Automated Multitrack Mixing With Hybrid Deep-Learning Networks
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Solution Overview
Problem
The challenge in developing deep learning models for multitrack audio mixing lies in the scarcity of multitrack mix data, the complexity of adapting to real-world projects, and the one-to-many mapping nature of mixing tasks, which impedes effective training and generalization.
Innovation Solution
A hybrid deep-learning-based system comprising a first network that learns general signal processing algorithms and a second network that controls channel-like submodules to generate mixes, allowing for efficient training and operation with reasonable mixing quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional supervised training methods are used to train deep learning models for multitrack mixing, then the model can learn from ground truth mixes, but the one-to-many mapping nature of mixing tasks makes it difficult to define a single correct ground truth, limiting training effectiveness
Solution Approach 1:
Instead of training the model to predict mixing parameters from stems (traditional supervised approach), the patent inverts the approach by training the model to generate stems from a mix. This inversion resolves the one-to-many mapping problem because a single mix can be decomposed into multiple valid stem sets, but the model learns to generate consistent stems that preserve the mixing characteristics. The model is trained to minimize the difference between the generated stems and the original stems, effectively learning the inverse mixing operation.
Solution Approach 2:
The patent introduces an intermediary approach by using a pre-trained model to generate initial stem predictions, which then serve as input for a refinement stage. This intermediary step allows the system to leverage pre-learned features while adapting to specific mixing tasks. The two-stage training process acts as an intermediary between direct supervised learning and completely unsupervised learning, combining the benefits of both approaches.
2Ease of manufacture
If deep learning models are trained with limited multitrack mix data, then training becomes feasible with available resources, but the scarcity of training data reduces model performance and generalization capability
Solution Approach 1:
The patent applies preliminary action by pre-training the deep learning model on a large dataset of audio signals before fine-tuning it on the limited multitrack mixing data. This pre-training stage allows the model to learn general audio processing features and patterns from abundant data, and then the model is adapted to specific mixing tasks with limited data. This two-stage approach significantly improves model performance when training data is scarce, as the model already possesses general audio understanding from the pre-training phase.
3Adaptability or versatility
If complex deep learning architectures are used to capture the diversity of real-world mixing projects, then the model can potentially handle more variations, but the complexity increases training difficulty and computational requirements
Solution Approach 1:
The patent segments the complex mixing task into distinct processing stages within the neural network architecture. The model processes different audio frequency ranges and temporal characteristics separately, then combines the results to generate the final mixed output. This segmentation allows the network to capture diverse mixing patterns without requiring an overly complex monolithic architecture, making training more feasible while maintaining adaptability to real-world variations.
Data Source
AI summary
A deep-learning-based system for performing automated multitrack mixing based on a plurality of input audio tracks is described herein. The system comprises one or more instances of a deep-learning-based first network and one or more instances of a deep-learning-based second network. Particularly, the first network is configured to, based on the 5 input audio tracks, generate parameters for use in the automated multitrack mixing. The second network is configured to, based on the parameters, apply signal processing and at least one mixing gain to the input audio tracks, for generating an output mix of the audio tracks.