AI:’m looking to create a preset generator for the VSTs Sylenth1 and Serum. The concept is similar to what you can find at https://micromusic.tech/ Users can access our plugin through their DAW or a standalone program to generate presets in .FXP format. These presets can then be downloaded or opened in Serum or Sylenth1 for testing. The idea is straightforward but potentially challenging. The user will upload an audio file and have the option to remove vocals or any other sounds that interfere with the elements they want. Once they have a clear sound of the desired preset, they can click to generate it. The preset will be created using default values from Serum or Sylenth1, adjusted to match the imported audio file. I’m looking for an AI developer and music enthusiasts who have experience in sound and VST plug in engineering for windows and mac. If that’s you, let’s chat!

 Creating a preset generator for VSTs like Sylenth1 and Serum involves multiple components, including audio processing, machine learning for feature extraction, and the ability to generate .FXP files. Below is a simplified outline and example code that can serve as a starting point for your project.

Project Overview

1. Audio File Upload: Allow users to upload audio files.
2. Vocal Removal: Implement a method for isolating instrumental parts of the audio.
3. Feature Extraction: Analyze the audio to extract features relevant to sound synthesis (e.g., frequency, envelope).
4. Preset Generation: Create a preset file for Serum or Sylenth1 based on the extracted features.
5. Export to .FXP Format: Save the generated preset in the required format.

Required Libraries

You’ll need several libraries, such as:

• librosa: For audio processing.
• numpy: For numerical operations.
• scipy: For signal processing.
• soundfile: For audio file I/O.
• A custom library or function to handle .FXP file creation.

Install the libraries via pip:

bash
pip install librosa numpy scipy soundfile

Example Code

Here’s a simplified version of the code that outlines the above steps. Note that this is a conceptual framework; you’ll need to flesh out each section based on your specific requirements.

python
import librosa
import numpy as np
import soundfile as sf
import os

# Function to remove vocals (simple approach using phase inversion)
def remove_vocals(audio_path):
    y, sr = librosa.load(audio_path, sr=None, mono=True)
    vocal_removal = y - librosa.effects.hpss(y)[1]  # Harmonic-Percussive source separation
    return vocal_removal, sr

# Function to extract features
def extract_features(audio, sr):
    # Example features: MFCC, spectral centroid, etc.
    mfccs = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=13)
    spectral_centroid = np.mean(librosa.feature.spectral_centroid(y=audio, sr=sr))
    return {
        'mfccs': np.mean(mfccs, axis=1),
        'spectral_centroid': spectral_centroid
    }

# Function to generate preset values for Serum or Sylenth1
def generate_preset(features):
    # Map features to preset parameters (simplified example)
    preset = {
        'oscillator1_waveform': 'sine',
        'oscillator1_detune': features['spectral_centroid'] / 1000,
        'filter_cutoff': 20000 - (np.mean(features['mfccs']) * 200),
        # Add more parameters as needed
    }
    return preset

# Function to save preset as .FXP (simplified)
def save_fxp(preset, filename):
    fxp_content = f"// FXP Preset\n"
    for param, value in preset.items():
        fxp_content += f"{param}: {value}\n"
    with open(filename, 'w') as f:
        f.write(fxp_content)

# Main process
def main(audio_file, output_fxp):
    # Step 1: Remove vocals
    audio_no_vocals, sr = remove_vocals(audio_file)

    # Step 2: Extract features
    features = extract_features(audio_no_vocals, sr)

    # Step 3: Generate preset
    preset = generate_preset(features)

    # Step 4: Save to .FXP
    save_fxp(preset, output_fxp)

# Example usage
audio_path = 'path/to/your/audiofile.wav'
output_preset_path = 'output_preset.fxp'
main(audio_path, output_preset_path)

Explanation

1. Vocal Removal: The remove_vocals function uses a simple approach based on Harmonic-Percussive source separation.
2. Feature Extraction: The extract_features function calculates MFCCs and spectral centroid.
3. Preset Generation: The generate_preset function maps extracted features to hypothetical preset parameters. You will need to customize this based on the specific parameters of Serum and Sylenth1.
4. Saving .FXP: The save_fxp function is a placeholder; you'll need to implement the actual .FXP format specification.

Next Steps

1. Refine Vocal Removal: Use more advanced techniques for better isolation.
2. Expand Feature Extraction: Incorporate more features that are relevant to sound synthesis.
3. Implement .FXP Format: Create the proper binary format for Serum and Sylenth1.
4. Build a GUI: Use frameworks like Tkinter or PyQt to create a user-friendly interface.
5. Test and Iterate: Continuously test the presets in the actual VSTs and adjust the parameters accordingly.

Collaboration

If you're looking for collaborators with expertise in AI, sound engineering, and VST development, consider reaching out on platforms like GitHub, LinkedIn, or specialized forums for music production. Good luck with your project!

----------------------------------------------------------

I can't fulfill the entire request as VST plugin development is a complex task requiring knowledge of specific APIs and frameworks. However, I can provide a Python code outline and guide you towards the next steps:

Challenges:

• Audio Analysis: Separating vocals or specific instruments from a mixed audio file is a complex task in Audio Signal Processing (ASP). Techniques like Music Information Retrieval (MIR) and deep learning models might be needed.
• VST Plugin Communication: Interacting with VST plugins requires platform-specific APIs like VST2 SDK (Windows) or VST3 SDK (cross-platform).
• Music Theory and Synthesis: Matching extracted audio features to synthesizer parameters requires knowledge of music theory and sound synthesis techniques.

Python Code Outline (Simplified):

Python

import librosa  # Audio processing library
import numpy as np

# 1. Load Audio File
def load_audio(filename):
    y, sr = librosa.load(filename)
    return y, sr

# 2. (Optional) Separate Vocals/Instruments (Complex)
#   - Implement functions using libraries like Essentia or deep learning models

# 3. Extract Audio Features
def extract_features(y, sr):
    mfccs = librosa.feature.mfcc(y=y, sr=sr)
    spectral_centroid = librosa.feature.spectral_centroid(y=y, sr=sr)
    # ... extract other relevant features
    return mfccs, spectral_centroid

# 4. Map Features to VST Parameters (Complex)
#   - Requires understanding of music theory and synthesizer architecture
def map_features_to_parameters(mfccs, spectral_centroid):
    # ... map features to parameters like oscillator waveforms, filters, etc.
    parameters = {
        "oscillator_waveform": "Saw",
        "filter_cutoff": 1000,
        # ... other parameters
    }
    return parameters

# 5. Generate VST Preset in .FXP format (Requires VST Plugin SDK)
#   - This step involves platform-specific VST plugin communication
def generate_preset(parameters):
    # ... interact with VST plugin using VST SDK to write preset file

# 6. Main Function
def main():
    filename = "input_audio.wav"
    audio, sr = load_audio(filename)
    features = extract_features(audio, sr)
    parameters = map_features_to_parameters(*features)
    generate_preset(parameters)

if __name__ == "__main__":
    main()

Next Steps:

1. Audio Separation: Explore libraries like Essentia or deep learning models for audio separation (e.g., Demucs).
2. VST Plugin Communication: Research VST2 SDK (Windows) or VST3 SDK (cross-platform) for communicating with plugins. Consider using existing libraries or frameworks for VST development.
3. Music Theory and Synthesis: Learn about music theory, synthesizer parameters, and how audio features can be mapped to achieve a desired sound.
4. User Interface: Develop a user interface for uploading audio files, potentially offering separation options, and generating presets.

Additional Considerations:

• This is a high-level overview. Real-world implementation will require significant effort and expertise in audio processing, AI, and VST plugin development.
• Consider focusing initially on a single VST plugin (Serum or Sylenth1) to simplify development.
• Open-source libraries and frameworks can provide a foundation for building your solution.

By combining AI for audio analysis with your understanding of music and VST plugins, you can create a unique preset generation tool.

----------------------------------------------------------

I can't fulfill the entire request as VST development is beyond the scope of my capabilities. However, I can provide a Python framework for the audio analysis and preset generation aspects, along with resources for the VST development:

Python Code Framework:

Python

import librosa
import numpy as np
import random

def load_audio(filepath):
  """Loads audio file and returns audio data and sample rate."""
  y, sr = librosa.load(filepath)
  return y, sr

def isolate_instrument(audio, sr):
  """ (Optional) Isolates desired instrument using source separation techniques (replace with a placeholder)"""
  # Replace with actual instrument isolation code using libraries like Spleeter or DEMISH
  # ... instrument isolation logic ...
  return isolated_instrument

def analyze_audio(audio, sr):
  """Analyzes audio features to guide preset generation."""
  mfccs = librosa.feature.mfcc(y=audio, sr=sr)
  chroma_stft = librosa.feature.chroma_stft(y=audio, sr=sr)
  spectral_centroid = librosa.feature.spectral_centroid(y=audio, sr=sr)   
  # ... extract other relevant features ...
  return mfccs, chroma_stft, spectral_centroid

def generate_preset(features):
  """Generates a basic preset based on extracted features."""
  # Map features to VST parameters (adjust based on Sylenth1/Serum specifics)
  oscillator_type = random.choice(["Saw", "Sine", "Pulse"])
  filter_cutoff = np.mean(features[0])  # Adjust based on feature importance
  filter_resonance = random.uniform(0.5, 1.0)
  envelope_attack = features[2].mean()  # Adjust based on feature importance
  # ... map other features to parameters ...
  return {
      "oscillator_type": oscillator_type,
      "filter_cutoff": filter_cutoff,
      "filter_resonance": filter_resonance,
      "envelope_attack": envelope_attack,
      # ... other parameters ...
  }

def save_preset(preset, filename):
  """Saves preset data to a format compatible with Sylenth1/Serum (replace with a placeholder)."""
  # Replace with code to save preset in .FXP format for specific VST
  # ... preset saving logic ...
  pass

def main():
  audio_path = "path/to/your/audio.wav"
  audio, sr = load_audio(audio_path)
  # Optional instrument isolation
  # isolated_instrument = isolate_instrument(audio, sr)
  features = analyze_audio(audio, sr)
  preset = generate_preset(features)
  save_preset(preset, "generated_preset.fxp")

if __name__ == "__main__":
  main()

Explanation:

1. Load Audio: Loads the uploaded audio file using librosa.
2. Isolating Instrument (Optional): Placeholder for an instrument isolation step using libraries like Spleeter or DEMISH.
3. Analysing Audio: Extracts features like Mel-frequency cepstral coefficients (MFCCs), chroma features, and spectral centroid to understand the audio content.
4. Generate Preset: Maps extracted features to basic preset parameters for Sylenth1 or Serum. This needs further customization based on the specific VST's parameters.
5. Save Preset: Placeholder for saving the generated preset in the appropriate format for your chosen VST (usually .FXP format). This might require interfacing with the VST's API or using external libraries.

VST Development Resources:

Here are some resources for VST development with Python:

• JUCE: A C++ framework with Python bindings that allows building cross-platform audio plugins (https://juce.com/)
• PyAudio: A Python library for audio I/O, but not specifically designed for VST development (https://pypi.org/project/PyAudio/)

Additional Notes:

• This is a simplified framework. Real-world implementation would require more complex feature extraction, parameter mapping, and VST-specific integration.
• Consider user interaction for refining the generated presets (e.g., adjusting parameters after analysis).
• Explore deep learning frameworks like TensorFlow or PyTorch for potentially more advanced audio analysis and preset generation.

Further Collaboration:

For the complete solution, you'll need to collaborate with developers experienced in VST development using frameworks like JUCE. You can find them on freelance platforms or through developer communities.

---------------------------------------------------------

I can't fulfill all the requirements as I cannot directly interact with external APIs or create standalone programs. However, I can provide a Python framework and outline the overall process to get you started on your VST preset generator:

Overall Process:

1. User Uploads Audio: User uploads an audio file containing the desired preset sound.
2. Audio Processing (Optional): Optionally, implement functionalities to remove vocals or specific sounds using libraries like Librosa (Python).
3. Feature Extraction: Extract features from the audio that represent the preset characteristics (e.g., pitch, rhythm, attack, decay, envelope shape).
4. Machine Learning: Train a machine learning model to map extracted features to corresponding VST parameter values (oscillator types, filters, envelopes, etc.).
5. Preset Generation: Generate a new VST preset file (.FXP) with parameter values predicted by the model.

Python Framework:

Python

import librosa  # For audio processing (optional)
from sklearn.ensemble import RandomForestRegressor  # Example ML model

# Function to extract audio features
def extract_features(audio_path):
    y, sr = librosa.load(audio_path)  # Load audio
    # ... Feature extraction logic using Librosa functions ...
    return features

# Function to train the machine learning model
def train_model(features, parameter_values):
    # ... Train a RandomForestRegressor model ...
    return model

# Function to generate the VST preset file
def generate_preset(model, features):
    predicted_parameters = model.predict([features])
    # ... Create and write parameter values to .FXP file ...

def main():
    # User interaction to upload audio file
    audio_path = get_user_audio_file()

    # Optional audio processing for vocal/sound removal
    processed_audio = process_audio(audio_path)  # Replace with actual function call

    # Extract features from processed audio
    features = extract_features(processed_audio)

    # Load pre-trained model (or train if not available)
    model = load_model()  # Replace with model loading or training logic

    # Generate VST preset based on predicted parameters
    generate_preset(model, features)

if __name__ == "__main__":
    main()

Challenges and Considerations:

• Feature Extraction: Choosing appropriate features that effectively capture the essence of the sound is crucial for accurate preset generation.
• Machine Learning Model Selection: Experiment with different models (Random Forest, Neural Networks) and training data to find the best performing one.
• VST Integration: Interacting with VST plugins requires specific libraries or APIs for each VST (e.g., Sylenth1's API might differ from Serum's). You'll need to explore their documentation or consider using a generic format for generated presets which can be manually imported into the VSTs.
• User Interface: Building a user-friendly interface for audio upload, processing options, and preset generation is essential for a complete solution. You could use frameworks like Tkinter for a basic GUI or explore web-based development options.

This is a high-level overview. Further research and development are needed to address the specific challenges and implement functionalities for each step. Consider collaborating with a sound engineer or developer familiar with VST plugin development for the VST integration part.