AI Transformer Audio Compression Using Spectral Order Extraction
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Solution Overview
Problem
Current audio compression technologies are limited in their ability to achieve high levels of compression while maintaining audio fidelity, leading to significant storage, transmission, and processing overhead.
Innovation Solution
The method employs AI transformer-based file compression using a deep learning enabled temporal Generative Adversarial Network (GAN) for upsampling, AI-assisted mapping of dynamics and harmonics, noise reduction, and deep learning-based audio compression with attention transformers to increase dimensional complexity and optimize sampling rate and depth, resulting in enhanced compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional audio compression methods are used, then compression ratio is limited, but storage and processing overhead remains high
Solution Approach 1:
The patent applies parameter changes by transforming audio from the time domain to the frequency domain using Fast Fourier Transform (FFT), then applying compression in the frequency domain where spectral characteristics reveal hidden patterns and correlations. This dimensional transformation enables significantly higher compression ratios while maintaining audio fidelity, directly resolving the contradiction between compression ratio and storage overhead.
Solution Approach 2:
The patent replaces traditional mechanical compression systems with an AI transformer-based system that uses deep learning models trained on audio data. The transformer architecture processes audio spectrograms and generates compressed representations through learned patterns, achieving superior compression efficiency compared to conventional algorithms, thereby reducing both storage requirements and processing overhead.
2Quantity of substance
If compression level is increased, then storage overhead is reduced, but audio fidelity deteriorates
Solution Approach 1:
The patent applies dimensionality change by converting audio signals into spectrogram representations in the frequency-time domain. This transformation reveals spectral correlations and patterns that are not apparent in the time domain, enabling the AI transformer to identify and compress redundant information while preserving essential audio characteristics. The result is high compression levels maintained with excellent audio fidelity.
Solution Approach 2:
The patent implements feedback mechanisms through the transformer architecture's attention mechanisms and residual connections, which continuously refine the compression process. The model learns from the input audio spectrum and adjusts its compression strategy in real-time, ensuring that compressed representations maintain high fidelity while achieving maximum compression, thus resolving the contradiction between compression level and audio quality.
Data Source
AI summary
An AI-based audio compression method for use with audio formats, alone or in combination with other audio compression and enhancement approaches. A combination of audio pre-processing, sound to visual transcoding of audio, and a sequence of AI-enabled methods enabling maximal entropy order extraction applied within the sound and dimensionally extended visual domain projection of the audio significantly increases the degree of compression of audio files, thereby reducing storage, transmission and processing overhead associated with audio. A unique AI-driven domain conversion is leveraged together with domain-specific AI processing stages to reduce file size, while supporting optional use of standard and proprietary audio encoding, decoding, compression, and other methods. Support for native mode photonic computer processing of the higher-dimensional order representation of media enables further optimization via photonic computing methods that would not be possible if the audio was not extended into higher order visual domain space.