meter = new Tone.Meter({ smoothing: 0.9 });
this.smoothers = {
bass: new SmoothValue(0.85),
mid: new SmoothValue(0.9),
high: new SmoothValue(0.9),
volume: new SmoothValue(0.95)
};
this.beatDetector = new BeatDetector();
}
connect(source) {
source.connect(this.fftAnalyser);
source.connect(this.waveformAnalyser);
source.connect(this.meter);
source.toDestination();
}
getAnalysis() {
const fft = this.fftAnalyser.getValue();
const waveform = this.waveformAnalyser.getValue();
const volume = this.meter.getValue();
const bands = this.extractBands(fft);
return {
// Raw data
fft,
waveform,
// Smoothed bands (0-1)
bass: this.smoothers.bass.update(bands.bass),
mid: this.smoothers.mid.update(bands.mid),
high: this.smoothers.high.update(bands.high),
// Volume
volume: this.smoothers.volume.update(this.normalizeDb(volume)),
volumeDb: volume,
// Beat
isBeat: this.beatDetector.detect(this.fftAnalyser),
// Waveform metrics
rms: this.getRMS(waveform),
peak: this.getPeak(waveform)
};
}
extractBands(fft) {
const len = fft.length;
return {
bass: this.normalizeDb(this.avgRange(fft, 0, len * 0.1)),
mid: this.normalizeDb(this.avgRange(fft, len * 0.1, len * 0.5)),
high: this.normalizeDb(this.avgRange(fft, len * 0.5, len))
};
}
avgRange(data, start, end) {
let sum = 0;
const s = Math.floor(start);
const e = Math.floor(end);
for (let i = s; i < e; i++) sum += data[i];
return sum / (e - s);
}
normalizeDb(db) {
return Math.max(0, Math.min(1, (db + 100) / 100));
}
getRMS(waveform) {
let sum = 0;
for (let i = 0; i < waveform.length; i++) {
sum += waveform[i] * waveform[i];
}
return Math.sqrt(sum / waveform.length);
}
getPeak(waveform) {
let peak = 0;
for (let i = 0; i < waveform.length; i++) {
const abs = Math.abs(waveform[i]);
if (abs > peak) peak = abs;
}
return peak;
}
dispose() {
this.fftAnalyser.dispose();
this.waveformAnalyser.dispose();
this.meter.dispose();
}
}
Temporal Collapse Usage
class TemporalAudioAnalysis extends AudioAnalysisSystem {
getCountdownData() {
const analysis = this.getAnalysis();
return {
// For bloom intensity
glowIntensity: analysis.bass * 0.5 + analysis.volume * 0.5,
// For particle speed
particleEnergy: analysis.mid,
// For chromatic aberration
distortion: analysis.high * 0.3,
// For digit pulse
pulse: analysis.isBeat ? 1 : 0,
// For background intensity
ambientLevel: analysis.rms
};
}
}
Performance Tips
// 1. Use appropriate FFT size
const analyser = new Tone.Analyser('fft', 128); // Smaller = faster
// 2. Don't analyze every frame if not needed
let frameCount = 0;
function update() {
if (frameCount % 2 === 0) { // Every other frame
const data = analyser.getValue();
}
frameCount++;
}
// 3. Reuse arrays
const dataArray = new Float32Array(256);
analyser.getValue(dataArray); // Pass in array to avoid allocation
// 4. Use smoothing to reduce visual jitter
const smoothedValue = smoother.update(rawValue);
Reference
- See
audio-playbackfor loading and playing audio - See
audio-reactivefor connecting analysis to visuals - See
audio-routerfor audio domain routing
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.