Structured Latent Audio Compression for Clean-Noise Separation
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Solution Overview
Problem
Existing machine learning models for audio processing lack efficiency in memory and bandwidth usage due to the need for supervised learning and lack of structured latent spaces that disentangle clean and noise features, leading to inefficient compression and decompression of audio waveforms.
Innovation Solution
A training system that jointly trains an encoder and decoder neural network using unsupervised learning to generate embeddings with structured latent spaces, separating clean and noise features, enabling efficient compression and decompression of audio waveforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning is used for audio processing, then model accuracy can be improved, but memory and bandwidth usage increase
Solution Approach 1:
The latent space is segmented into distinct regions representing clean audio features and noise features. The encoder network separates these features into different dimensions, allowing selective processing and compression. This segmentation enables efficient representation by only transmitting essential clean audio features while discarding or compressing noise features, thereby reducing memory and bandwidth usage while maintaining model accuracy.
Solution Approach 2:
The invention extracts and isolates clean audio features from noisy audio inputs through the encoder network's structured latent space. By taking out only the necessary clean features and separating them from noise components, the system reduces the quantity of data that needs to be stored and transmitted, while preserving the accuracy needed for audio processing tasks.
2Productivity
If structured latent spaces with disentangled features are implemented, then compression efficiency is improved, but model complexity increases
Solution Approach 1:
Different regions of the latent space are assigned different qualities and purposes. The clean feature dimensions are designed with higher precision and are preserved for accurate audio reconstruction, while noise feature dimensions are allowed greater compression or discarding. This local differentiation optimizes compression efficiency by applying different processing strategies to different parts of the feature space, managing model complexity through targeted design rather than uniform processing.
Solution Approach 2:
The invention introduces a structured dimensional organization to the latent space, where different dimensions represent different semantic meanings (clean features vs. noise features). This dimensional restructuring allows the model to efficiently compress data by operating on specific dimensions independently, improving compression efficiency while managing complexity through organized feature representation.
3Measurement precision
If clean and noise features are disentangled in the latent space, then audio reconstruction quality is maintained, but training difficulty increases
Solution Approach 1:
The training process incorporates preliminary actions to guide the encoder network toward creating a structured latent space with disentangled features. By pre-defining the desired structure and providing appropriate loss functions and training objectives, the system facilitates the separation of clean and noise features during training, making the complex task of feature disentanglement more manageable while preserving audio reconstruction quality.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an encoder neural network and a decoder neural network. In one aspect, a method includes obtaining a first initial audio waveform and a first noisy audio waveform, obtaining a second initial audio waveform and a second noisy audio waveform, processing the first noisy audio waveform and the second noisy audio waveform using an encoder neural network, generating a blended embedding by concatenating: (i) clean feature dimensions from an embedding of the first noisy audio waveform, and (ii) noise feature dimensions from an embedding of the second noisy audio waveform, processing the blended embedding using a decoder neural network to generate a reconstructed audio waveform, determining gradients of an objective function; and updating parameter values of the encoder neural network and the decoder neural network using the gradients.