Frame-Based Voice Synthesis for Low-Resource Real-Time Processing
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Solution Overview
Problem
Existing audio processing methods, particularly vocoders based on generative adversarial networks (GANs), are computationally prohibitive for low-resource devices like smartphones and IoT devices due to their sample-by-sample synthesis approach, requiring significant computing power that these devices lack.
Innovation Solution
Implementing a frame-based processing architecture using gated recurrent units (GRUs) and fully-connected layers to generate voice synthesis in a framewise manner, reducing computational complexity while maintaining quality, suitable for low-resource devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If vocoders based on GANs synthesize voice signal on a sample-by-sample basis, then audio quality is improved, but computational complexity increases making it prohibitive for low-resource devices
Solution Approach 1:
The patent divides the voice synthesis process into frame-based segments rather than processing sample-by-sample. Each frame contains multiple audio samples, allowing the model to process groups of samples simultaneously. This segmentation reduces computational complexity while maintaining audio quality by capturing temporal dependencies within each frame using convolutional layers and attention mechanisms.
2Manufacturing precision
If vocoders based on GANs are used for high-quality voice synthesis, then audio quality is improved, but processing speed decreases making real-time performance difficult
Solution Approach 1:
The patent implements periodic action by processing audio in fixed-frame intervals rather than continuous sample-by-sample processing. This periodic frame-based approach allows the model to generate multiple audio samples at once, significantly improving processing speed and enabling real-time performance while maintaining high audio quality through the use of residual connections and attention mechanisms within each frame.
3Adaptability or versatility
If GAN-based vocoders are deployed on low-resource devices, then accessibility is improved, but computational power becomes insufficient
Solution Approach 1:
The patent applies parameter changes by modifying the architectural parameters of the GAN vocoder to reduce computational requirements. This includes using frame-based processing with optimized frame sizes, reducing the number of layers while maintaining effectiveness through residual connections, and adjusting attention mechanism parameters. These parameter changes enable deployment on low-resource devices while preserving audio quality and real-time performance capabilities.
Data Source
AI summary
Efficient voice synthesis using frame-based processing may be performed. An audio processing system converts an input speech waveform to an acoustic feature representation, which includes a sequence of frames at a lower resolution than the sampling resolution of the input waveform. The system propagates the acoustic feature representation through GRUs and fully-connected layers, while maintaining the lower resolution. At the end, the system performs a flattening operation on the frames of the final acoustic feature representation to generate an output waveform at a target sampling resolution.