GN Audio A/S (20240276171). METHOD FOR PROCESSING AUDIO INPUT DATA AND A DEVICE THEREOF simplified abstract
Contents
METHOD FOR PROCESSING AUDIO INPUT DATA AND A DEVICE THEREOF
Organization Name
Inventor(s)
Pejman Mowlaee of Ballerup (DK)
Rasmus Kongsgaard Olsson of Ballerup (DK)
METHOD FOR PROCESSING AUDIO INPUT DATA AND A DEVICE THEREOF - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240276171 titled 'METHOD FOR PROCESSING AUDIO INPUT DATA AND A DEVICE THEREOF
The abstract describes a method for processing audio input data using a neural network associated with different room types to optimize room acoustics.
- The method involves obtaining room response data using a microphone, determining room acoustic metrics based on the data, and selecting a matching neural network for processing the audio data.
- The neural networks are associated with specific room types, each linked to reference room acoustic metrics.
- By comparing the room acoustic metrics with the reference metrics, the method selects the most appropriate neural network for processing the audio data.
Potential Applications: - This technology can be used in smart speakers, headphones, and other audio devices to enhance sound quality based on room acoustics. - It can also be applied in conference call systems to improve audio clarity and reduce background noise.
Problems Solved: - Addresses the challenge of optimizing audio processing based on specific room acoustics. - Improves the overall audio experience by tailoring processing to different room types.
Benefits: - Enhances sound quality and clarity in various environments. - Provides a more personalized audio experience based on room acoustics.
Commercial Applications: Title: "Enhancing Audio Quality with Room-Specific Neural Networks" This technology can be utilized in the consumer electronics industry to develop audio devices that adapt to different room acoustics, providing users with a superior listening experience. Market implications include increased demand for smart audio devices that offer optimized sound quality in any environment.
Prior Art: Researchers and developers in the field of audio signal processing and neural networks have explored similar techniques for optimizing audio based on room acoustics. Relevant patents and academic papers may provide further insights into the evolution of this technology.
Frequently Updated Research: Ongoing research in the fields of audio signal processing, machine learning, and neural networks may lead to advancements in optimizing audio quality based on room acoustics. Stay updated on the latest studies and developments to leverage cutting-edge techniques in this area.
Questions about Room-Specific Neural Networks: 1. How do room-specific neural networks differ from traditional audio processing methods? Room-specific neural networks utilize machine learning algorithms to adapt audio processing to specific room acoustics, enhancing sound quality based on environmental factors.
2. What are the potential challenges in implementing room-specific neural networks in audio devices? Implementing room-specific neural networks may require robust data collection methods and efficient processing algorithms to ensure accurate adaptation to varying room acoustics.
Original Abstract Submitted
a computer-implemented method for processing audio input data into processed audio data by using an audio device comprising a microphone, a processor device and a memory holding a plurality of neural networks is presented. the plurality of neural networks are associated with different room types, wherein each room type is associated with one or more reference room acoustic metrics. the method comprises obtaining, by the microphone, room response data, wherein the room response data is reflecting room acoustics of a room in which the audio device is placed, determining, by using the processor device, the one or more room acoustic metrics based on the room response data, and selecting, by using the processor device, a matching neural network among the plurality of neural networks by comparing the one or more room acoustic metrics with the one or more reference room acoustic metrics associated with the different room types associated with the plurality of neural networks.
