Feedback Recurrent Autoencoder Audio Compression
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Solution Overview
Problem
Existing signal encoding methods for audio signals result in reduced quality when aiming for lower bit-rates, leading to inefficient use of resources such as transmission resources and memory usage.
Innovation Solution
A feedback recurrent autoencoder (FRAE) is employed, which includes an encoder and decoder portion with recurrent connections, utilizing the state of the decoder portion to generate compact codes that account for temporal correlations in audio signals, enabling efficient compression without compromising reconstruction quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If encoding is performed to achieve lower bit-rate, then resource efficiency is improved, but audio quality deteriorates
Solution Approach 1:
The audio signal is segmented into sequential frames, with each frame processed independently through the autoencoder. This allows the system to capture temporal correlations between frames while maintaining efficient compression for each segment, resolving the contradiction between compression efficiency and quality preservation.
Solution Approach 2:
The recurrent autoencoder incorporates feedback connections that allow the decoder state to influence subsequent encoding decisions. This feedback mechanism enables the system to maintain audio quality by using decoded information to guide further compression, achieving lower bit-rates without significant quality loss.
2Productivity
If compression ratio is increased, then transmission efficiency is improved, but reconstruction quality deteriorates
Solution Approach 1:
The autoencoder employs dynamic recurrent connections that adaptively adjust the compression representation based on the temporal context of the audio signal. This dynamic approach allows the system to achieve higher compression ratios while maintaining reconstruction quality by optimizing the representation for each sequential frame based on previous frames.
Solution Approach 2:
The system changes the representation parameters of the audio signal through the encoder-decoder transformation, using learned latent representations that capture essential audio characteristics. This parameter transformation enables efficient compression while preserving the ability to reconstruct high-quality audio from the compressed representation.
3Productivity
If recurrent connections are added to capture temporal correlations, then compression efficiency is improved, but system complexity increases
Solution Approach 1:
The recurrent autoencoder uses a unified encoder-decoder architecture where the same neural network structures serve multiple functions: encoding, decoding, and capturing temporal correlations through recurrent connections. This multi-functionality reduces overall system complexity compared to separate specialized components while maintaining compression efficiency.
Data Source
AI summary
A device includes one or more processors configured to generate, at an encoder portion of an autoencoder, first output data at least partially based on first input data and to generate, at a decoder portion or the autoencoder, a representation of the first input data at least partially based on the first output data. The one or more processors are configured to generate, at the encoder portion, second output data based on second input data and first state data and to generate, at the decoder portion, a representation of the second input data based on the second output data and second state data. Each of the first state data and the second state data correspond to the state of the decoder portion resulting from generation of the representation of the first input data. The first and second input data correspond to sequential values of a signal to be encoded.