Autoencoder Echo Generation Network for Acoustic Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for acoustic echo cancellation in video conferencing rely heavily on real-world recordings, which are labor-intensive and costly to collect, and require setting up simulation rooms to mimic various environments, limiting efficiency and accuracy.
Innovation Solution
A computer system utilizing an autoencoder and generative adversarial network (GAN) to generate simulated echo recordings, allowing for one-shot learning and reducing the need for extensive training data, thereby improving performance and reducing costs by using a single reference audio recording to extract room information and generate echo embeddings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world recordings are used for training acoustic echo cancellation systems, then training data quality is improved, but data collection cost and time increase significantly
Solution Approach 1:
The patent uses generative adversarial networks to create synthetic echo recordings that copy the statistical properties and characteristics of real-world recordings. The GAN generates training data that mimics real acoustic environments, allowing the system to train on synthesized data rather than requiring extensive manual data collection while maintaining training quality
Solution Approach 2:
The system performs preliminary action by pre-training the autoencoder on available data and then using it to generate synthetic training data before final system training. This preliminary generation of training data reduces the need for extensive real-world data collection while ensuring adequate training material is available
2Adaptability or versatility
If simulation rooms are set up to mimic various environments, then environmental diversity is improved, but system complexity and setup cost increase
Solution Approach 1:
The patent replaces the mechanical approach of physically setting up simulation rooms with different configurations with a computational approach using generative adversarial networks. The GAN learns the characteristics of various acoustic environments from training data and generates synthetic recordings that represent diverse environments without requiring physical simulation chambers
Solution Approach 2:
The system achieves environmental diversity by varying parameters in the synthetic data generation process. The GAN can generate recordings with different acoustic properties, room characteristics, and environmental conditions by adjusting the underlying data distributions, eliminating the need for physical environment changes
3Measurement precision
If extensive training data is collected, then model accuracy is improved, but data processing time and storage requirements increase
Solution Approach 1:
The autoencoder learns efficient representations of acoustic data by copying and reconstructing training examples during the pre-training phase. This compression and representation learning allows the system to capture essential patterns from relatively small datasets, achieving good model accuracy without requiring extensive data processing and storage
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media for generating echo recordings. The system receives, by an autoencoder, an audio signal representation that represents an audio signal and a target echo embedding that comprises information about a target room. The autoencoder comprises an encoder and a decoder. The system generates, by the encoder, a content embedding and an estimated echo embedding. The system generates, by the decoder, an echo recording representation based on the content embedding and the target echo embedding.