Neural Network Audio Spatial Encoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing audio content classification and generation methods lack advanced capabilities to effectively model and understand complex multi-dimensional relationships in multi-channel audio content, limiting their ability to generate new audio content based on desired classes such as movie dialogue, podcasts, or music.
Innovation Solution
The use of neural networks, specifically attention-based neural networks with positional encoding processes, to transform audio data from one spatial data type to another, allowing for the identification of latent space variables and generation of new audio content by training on encoded audio data that includes spatial and feature type representations in embedding vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing classification methods are used, then basic audio content categorization is achieved, but advanced capabilities to model complex multi-dimensional relationships are lacking
Solution Approach 1:
The patent applies positional encoding to transform spatial audio data into a higher-dimensional embedding space, enabling the neural network to capture complex multi-dimensional relationships between audio channels. This dimensional transformation allows the model to process spatial relationships (e.g., channel positions in Dolby 5.1 or 7.1 formats) as rich feature representations, significantly improving the ability to model complex audio content patterns while maintaining classification accuracy.
2Measurement precision
If neural networks with positional encoding are used, then new audio content generation and classification accuracy are improved, but device complexity increases
Solution Approach 1:
The patent replaces traditional audio processing mechanisms with neural network-based processing. Instead of using conventional signal processing algorithms, the system employs trained neural networks that automatically learn and apply complex transformations to audio data. This substitution enables advanced classification and content generation capabilities while the trained models can be efficiently deployed on various hardware platforms, managing the complexity through software-based solutions.
3Adaptability or versatility
If audio data is transformed between different spatial formats, then adaptability to different audio formats is improved, but processing complexity increases
Solution Approach 1:
The patent creates a universal neural network model that can handle multiple audio spatial formats (e.g., Dolby 5.1, Dolby 7.1, stereo) through a single unified architecture. The positional encoding mechanism is designed to work with different channel configurations, allowing the same model to process and transform audio data across various formats. This multi-functional approach enables format adaptability without requiring separate processing pipelines for each audio format, managing complexity through a single versatile system.
Data Source
AI summary
Some disclosed methods involve receiving audio data of at least a first audio data type and a second audio data type, including audio signals and associated spatial data indicating intended perceived spatial positions for the audio signals, determining at least a first feature type from the audio data and applying a positional encoding process to the audio data, to produce encoded audio data. The encoded audio data may include representations of at least the spatial data and the first feature type in first embedding vectors of an embedding dimension. Some methods may involve training a neural network, based on the encoded audio data, to transform audio data from an input audio data type having an input spatial data type to a transformed audio data type having a transformed spatial data type. Some methods may involve training a neural network to identify an input audio data type.