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Audio Super Resolution

Explore all of Audio Super Resolution’s works! Campaigns, activations & news about the company’s work in Web 3.0, AR, VR, AI & more

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Audio Super Resolution is a technique that involves using deep convolutional neural networks to upscale or increase the sampling rate of audio signals, such as speech or music. By leveraging the power of neural networks, this approach aims to enhance the quality and fidelity of low-resolution or compressed audio. The process involves training neural networks on large datasets of audio samples, teaching them to learn patterns and features that can be used to generate high-resolution versions of the input audio.

These networks utilize complex mathematical operations and deep learning algorithms to predict and reconstruct missing or degraded audio information. The application of Audio Super Resolution has been explored in various research papers and code repositories. Researchers have developed models and algorithms to improve the perceptual quality, clarity, and detail of audio signals, enabling better listening experiences and more accurate audio processing tasks.


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