Nvidia corporation (20240203443). EFFICIENT FREQUENCY-BASED AUDIO RESAMPLING FOR USING NEURAL NETWORKS simplified abstract
Contents
EFFICIENT FREQUENCY-BASED AUDIO RESAMPLING FOR USING NEURAL NETWORKS
Organization Name
Inventor(s)
Suchitra Mandar Jjoshi of Pune (IN)
Mihir Manohar Nyayate of Pune (IN)
Nitin Mahesh Gode of Pune (IN)
EFFICIENT FREQUENCY-BASED AUDIO RESAMPLING FOR USING NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240203443 titled 'EFFICIENT FREQUENCY-BASED AUDIO RESAMPLING FOR USING NEURAL NETWORKS
Simplified Explanation: The patent application describes a method for enhancing audio using machine learning-based audio super-resolution processing. It involves resampling lower frequency audio data to a higher frequency for input to a neural network, which can then perform audio enhancement operations.
- Efficient resampling approach for audio data received at a lower frequency
- Conversion of audio data into the frequency domain using a spectrogram
- Resampling lower frequency data to the target input resolution for the neural network
- Padding upper frequency bands with zero values to keep the resampling process lightweight
- Providing the resampled, higher frequency spectrogram as input to the neural network for audio enhancement operations
Potential Applications: - Audio upscaling in music production - Improving speech clarity in audio recordings - Enhancing sound quality in video editing
Problems Solved: - Enhancing audio quality without distortion - Upsampling audio data efficiently - Improving the overall listening experience
Benefits: - High-quality audio output - Efficient processing of audio data - Enhanced user experience in various applications
Commercial Applications: Title: Enhanced Audio Processing Technology for Music Production and Video Editing This technology can be used in music production studios, video editing software, and speech enhancement tools to improve audio quality and user experience.
Prior Art: Prior research in audio signal processing, machine learning-based audio enhancement, and spectrogram analysis can provide insights into similar technologies and approaches.
Frequently Updated Research: Ongoing research in machine learning algorithms for audio processing, advancements in audio super-resolution techniques, and improvements in neural network architectures for audio enhancement are relevant to this technology.
Questions about Audio Enhancement Technology: 1. How does this technology compare to traditional audio upscaling methods? 2. What are the potential limitations of using machine learning for audio super-resolution processing?
Original Abstract Submitted
systems and methods described relate to the enhancement of audio, such as through machine learning-based audio super-resolution processing. an efficient resampling approach can be used for audio data received at a lower frequency than is needed for an audio enhancement neural network. this audio data can be converted into the frequency domain using, and once in the frequency domain (e.g., represented using a spectrogram) this lower frequency data can be resampled to provide a frequency-based representation that is at the target input resolution for the neural network. to keep this resampling process lightweight, the upper frequency bands can be padded with zero value entries (or other such padding values). this resampled, higher frequency spectrogram can be provided as input to the neural network, which can perform an enhancement operation such as audio upsampling or super-resolution.
