---
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

```javascript
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

```javascript
// 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

```javascript
// 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)

```javascript
// 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

```javascript
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

```javascript
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

```javascript
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

```javascript
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)

```javascript
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

```javascript
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

```javascript
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

```javascript
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

```javascript
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

```javascript
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

```javascript
// 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-playback` for loading and playing audio
- See `audio-reactive` for connecting analysis to visuals
- See `audio-router` for audio domain routing


---
Source attribution: Bbeierle12
Original project: https://github.com/Bbeierle12/Skill-MCP-Claude/blob/9f228393b17de9671959227d5fdaa43f147f0cb2/skills/audio-analysis/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Dave AI.

## Upstream license notice

MIT License

Copyright (c) 2025 Bbeierle12

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Dave AI resource metadata

Creator / publisher: Bbeierle12
Original project: https://github.com/Bbeierle12/Skill-MCP-Claude/blob/9f228393b17de9671959227d5fdaa43f147f0cb2/skills/audio-analysis/SKILL.md
Attribution basis: Recovered original package and original project license at a pinned revision

Fields of work: Data & Analytics, Development & Engineering, Product Management
Roles: Backend Developer, Data Scientist, Frontend Developer, Mobile Developer, Product Manager
Specialties: Backend, Frontend
Tools: Web Audio Api, Tone.Js
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: cd6ba6f3a3b01e9247c612e599e051b4b96d39e99efa47eb8ecd41a93638dd6b
Metadata reviewed: 2026-10-27
This metadata supersedes earlier Dave AI catalog attribution. Original instruction and supporting files are preserved.