// Comprehensive audio feature extraction for music AI
class AudioFeatureExtractor {
constructor(audioContext, fftSize = 2048) {
this.audioContext = audioContext;
this.fftSize = fftSize;
this.sampleRate = audioContext.sampleRate;
// Initialize analysis nodes
this.analyzer = audioContext.createAnalyser();
this.analyzer.fftSize = fftSize;
this.analyzer.smoothingTimeConstant = 0.3;
// Feature extraction buffers
this.frequencyBuffer = new Uint8Array(this.analyzer.frequencyBinCount);
this.timeBuffer = new Uint8Array(this.analyzer.fftSize);
this.floatFrequencyBuffer = new Float32Array(this.analyzer.frequencyBinCount);
// Feature computation utilities
this.melFilterBank = this.createMelFilterBank();
this.chromaFilterBank = this.createChromaFilterBank();
this.windowFunction = this.createWindowFunction();
// Feature history for temporal analysis
this.featureHistory = {
mfcc: [],
chroma: [],
spectralCentroid: [],
spectralRolloff: [],
zeroCrossingRate: []
};
this.historyLength = 100; // Number of frames to remember
}
// Connect audio source for analysis
connectSource(audioNode) {
audioNode.connect(this.analyzer);
return this.analyzer; // Return for further chaining
}
// Extract comprehensive feature set
extractFeatures() {
// Update frequency and time domain data
this.analyzer.getByteFrequencyData(this.frequencyBuffer);
this.analyzer.getByteTimeDomainData(this.timeBuffer);
this.analyzer.getFloatFrequencyData(this.floatFrequencyBuffer);
const features = {
// Spectral features
spectralCentroid: this.calculateSpectralCentroid(),
spectralBandwidth: this.calculateSpectralBandwidth(),
spectralRolloff: this.calculateSpectralRolloff(),
spectralFlatness: this.calculateSpectralFlatness(),
spectralContrast: this.calculateSpectralContrast(),
// Mel-frequency cepstral coefficients
mfcc: this.calculateMFCC(),
// Harmonic and tonal features
chroma: this.calculateChroma(),
tonnetz: this.calculateTonnetz(),
// Temporal features
zeroCrossingRate: this.calculateZeroCrossingRate(),
tempo: this.estimateTempo(),
// Energy and dynamics
rms: this.calculateRMS(),
spectralEnergy: this.calculateSpectralEnergy(),
// Raw data for neural networks
melSpectrogram: this.calculateMelSpectrogram(),
rawSpectrum: Array.from(this.floatFrequencyBuffer)
};
// Update feature history
this.updateFeatureHistory(features);
return features;
}
calculateSpectralCentroid() {
let numerator = 0;
let denominator = 0;
for (let i = 0; i < this.frequencyBuffer.length; i++) {
const frequency = (i * this.sampleRate) / (2 * this.frequencyBuffer.length);
const magnitude = this.frequencyBuffer[i] / 255.0;
numerator += frequency * magnitude;
denominator += magnitude;
}
return denominator > 0 ? numerator / denominator : 0;
}
calculateSpectralBandwidth() {
const centroid = this.calculateSpectralCentroid();
let numerator = 0;
let denominator = 0;
for (let i = 0; i < this.frequencyBuffer.length; i++) {
const frequency = (i * this.sampleRate) / (2 * this.frequencyBuffer.length);
const magnitude = this.frequencyBuffer[i] / 255.0;
const diff = frequency - centroid;
numerator += Math.pow(diff, 2) * magnitude;
denominator += magnitude;
}
return denominator > 0 ? Math.sqrt(numerator / denominator) : 0;
}
calculateSpectralRolloff(threshold = 0.85) {
const totalEnergy = this.frequencyBuffer.reduce((sum, val) => sum + val, 0);
const rolloffEnergy = totalEnergy * threshold;
let cumulativeEnergy = 0;
for (let i = 0; i < this.frequencyBuffer.length; i++) {
cumulativeEnergy += this.frequencyBuffer[i];
if (cumulativeEnergy >= rolloffEnergy) {
return (i * this.sampleRate) / (2 * this.frequencyBuffer.length);
}
}
return this.sampleRate / 2; // Nyquist frequency
}
calculateSpectralFlatness() {
// Geometric mean / Arithmetic mean
let geometricMean = 1;
let arithmeticMean = 0;
let validBins = 0;
for (let i = 1; i < this.frequencyBuffer.length; i++) { // Skip DC bin
const magnitude = this.frequencyBuffer[i] / 255.0;
if (magnitude > 0) {
geometricMean *= Math.pow(magnitude, 1 / this.frequencyBuffer.length);
arithmeticMean += magnitude;
validBins++;
}
}
arithmeticMean /= validBins;
return arithmeticMean > 0 ? geometricMean / arithmeticMean : 0;
}
calculateMFCC(numCoefficients = 13) {
// Apply mel filter bank to get mel spectrum
const melSpectrum = this.applyMelFilterBank(this.floatFrequencyBuffer);
// Convert to log scale
const logMelSpectrum = melSpectrum.map(val => Math.log(Math.max(val, 1e-10)));
// Apply DCT to get MFCCs
return this.discreteCosineTransform(logMelSpectrum, numCoefficients);
}
createMelFilterBank(numFilters = 26) {
const lowFreq = 80;
const highFreq = this.sampleRate / 2;
const melLow = this.hzToMel(lowFreq);
const melHigh = this.hzToMel(highFreq);
// Create mel-spaced frequency points
const melPoints = [];
for (let i = 0; i <= numFilters + 1; i++) {
melPoints.push(melLow + (i * (melHigh - melLow)) / (numFilters + 1));
}
// Convert back to Hz
const hzPoints = melPoints.map(mel => this.melToHz(mel));
// Convert to FFT bin indices
const binPoints = hzPoints.map(hz =>
Math.floor((hz * this.fftSize) / this.sampleRate)
);
// Create triangular filters
const filterBank = [];
for (let i = 1; i <= numFilters; i++) {
const filter = new Array(this.analyzer.frequencyBinCount).fill(0);
for (let j = binPoints[i-1]; j < binPoints[i]; j++) {
if (j >= 0 && j < filter.length) {
filter[j] = (j - binPoints[i-1]) / (binPoints[i] - binPoints[i-1]);
}
}
for (let j = binPoints[i]; j < binPoints[i+1]; j++) {
if (j >= 0 && j < filter.length) {
filter[j] = (binPoints[i+1] - j) / (binPoints[i+1] - binPoints[i]);
}
}
filterBank.push(filter);
}
return filterBank;
}
applyMelFilterBank(spectrum) {
return this.melFilterBank.map(filter => {
return filter.reduce((sum, weight, i) => {
return sum + weight * Math.max(spectrum[i] || 0, 0);
}, 0);
});
}
hzToMel(hz) {
return 2595 * Math.log10(1 + hz / 700);
}
melToHz(mel) {
return 700 * (Math.pow(10, mel / 2595) - 1);
}
discreteCosineTransform(input, numCoefficients) {
const output = [];
const N = input.length;
for (let k = 0; k < numCoefficients; k++) {
let sum = 0;
for (let n = 0; n < N; n++) {
sum += input[n] * Math.cos((Math.PI * k * (2 * n + 1)) / (2 * N));
}
output.push(sum);
}
return output;
}
calculateChroma() {
const chromaBins = 12;
const chroma = new Array(chromaBins).fill(0);
for (let i = 0; i < this.frequencyBuffer.length; i++) {
const frequency = (i * this.sampleRate) / (2 * this.frequencyBuffer.length);
const magnitude = this.frequencyBuffer[i] / 255.0;
if (frequency > 80 && magnitude > 0) { // Skip very low frequencies
const pitch = this.frequencyToPitch(frequency);
const chromaIndex = Math.round(pitch) % 12;
chroma[chromaIndex] += magnitude;
}
}
// Normalize
const sum = chroma.reduce((a, b) => a + b, 0);
return sum > 0 ? chroma.map(val => val / sum) : chroma;
}
frequencyToPitch(frequency) {
return 12 * Math.log2(frequency / 440) + 69; // A4 = 440Hz = MIDI note 69
}
calculateZeroCrossingRate() {
let crossings = 0;
for (let i = 1; i < this.timeBuffer.length; i++) {
const prev = (this.timeBuffer[i-1] - 128) / 128; // Convert to [-1, 1]
const curr = (this.timeBuffer[i] - 128) / 128;
if ((prev >= 0) !== (curr >= 0)) {
crossings++;
}
}
return crossings / this.timeBuffer.length;
}
calculateRMS() {
let sum = 0;
for (let i = 0; i < this.timeBuffer.length; i++) {
const sample = (this.timeBuffer[i] - 128) / 128; // Convert to [-1, 1]
sum += sample * sample;
}
return Math.sqrt(sum / this.timeBuffer.length);
}
// Temporal feature analysis
updateFeatureHistory(features) {
// Add current features to history
Object.keys(this.featureHistory).forEach(featureName => {
if (features[featureName] !== undefined) {
this.featureHistory[featureName].push(features[featureName]);
// Maintain history length
if (this.featureHistory[featureName].length > this.historyLength) {
this.featureHistory[featureName].shift();
}
}
});
}
getFeatureStatistics(featureName, windowSize = 10) {
const history = this.featureHistory[featureName];
if (!history || history.length === 0) return null;
const recentHistory = history.slice(-windowSize);
const values = Array.isArray(recentHistory[0]) ?
recentHistory.flat() : recentHistory;
return {
mean: values.reduce((a, b) => a + b) / values.length,
std: this.calculateStandardDeviation(values),
min: Math.min(...values),
max: Math.max(...values),
trend: this.calculateTrend(recentHistory)
};
}
calculateStandardDeviation(values) {
const mean = values.reduce((a, b) => a + b) / values.length;
const squareDiffs = values.map(value => Math.pow(value - mean, 2));
return Math.sqrt(squareDiffs.reduce((a, b) => a + b) / squareDiffs.length);
}
calculateTrend(values) {
if (values.length < 2) return 0;
// Simple linear trend calculation
const firstHalf = values.slice(0, Math.floor(values.length / 2));
const secondHalf = values.slice(Math.floor(values.length / 2));
const firstMean = Array.isArray(firstHalf[0]) ?
firstHalf.flat().reduce((a, b) => a + b) / firstHalf.flat().length :
firstHalf.reduce((a, b) => a + b) / firstHalf.length;
const secondMean = Array.isArray(secondHalf[0]) ?
secondHalf.flat().reduce((a, b) => a + b) / secondHalf.flat().length :
secondHalf.reduce((a, b) => a + b) / secondHalf.length;
return secondMean - firstMean;
}
}
Real-time AI music processing requires careful balance between model complexity and processing speed. Systems must provide immediate response to user input while maintaining audio quality and system stability.
Custom neural network architectures for music tasks require understanding both musical structure and deep learning principles. Different tasks benefit from specialized architectures optimized for temporal, spectral, or structural musical characteristics.
// Custom neural network architectures for music tasks
class MusicNeuralNetworks {
constructor(tf) {
this.tf = tf;
this.models = new Map();
}
// Melody generation RNN
createMelodyRNN(sequenceLength = 32, noteVocabSize = 128, hiddenSize = 256) {
const model = this.tf.sequential();
// Embedding layer for note representation
model.add(this.tf.layers.embedding({
inputDim: noteVocabSize,
outputDim: 64,
inputLength: sequenceLength
}));
// LSTM layers for sequence modeling
model.add(this.tf.layers.lstm({
units: hiddenSize,
returnSequences: true,
dropout: 0.2,
recurrentDropout: 0.2
}));
model.add(this.tf.layers.lstm({
units: hiddenSize,
returnSequences: true,
dropout: 0.2,
recurrentDropout: 0.2
}));
// Attention mechanism
model.add(this.tf.layers.attention());
// Dense layer for note prediction
model.add(this.tf.layers.timeDistributed({
layer: this.tf.layers.dense({
units: noteVocabSize,
activation: 'softmax'
})
}));
model.compile({
optimizer: 'adam',
loss: 'categoricalCrossentropy',
metrics: ['accuracy']
});
return model;
}
// Audio classification CNN
createAudioClassifierCNN(inputShape = [128, 128, 1], numClasses = 10) {
const model = this.tf.sequential();
// Convolutional layers for spectral feature extraction
model.add(this.tf.layers.conv2d({
filters: 32,
kernelSize: [3, 3],
activation: 'relu',
inputShape: inputShape
}));
model.add(this.tf.layers.batchNormalization());
model.add(this.tf.layers.maxPooling2d({ poolSize: [2, 2] }));
model.add(this.tf.layers.conv2d({
filters: 64,
kernelSize: [3, 3],
activation: 'relu'
}));
model.add(this.tf.layers.batchNormalization());
model.add(this.tf.layers.maxPooling2d({ poolSize: [2, 2] }));
model.add(this.tf.layers.conv2d({
filters: 128,
kernelSize: [3, 3],
activation: 'relu'
}));
model.add(this.tf.layers.batchNormalization());
model.add(this.tf.layers.maxPooling2d({ poolSize: [2, 2] }));
// Global average pooling
model.add(this.tf.layers.globalAveragePooling2d());
// Dense layers for classification
model.add(this.tf.layers.dense({
units: 256,
activation: 'relu'
}));
model.add(this.tf.layers.dropout({ rate: 0.5 }));
model.add(this.tf.layers.dense({
units: numClasses,
activation: 'softmax'
}));
model.compile({
optimizer: 'adam',
loss: 'categoricalCrossentropy',
metrics: ['accuracy']
});
return model;
}
// Music VAE for style interpolation
createMusicVAE(inputDim = 128, latentDim = 16, hiddenDims = [256, 128]) {
// Encoder
const encoderInput = this.tf.input({ shape: [inputDim] });
let x = encoderInput;
// Encoder hidden layers
for (const dim of hiddenDims) {
x = this.tf.layers.dense({
units: dim,
activation: 'relu'
}).apply(x);
x = this.tf.layers.batchNormalization().apply(x);
x = this.tf.layers.dropout({ rate: 0.2 }).apply(x);
}
// Latent space
const zMean = this.tf.layers.dense({
units: latentDim,
name: 'z_mean'
}).apply(x);
const zLogVar = this.tf.layers.dense({
units: latentDim,
name: 'z_log_var'
}).apply(x);
// Sampling layer
const sampling = this.tf.layers.lambda({
outputShape: [latentDim],
function: (args) => {
const [zMean, zLogVar] = args;
const epsilon = this.tf.randomNormal(zMean.shape);
return zMean.add(zLogVar.mul(0.5).exp().mul(epsilon));
}
}).apply([zMean, zLogVar]);
// Encoder model
const encoder = this.tf.model({
inputs: encoderInput,
outputs: [zMean, zLogVar, sampling],
name: 'encoder'
});
// Decoder
const decoderInput = this.tf.input({ shape: [latentDim] });
let y = decoderInput;
// Decoder hidden layers
const reversedHiddenDims = [...hiddenDims].reverse();
for (const dim of reversedHiddenDims) {
y = this.tf.layers.dense({
units: dim,
activation: 'relu'
}).apply(y);
y = this.tf.layers.batchNormalization().apply(y);
y = this.tf.layers.dropout({ rate: 0.2 }).apply(y);
}
const decoderOutput = this.tf.layers.dense({
units: inputDim,
activation: 'sigmoid'
}).apply(y);
// Decoder model
const decoder = this.tf.model({
inputs: decoderInput,
outputs: decoderOutput,
name: 'decoder'
});
// VAE model
const vaeOutput = decoder.apply(encoder.outputs[2]);
const vae = this.tf.model({
inputs: encoderInput,
outputs: vaeOutput,
name: 'vae'
});
// Custom VAE loss combining reconstruction and KL divergence
const reconstructionLoss = this.tf.metrics.binaryCrossentropy(encoderInput, vaeOutput);
const klLoss = this.tf.mean(
zLogVar.add(1).sub(zMean.square()).sub(zLogVar.exp())
).mul(-0.5);
const vaeLoss = reconstructionLoss.add(klLoss.mul(0.1)); // Beta-VAE with beta=0.1
vae.compile({
optimizer: 'adam',
loss: () => vaeLoss
});
return { vae, encoder, decoder };
}
// Transformer for music generation
createMusicTransformer(vocabSize = 128, maxLength = 512, dModel = 256, numHeads = 8, numLayers = 6) {
// Positional encoding
const createPositionalEncoding = (maxLen, dModel) => {
const pos = this.tf.range(0, maxLen).expandDims(1);
const i = this.tf.range(0, dModel);
const angleRates = this.tf.pow(10000, i.div(dModel).mul(-1));
const angleRads = pos.mul(angleRates.expandDims(0));
const sines = angleRads.slice([0, 0], [-1, dModel / 2]).sin();
const cosines = angleRads.slice([0, dModel / 2], [-1, -1]).cos();
return this.tf.concat([sines, cosines], 1);
};
// Input layers
const inputs = this.tf.input({ shape: [null] });
// Token embedding
let x = this.tf.layers.embedding({
inputDim: vocabSize,
outputDim: dModel,
maskZero: true
}).apply(inputs);
// Add positional encoding
const posEncoding = createPositionalEncoding(maxLength, dModel);
x = this.tf.layers.add().apply([x, posEncoding]);
// Transformer blocks
for (let i = 0; i < numLayers; i++) {
// Multi-head attention
const attention = this.tf.layers.multiHeadAttention({
numHeads: numHeads,
keyDim: dModel / numHeads
}).apply(x, x);
// Add & Norm
x = this.tf.layers.add().apply([x, attention]);
x = this.tf.layers.layerNormalization().apply(x);
// Feed forward
const ff = this.tf.layers.dense({
units: dModel * 4,
activation: 'relu'
}).apply(x);
const ffOut = this.tf.layers.dense({
units: dModel
}).apply(ff);
// Add & Norm
x = this.tf.layers.add().apply([x, ffOut]);
x = this.tf.layers.layerNormalization().apply(x);
}
// Output projection
const outputs = this.tf.layers.dense({
units: vocabSize,
activation: 'softmax'
}).apply(x);
const model = this.tf.model({
inputs: inputs,
outputs: outputs,
name: 'music_transformer'
});
model.compile({
optimizer: 'adam',
loss: 'sparseCategoricalCrossentropy',
metrics: ['accuracy']
});
return model;
}
}
Intelligent Music Generation (I)
Intelligent music generation combines AI models with musical knowledge to create systems that understand and generate musically coherent content. These systems can assist with composition, improvisation, and creative exploration.
Generation Strategy: Combine rule-based musical knowledge with AI generation for best results. Pure AI generation benefits from constraints based on music theory and stylistic conventions.
Natural Language Processing (N)
Integrating natural language processing enables voice and text-based control of music systems, allowing users to describe musical ideas in natural language and have them translated into musical parameters or generation prompts.
| NLP Application |
Use Case |
Implementation |
Complexity |
| Sentiment Analysis |
Mood-based generation |
Pre-trained models |
Low |
| Lyric Generation |
Text creation for songs |
GPT-style models |
Medium |
| Voice Commands |
DAW control |
Speech recognition + NLU |
Medium |
| Music Description |
Style transfer prompts |
Text embeddings |
High |
Generative Model Training (G)
Training custom generative models enables creation of AI systems tailored to specific musical styles, instruments, or creative objectives. This requires understanding both the musical domain and machine learning training techniques.
Training Considerations: Training neural networks in browsers is limited by computational resources and memory constraints. Consider using transfer learning or fine-tuning pre-trained models for better results.
Transform Music Creation with AI
Master the integration of artificial intelligence and machine learning in browser-based music production. Our comprehensive L.E.A.R.N.I.N.G. framework provides the foundation for creating intelligent music systems that enhance human creativity.
From feature extraction to neural network design, you now have the knowledge to build AI-powered music tools that push the boundaries of creative expression and musical interaction.
Begin Your AI Music Journey
Conclusion: The Creative Partnership of AI and Human Musicians
Browser-based AI music integration represents a new paradigm in creative collaboration between humans and machines. The L.E.A.R.N.I.N.G. framework provides systematic approaches to building AI systems that enhance rather than replace human creativity, opening new possibilities for musical expression and exploration.
As machine learning models become more sophisticated and browser capabilities continue expanding, the techniques outlined in this guide become increasingly valuable. Understanding both the technical implementation and creative applications positions developers and musicians at the forefront of the AI music revolution.
The Collaborative Future
Today, my AI music systems don't just generate notes – they understand musical context, respond to emotional cues, and adapt to my creative intentions in real-time. The machine learning models I've trained on years of musical interactions have become creative partners that inspire rather than constrain. When I play a melancholy chord progression, the AI responds with complementary harmonies that amplify the emotional impact. When I experiment with complex rhythms, it suggests variations I never would have considered. This isn't automation of creativity – it's the augmentation of human musical intelligence with artificial creativity that opens entirely new creative territories. The future of music isn't human versus AI, but human with AI, creating possibilities that neither could achieve alone.
Whether you're developing commercial AI music software, creating interactive compositions, or exploring the boundaries of human-AI collaboration, browser-based machine learning provides unprecedented opportunities for innovation. The democratization of AI tools through web technology ensures that these powerful capabilities are accessible to creators worldwide, fostering a new era of intelligent, responsive, and deeply personal musical experiences.