Audio Enhancement Prediction Model Using GAN Spectrogram Loss
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing audio enhancement techniques fail to effectively address multiple quality issues in audio recordings, such as noise, reverberation, and distortion, and often result in artifacts due to inaccurate phase information and misalignment, which can lead to unrecognizable outputs that do not align with human perception of sound quality.
Innovation Solution
A prediction model trained within a generative adversarial network (GAN) using a combination of spectrogram loss and adversarial loss functions, which learns to map source audios to target audios, effectively enhancing audio recordings by reducing noise, reverberation, and distortion, and maintaining the temporal structure of the original audio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing audio enhancement techniques are used to reduce noise, reverberation, and distortion, then audio quality is improved, but artifacts are introduced and temporal structure is lost resulting in unrecognizable outputs
Solution Approach 1:
The patent replaces traditional mechanical signal processing methods with a neural network-based system. The neural network learns to enhance audio quality by mapping degraded audio to enhanced audio, automatically preserving temporal structure and avoiding artifacts that plague conventional methods. This substitution of mechanical processing with intelligent learning systems resolves the contradiction between quality improvement and structural preservation.
Solution Approach 2:
The patent changes the fundamental parameters of the audio enhancement process by using a neural network that learns optimal enhancement strategies. Instead of fixed mathematical operations, the system adapts parameters dynamically based on the input audio characteristics, enabling simultaneous quality improvement and temporal structure preservation through learned transformations.
2Manufacturing precision
If deep learning is used to map waveform to improved waveform, then noise reduction is achieved, but the system can only address single quality issue and not multiple issues simultaneously
Solution Approach 1:
The patent implements a universal audio enhancement system that can handle multiple quality issues simultaneously. The neural network is designed to process various types of degradation (noise, reverberation, distortion, compression artifacts) in a single unified framework, making the system versatile and adaptable to different audio quality problems without requiring separate specialized models for each issue.
3Reliability
If traditional loss functions are used for training, then model convergence is achieved, but phase information inaccuracy and misalignment cause artifacts in output
Solution Approach 1:
The patent replaces traditional loss functions with a perceptual loss function that incorporates human auditory perception characteristics. This substitution ensures that the model converges while maintaining phase information accuracy by training on perceptually relevant metrics rather than raw mathematical differences, thereby avoiding artifacts caused by phase misalignment.
Solution Approach 2:
The patent changes the training objective by using perceptual loss functions that measure audio quality based on human perception rather than raw signal differences. This parameter change in the loss function design allows the model to learn phase-aligned transformations that sound natural to human ears, resolving the contradiction between convergence and phase accuracy.
Data Source
AI summary
Operations of a method include receiving a request to enhance a new source audio. Responsive to the request, the new source audio is input into a prediction model that was previously trained. Training the prediction model includes providing a generative adversarial network including the prediction model and a discriminator. Training data is obtained including tuples of source audios and target audios, each tuple including a source audio and a corresponding target audio. During training, the prediction model generates predicted audios based on the source audios. Training further includes applying a loss function to the predicted audios and the target audios, where the loss function incorporates a combination of a spectrogram loss and an adversarial loss. The prediction model is updated to optimize that loss function. After training, based on the new source audio, the prediction model generates a new predicted audio as an enhanced version of the new source audio.