TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED (20240296856). AUDIO DATA PROCESSING METHOD AND APPARATUS, DEVICE, STORAGE MEDIUM, AND PROGRAM PRODUCT simplified abstract
Contents
- 1 AUDIO DATA PROCESSING METHOD AND APPARATUS, DEVICE, STORAGE MEDIUM, AND PROGRAM PRODUCT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 AUDIO DATA PROCESSING METHOD AND APPARATUS, DEVICE, STORAGE MEDIUM, AND PROGRAM PRODUCT - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Audio Data Processing
- 1.13 Original Abstract Submitted
AUDIO DATA PROCESSING METHOD AND APPARATUS, DEVICE, STORAGE MEDIUM, AND PROGRAM PRODUCT
Organization Name
TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
Inventor(s)
AUDIO DATA PROCESSING METHOD AND APPARATUS, DEVICE, STORAGE MEDIUM, AND PROGRAM PRODUCT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240296856 titled 'AUDIO DATA PROCESSING METHOD AND APPARATUS, DEVICE, STORAGE MEDIUM, AND PROGRAM PRODUCT
Simplified Explanation
This patent application describes a method for processing audio data to enhance the quality by suppressing noise.
Key Features and Innovation
- Obtaining target audio data frame and historical audio data frames associated with raw audio data.
- Calculating first-order and second-order time derivatives of the target audio data frame.
- Determining a dynamic spectrum feature for the target audio data frame.
- Using a target mask estimation model to obtain a target mask based on various audio data features.
- Applying the target mask to suppress noise in the raw audio data, resulting in enhanced audio quality.
Potential Applications
This technology can be used in audio enhancement systems, noise reduction software, and audio processing applications in various industries such as telecommunications, entertainment, and security.
Problems Solved
This technology addresses the problem of noise interference in audio data, improving the overall quality and clarity of the sound.
Benefits
- Enhanced audio quality with reduced noise interference.
- Improved clarity and intelligibility of audio data.
- Versatile application in different industries and audio processing systems.
Commercial Applications
Enhanced Audio Data Processing for Noise Suppression: Improving audio quality in telecommunications, entertainment, and security systems.
Prior Art
Readers can explore prior art related to audio data processing, noise reduction techniques, and audio enhancement technologies to understand the background of this innovation.
Frequently Updated Research
Researchers may find updated studies on audio signal processing, noise reduction algorithms, and machine learning models for audio enhancement relevant to this technology.
Questions about Audio Data Processing
How does this method compare to traditional noise reduction techniques?
This method combines cepstrum coefficients, time derivatives, and dynamic spectrum features to create a target mask for noise suppression, offering a more comprehensive approach than traditional methods that may focus on specific aspects of noise reduction.
What are the potential limitations of applying this technology to real-time audio processing systems?
Real-time processing may require significant computational resources to calculate cepstrum coefficients, derivatives, and dynamic spectrum features quickly, which could impact the efficiency of the system.
Original Abstract Submitted
this application discloses an audio data processing method. the method includes obtaining a target audio data frame and k historical audio data frames that are associated with raw audio data, and k being a positive integer; in a case that n target cepstrum coefficients of the target audio data frame are obtained, obtaining, m first-order time derivatives and m second-order time derivatives, n being a positive integer greater than 1, and m being a positive integer less than n; obtaining n historical cepstrum coefficients, and determining a dynamic spectrum feature associated with the target audio data frame; and inputting the n target cepstrum coefficients, the m first-order and second-order time derivatives, and the dynamic spectrum feature into a target mask estimation model to obtain a target mask; and applying the target mask to obtain enhanced audio data by suppressing noise data in the raw audio data.
