Machine Learning Meets SuperCollider: Audio Synthesis in 2025

SuperCollider’s synthesis capabilities are being revolutionized by machine learning. After extensive experimentation with neural synthesis, real-time ML processing, and AI-driven sound design, I’ll show you how to integrate modern ML techniques with SuperCollider’s powerful audio engine.

The ML-Audio Revolution

Traditional vs. ML-Enhanced Synthesis

Traditional SuperCollider Synthesis:

// Classic frequency modulation SynthDef(\fm_synth, { var carrier, modulator, output; modulator = SinOsc.ar(freq: 440 * 2, mul: 100); carrier = SinOsc.ar(freq: 440 + modulator); output = carrier * EnvGen.kr(Env.perc, doneAction: 2); Out.ar(0, output ! 2); }).add;

ML-Enhanced Synthesis:

// Neural network-controlled synthesis SynthDef(\neural_fm, { |freq=440, neural_params=#[0.5, 0.3, 0.8]| var carrier, modulator, output; var ml_mod_freq = neural_params[0] * 4 * freq; var ml_mod_depth = neural_params[1] * 200; var ml_env_curve = neural_params[2] * 4 + 0.1; modulator = SinOsc.ar(freq: ml_mod_freq, mul: ml_mod_depth); carrier = SinOsc.ar(freq: freq + modulator); output = carrier * EnvGen.kr( Env([0, 1, 0], [0.01, ml_env_curve], [\lin, \exp]), doneAction: 2 ); Out.ar(0, output ! 2); }).add;

Setting Up ML-SuperCollider Integration

Python-SuperCollider Bridge

First, establish communication between ML models and SuperCollider:

# python_sc_bridge.py import numpy as np from pythonosc import udp_client import torch import torch.nn as nn from sklearn.preprocessing import MinMaxScaler class SuperColliderMLBridge: def __init__(self, sc_host="127.0.0.1", sc_port=57120): self.client = udp_client.SimpleUDPClient(sc_host, sc_port) self.scaler = MinMaxScaler() def send_neural_params(self, synth_name, params): """Send ML-generated parameters to SuperCollider""" # Scale parameters to appropriate ranges scaled_params = self.scaler.fit_transform(params.reshape(-1, 1)).flatten() # Send OSC message to SuperCollider self.client.send_message(f"/neural/{synth_name}", scaled_params.tolist()) def send_spectral_features(self, features): """Send spectral analysis features for real-time processing""" self.client.send_message("/ml/spectral", features) class AudioFeatureExtractor(nn.Module): """Neural network for audio feature extraction""" def __init__(self, input_size=1024, hidden_size=256, output_size=64): super().__init__() self.encoder = nn.Sequential( nn.Linear(input_size, hidden_size), nn.ReLU(), nn.Linear(hidden_size, hidden_size // 2), nn.ReLU(), nn.Linear(hidden_size // 2, output_size), nn.Tanh() # Bounded output for stable synthesis ) def forward(self, x): return self.encoder(x)

SuperCollider OSC Receiver Setup

// Setup OSC responders for ML communication ( ~mlBridge = (); // Receive neural parameters OSCdef(\neuralParams, { |msg| var synthName = msg[1]; var params = msg[2..]; // Store parameters globally for synth access ~mlBridge[synthName.asSymbol] = params; // Trigger synth with ML parameters Synth(synthName, [\neural_params, params]); }, '/neural/*'); // Receive spectral features for real-time modulation OSCdef(\spectralFeatures, { |msg| var features = msg[1..]; // Use spectral features to modulate existing synths ~mlBridge[\spectralFeatures] = features; // Real-time parameter modulation if(~currentSynth.notNil, { ~currentSynth.set(\spectral_mod, features[0]); ~currentSynth.set(\brightness, features[1]); ~currentSynth.set(\roughness, features[2]); }); }, '/ml/spectral'); )

Neural Audio Synthesis Techniques

1. Timbre Transfer with Neural Networks

Transfer the characteristics of one sound onto another:

class TimbreTransferModel(nn.Module): def __init__(self, spectral_size=513): super().__init__() # Encoder for source timbre self.source_encoder = nn.Sequential( nn.Conv1d(1, 64, 7, padding=3), nn.ReLU(), nn.Conv1d(64, 128, 5, padding=2), nn.ReLU(), nn.AdaptiveAvgPool1d(256) ) # Encoder for target timbre self.target_encoder = nn.Sequential( nn.Conv1d(1, 64, 7, padding=3), nn.ReLU(), nn.Conv1d(64, 128, 5, padding=2), nn.ReLU(), nn.AdaptiveAvgPool1d(256) ) # Decoder for synthesis parameters self.decoder = nn.Sequential( nn.Linear(512, 256), nn.ReLU(), nn.Linear(256, 128), nn.ReLU(), nn.Linear(128, 16), # 16 synthesis parameters nn.Sigmoid() ) def forward(self, source_spectrum, target_spectrum): source_features = self.source_encoder(source_spectrum.unsqueeze(1)) target_features = self.target_encoder(target_spectrum.unsqueeze(1)) # Concatenate features combined = torch.cat([source_features.flatten(1), target_features.flatten(1)], dim=1) # Generate synthesis parameters synth_params = self.decoder(combined) return synth_params # Usage with SuperCollider def perform_timbre_transfer(source_audio, target_audio, sc_bridge): model = TimbreTransferModel() # Load pre-trained weights model.load_state_dict(torch.load('timbre_transfer_model.pth')) # Extract spectral features source_spectrum = torch.stft(source_audio, 1024, return_complex=True).abs() target_spectrum = torch.stft(target_audio, 1024, return_complex=True).abs() # Generate synthesis parameters with torch.no_grad(): synth_params = model(source_spectrum.mean(dim=1), target_spectrum.mean(dim=1)) # Send to SuperCollider sc_bridge.send_neural_params("neural_transfer", synth_params.numpy())

2. Real-time Spectral Analysis and Synthesis

// Real-time spectral analysis with ML processing SynthDef(\spectral_ml, { |in_bus=0, out_bus=0| var input, fft, features, ml_params; // Input signal input = In.ar(in_bus, 1); // FFT analysis fft = FFT(LocalBuf(2048), input); // Extract spectral features (centroid, spread, rolloff) features = [ SpectralCentroid.kr(fft), SpecFlatness.kr(fft), SpectralSpread.kr(fft) ]; // Send features to ML model via OSC SendReply.kr(Impulse.kr(30), '/spectral_analysis', features); // Receive ML predictions ml_params = \ml_prediction.kr([0.5, 0.5, 0.5]); // Synthesis based on ML predictions var carrier = SinOsc.ar( freq: (ml_params[0] * 1000 + 200), mul: ml_params[1] ); var filter = BLowPass.ar( carrier, freq: (ml_params[2] * 8000 + 200), rq: 0.5 ); Out.ar(out_bus, filter ! 2); }).add;

Advanced ML Synthesis Architectures

Generative Adversarial Networks for Sound

class AudioGAN: def __init__(self, sample_rate=44100, sequence_length=8192): self.sample_rate = sample_rate self.sequence_length = sequence_length # Generator network self.generator = nn.Sequential( nn.Linear(100, 256), # Noise input nn.ReLU(), nn.Linear(256, 512), nn.ReLU(), nn.Linear(512, 1024), nn.ReLU(), nn.Linear(1024, sequence_length), nn.Tanh() # Output between -1 and 1 ) # Discriminator network self.discriminator = nn.Sequential( nn.Linear(sequence_length, 1024), nn.LeakyReLU(0.2), nn.Linear(1024, 512), nn.LeakyReLU(0.2), nn.Linear(512, 256), nn.LeakyReLU(0.2), nn.Linear(256, 1), nn.Sigmoid() ) def generate_audio(self, noise): """Generate audio from random noise""" return self.generator(noise) def synthesize_to_supercollider(self, sc_bridge, num_samples=10): """Generate samples and send parameters to SuperCollider""" with torch.no_grad(): for i in range(num_samples): # Generate random noise noise = torch.randn(1, 100) # Generate audio generated_audio = self.generate_audio(noise) # Extract synthesis parameters from generated audio params = self.audio_to_synth_params(generated_audio) # Send to SuperCollider sc_bridge.send_neural_params(f"gan_synth_{i}", params) def audio_to_synth_params(self, audio): """Convert generated audio to SuperCollider synthesis parameters""" # Analyze generated audio for synthesis parameters fft = torch.fft.fft(audio) magnitude = torch.abs(fft) # Extract features spectral_centroid = torch.mean(magnitude * torch.arange(len(magnitude))) spectral_spread = torch.std(magnitude) zero_crossing_rate = torch.mean(torch.abs(torch.diff(torch.sign(audio)))) # Normalize to synthesis parameter ranges params = torch.tensor([ (spectral_centroid / len(magnitude)).item(), (spectral_spread / torch.max(magnitude)).item(), zero_crossing_rate.item(), torch.mean(torch.abs(audio)).item() # RMS level ]) return params.numpy()

Recurrent Neural Networks for Temporal Synthesis

class TemporalSynthesizer(nn.Module): def __init__(self, input_size=13, hidden_size=128, output_size=8): super().__init__() # LSTM for temporal modeling self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True, num_layers=2) # Output layer for synthesis parameters self.output_layer = nn.Sequential( nn.Linear(hidden_size, output_size), nn.Sigmoid() # Bounded output for stable synthesis ) # Attention mechanism for temporal focus self.attention = nn.MultiheadAttention(hidden_size, num_heads=4) def forward(self, input_sequence): # LSTM processing lstm_output, _ = self.lstm(input_sequence) # Apply attention attended_output, _ = self.attention(lstm_output, lstm_output, lstm_output) # Generate synthesis parameters synth_params = self.output_layer(attended_output) return synth_params # Real-time temporal synthesis class RealTimeSynthesizer: def __init__(self, model_path, sc_bridge): self.model = TemporalSynthesizer() self.model.load_state_dict(torch.load(model_path)) self.model.eval() self.sc_bridge = sc_bridge self.sequence_buffer = [] self.sequence_length = 32 def process_audio_features(self, features): """Process incoming audio features and generate synthesis parameters""" self.sequence_buffer.append(features) if len(self.sequence_buffer) >= self.sequence_length: # Prepare sequence for model sequence = torch.tensor(self.sequence_buffer[-self.sequence_length:]).unsqueeze(0) with torch.no_grad(): # Generate synthesis parameters synth_params = self.model(sequence) # Use the latest output current_params = synth_params[0, -1].numpy() # Send to SuperCollider self.sc_bridge.send_neural_params("temporal_synth", current_params)

Interactive ML-Driven Synthesis

Reinforcement Learning for Adaptive Synthesis

import gym from stable_baselines3 import PPO class SynthEnvironment(gym.Env): def __init__(self, sc_bridge): super().__init__() self.sc_bridge = sc_bridge # Action space: synthesis parameters self.action_space = gym.spaces.Box( low=0.0, high=1.0, shape=(8,), dtype=np.float32 ) # Observation space: audio features self.observation_space = gym.spaces.Box( low=-1.0, high=1.0, shape=(16,), dtype=np.float32 ) self.current_features = np.zeros(16) self.target_features = None def step(self, action): # Send synthesis parameters to SuperCollider self.sc_bridge.send_neural_params("rl_synth", action) # Get audio feedback (simulated or real-time analysis) new_features = self.get_audio_features() # Calculate reward based on how close we are to target reward = self.calculate_reward(new_features) self.current_features = new_features # Episode is done if we're close enough to target done = np.linalg.norm(new_features - self.target_features) < 0.1 return new_features, reward, done, {} def reset(self): # Set new target features self.target_features = np.random.randn(16) self.current_features = np.zeros(16) return self.current_features def calculate_reward(self, features): # Reward is negative distance to target distance = np.linalg.norm(features - self.target_features) return -distance # Train the RL agent def train_adaptive_synthesizer(): sc_bridge = SuperColliderMLBridge() env = SynthEnvironment(sc_bridge) # PPO agent for continuous control model = PPO("MlpPolicy", env, verbose=1) # Training model.learn(total_timesteps=100000) # Save trained model model.save("adaptive_synthesizer") return model

Real-time Collaborative Synthesis

// Collaborative synthesis with multiple ML agents ( ~mlCollaboration = (); // Agent coordination ~mlCollaboration[\agents] = IdentityDictionary[ \rhythmic_agent -> IdentityDictionary[\params -> [0.5, 0.3, 0.8], \active -> true], \harmonic_agent -> IdentityDictionary[\params -> [0.7, 0.2, 0.6], \active -> true], \textural_agent -> IdentityDictionary[\params -> [0.4, 0.9, 0.1], \active -> true] ]; // Collaborative synthesis function ~mlCollaboration[\synthesize] = { var agents = ~mlCollaboration[\agents]; agents.keysValuesDo { |agentName, agentData| if(agentData[\active], { var params = agentData[\params]; // Each agent controls different synthesis aspects switch(agentName, \rhythmic_agent, { Synth(\ml_rhythmic, [ \rate, params[0] * 4 + 0.5, \pattern_complexity, params[1], \swing, params[2] * 0.2 ]); }, \harmonic_agent, { Synth(\ml_harmonic, [ \root_freq, params[0] * 200 + 100, \harmony_density, params[1] * 8 + 1, \dissonance, params[2] ]); }, \textural_agent, { Synth(\ml_textural, [ \grain_density, params[0] * 50 + 10, \spectral_blur, params[1], \spatialization, params[2] ]); } ); }); }; }; // OSC responder for agent updates OSCdef(\agentUpdate, { |msg| var agentName = msg[1].asSymbol; var newParams = msg[2..]; if(~mlCollaboration[\agents][agentName].notNil, { ~mlCollaboration[\agents][agentName][\params] = newParams; // Trigger collaborative synthesis ~mlCollaboration[\synthesize].value; }); }, '/ml/agent/*'); )

Performance Optimization

Efficient Neural Inference

class OptimizedMLSynth: def __init__(self, model_path, use_tensorrt=False): self.model = self.load_optimized_model(model_path, use_tensorrt) self.inference_cache = {} self.batch_size = 32 def load_optimized_model(self, model_path, use_tensorrt): """Load and optimize model for real-time inference""" model = torch.jit.load(model_path) # Use TorchScript for speed if use_tensorrt: # TensorRT optimization for NVIDIA GPUs import torch_tensorrt model = torch_tensorrt.compile(model, inputs=[torch_tensorrt.Input((1, 16))], enabled_precisions={torch.float16} # Half precision ) return model def batch_inference(self, feature_batch): """Process multiple features simultaneously""" with torch.no_grad(): return self.model(feature_batch) def cached_inference(self, features, cache_tolerance=0.01): """Use cached results for similar inputs""" features_key = tuple(np.round(features / cache_tolerance).astype(int)) if features_key in self.inference_cache: return self.inference_cache[features_key] # Compute new inference result = self.model(torch.tensor(features).unsqueeze(0)) self.inference_cache[features_key] = result # Limit cache size if len(self.inference_cache) > 1000: # Remove oldest entries oldest_keys = list(self.inference_cache.keys())[:100] for key in oldest_keys: del self.inference_cache[key] return result

Future Directions

Emerging Techniques

Diffusion Models for Audio Generation:

class AudioDiffusionModel: def __init__(self): # Denoising diffusion model for audio generation self.model = UNet1D( in_channels=1, out_channels=1, time_embedding_dim=128 ) def generate_audio_sequence(self, length=8192, steps=50): """Generate audio using diffusion process""" # Start with random noise x = torch.randn(1, 1, length) # Iterative denoising for t in range(steps, 0, -1): # Predict noise noise_pred = self.model(x, t) # Denoise step x = self.denoise_step(x, noise_pred, t) return x.squeeze()

Transformer Architectures for Musical Structure:

• Attention mechanisms for long-term musical dependencies
• Multi-scale temporal modeling
• Cross-modal attention between audio and control parameters

Conclusion

The integration of machine learning with SuperCollider opens unprecedented possibilities for audio synthesis and creative expression. From neural timbre transfer to real-time adaptive synthesis, ML techniques are expanding what’s possible in computer music.

Key takeaways:

• Real-time ML: Modern techniques enable low-latency neural processing
• Creative Control: ML can augment rather than replace musical intuition
• Collaborative Systems: Multiple ML agents can work together for complex synthesis
• Adaptive Behavior: Synthesis systems that learn and evolve over time

The future of computer music lies in the thoughtful combination of traditional synthesis techniques with intelligent, adaptive ML systems. SuperCollider’s flexibility makes it an ideal platform for exploring these new possibilities.

As we continue to develop these tools, the boundary between performer, instrument, and artificial intelligence becomes increasingly fluid, opening new territories for musical exploration.

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