Audio Device Generative Model Codec Processing
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Solution Overview
Problem
The increasing number of audio devices competing for limited bandwidth results in delays, dropouts, and poor audio communication experiences, particularly when trying to maintain high audio quality at low or very low bit-rates.
Innovation Solution
An audio device configured as a receiver, equipped with a decoder and a first signal processor operating according to a generative model, processes encoded audio signals using codec information to enhance signal quality, thereby preserving high audio quality even at low bit-rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If encoding and decoding of audio signals is used to reduce bit-rate, then the number of audio devices that can share bandwidth is improved, but the quality of audio signal deteriorates due to lossy compression and artifacts
Solution Approach 1:
The patent applies generative AI models to convert the harmful artifacts introduced by lossy compression into beneficial effects. The generative model learns from compressed audio data and reconstructs high-quality audio by predicting and replacing artifacts, effectively turning the harmful compression artifacts into opportunities for enhancement rather than simple restoration.
Solution Approach 2:
The system changes the parameter of audio quality by using generative models that operate in the latent space of audio representations. Instead of traditional linear filtering, the system transforms audio through non-linear generative processes that can recover information lost during compression, achieving high quality at low bit-rates that were previously impossible.
2Productivity
If low or very low bit-rates are used to fit more audio devices, then bandwidth efficiency is improved, but audio quality and communication experience deteriorate
Solution Approach 1:
The generative AI model converts the harmful effects of low bit-rate compression into beneficial enhancement opportunities. By training on compressed audio data and learning to predict and replace artifacts, the system achieves high communication quality at low bit-rates, effectively turning the limitation into an advantage for bandwidth efficiency.
Solution Approach 2:
The patent replaces traditional mechanical signal processing (linear filtering, equalization) with generative AI-based processing. This substitution enables the system to understand and reconstruct complex audio patterns even from highly compressed data, achieving reliable communication quality that traditional methods cannot attain at low bit-rates.
3Device complexity
If traditional encoding and decoding is used, then device complexity is reduced, but audio quality cannot be maintained at low bit-rates
Solution Approach 1:
The system changes the complexity parameter by introducing generative AI models that operate in transformed feature spaces. This allows the system to achieve high audio quality at low bit-rates through sophisticated non-linear transformations rather than traditional linear processing, accepting increased computational complexity for the gain in quality.
Data Source
AI summary
An audio device comprising one or more processors comprising a decoder and a first signal processor, wherein the audio device is configured to: obtain an audio input signal from a transmitter device, where the audio input signal is an encoded audio input signal encoded based on one or more encoder parameters; decode the audio input signal using the decoder and one or more decoder parameters for provision of a decoder output signal; obtain codec information indicative of the one or more encoder parameters and/or the one or more decoder parameters; and process, using the first signal processor and based on the codec information, the decoder output signal for provision of a first signal processor output signal, wherein to process the decoder output signal using the first signal processor comprises to process the decoder output signal using the first signal processor by applying the generative model for codec information-based processing.