Generative Music Platform Using Neural Networks for Multi-Instrument Audio
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current music generation technologies require musical training and manual input, limiting accessibility to generating high-quality multi-instrument music, and are often complex and expensive, necessitating the development of a system that can create multi-level music from human-created sound without the need for musical expertise or extensive editing.
Innovation Solution
A music generation platform utilizing a Vector Quantized Variational Autoencoder (VQ-VAE) and Long Short-Term Memory (LSTM) network to convert raw human-created audio into discrete representations, embeddings, and instrument-specific code sequences, allowing for real-time generation of multi-instrument music based on user steerings such as genre and style.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional music creation methods are used with multiple musicians and heavy editing, then high quality music can be generated, but the process becomes complex and requires substantial training
Solution Approach 1:
The patent replaces the mechanical system of multiple musicians performing and manually editing music with an automated neural network system. The multi-instrument music generation model automatically processes human audio input and generates multi-track music arrangements without requiring manual musical performance or editing, thus reducing system complexity while maintaining music quality
Solution Approach 2:
The system enables self-service music creation where users can generate high-quality multi-instrument music by simply providing human audio input without needing musical training or manual intervention. The neural network automatically handles the complex tasks of music composition, arrangement, and instrumentation
2Ease of manufacture
If MIDI-compatible instrumental recordings are generated through manual input of sheet music or keyboard performance, then instrumental recordings can be created, but the process requires musical training and experience
Solution Approach 1:
The patent replaces the manual mechanical process of inputting sheet music or performing on keyboards with an automated neural network system that processes human audio input directly. This substitution eliminates the need for users to have musical training or experience while maintaining the ability to generate high-quality instrumental recordings
Solution Approach 2:
The system introduces human audio input as an intermediary between the user and the music generation process. Instead of requiring users to directly manipulate musical notation or instrument controls, the system accepts natural human audio (such as humming or speaking) and uses the neural network to translate it into multi-instrument music arrangements
3Ease of operation
If synthesizers are used to generate instrumental recordings without human musicians, then musical performance requirements are reduced, but the synthesizers remain complex and expensive
Solution Approach 1:
The patent creates a universal music generation system that can handle multiple instrument types and musical styles through a single multi-instrument music generation model. This unified approach eliminates the need for separate synthesizers for different instruments, reducing overall system complexity and cost while maintaining ease of operation
Solution Approach 2:
The system merges the functionality of multiple instrument-specific synthesizers into a single integrated neural network model. By combining the capabilities of handling different instruments, styles, and arrangements in one system, the patent reduces complexity while maintaining accessibility for users without musical training
Data Source
AI summary
The technology can use a music generation platform to ingest raw audio to generate multi-level music (i.e., multiple streams corresponding to different instruments) based on user steerings, such as genre, artist, style, etc. Implementations can apply an encoder to take the raw audio and generate a sequence of discrete representations. Implementations can then input the sequence of discrete representations to an embedding layer that converts the sequence of discrete representations to sequences of embeddings in the same dimensionality, which are summed together to form a single sequence. The sequence of summed embeddings can be provided to a neural network that produces sequences of predicted embeddings for multiple instruments, which are then used by a coder layer to generate instrument-specific code sequences. Implementations can input the instrument-specific code sequences to a decoder, which can also receive the user steerings, and convert them into Mel spectrograms, then instrument-specific audio waveforms.