Synaptics Incorporated (20240257827). NEURAL TEMPORAL BEAMFORMER FOR NOISE REDUCTION IN SINGLE-CHANNEL AUDIO SIGNALS simplified abstract
Contents
- 1 NEURAL TEMPORAL BEAMFORMER FOR NOISE REDUCTION IN SINGLE-CHANNEL AUDIO SIGNALS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 NEURAL TEMPORAL BEAMFORMER FOR NOISE REDUCTION IN SINGLE-CHANNEL AUDIO SIGNALS - 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 Speech Enhancement Systems
- 1.13 Original Abstract Submitted
NEURAL TEMPORAL BEAMFORMER FOR NOISE REDUCTION IN SINGLE-CHANNEL AUDIO SIGNALS
Organization Name
Inventor(s)
Saeed Mosayyebpour Kaskari of Irvine CA (US)
NEURAL TEMPORAL BEAMFORMER FOR NOISE REDUCTION IN SINGLE-CHANNEL AUDIO SIGNALS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240257827 titled 'NEURAL TEMPORAL BEAMFORMER FOR NOISE REDUCTION IN SINGLE-CHANNEL AUDIO SIGNALS
Simplified Explanation
This patent application describes a system for speech enhancement using multi-frame beamforming with neural network supervision. It involves a linear filter, a deep neural network (DNN), a voice activity detector (VAD), and an IFC calculator to suppress noise in audio signals.
- The system uses a DNN to determine the probability of speech in a current audio frame.
- A VAD then decides if speech is present based on this probability.
- An IFC calculator estimates an IFC vector using the DNN output and VAD indication.
- The linear filter uses the IFC vector to reduce noise in the audio frame.
Key Features and Innovation
- Multi-frame beamforming with neural network supervision for speech enhancement.
- Integration of a DNN, VAD, and IFC calculator for noise suppression.
- Real-time processing of audio signals to improve speech quality.
Potential Applications
- Telecommunications for clearer audio in phone calls.
- Audio recording devices for better sound quality.
- Speech recognition systems for improved accuracy.
Problems Solved
- Noise interference in audio signals.
- Inaccurate speech detection.
- Poor audio quality in communication systems.
Benefits
- Enhanced speech clarity.
- Improved noise reduction.
- Better overall audio quality.
Commercial Applications
Title: Advanced Speech Enhancement System for Telecommunications This technology can be used in:
- Smartphone applications for clearer calls.
- Conference call systems for better audio quality.
- Voice-controlled devices for improved speech recognition.
Prior Art
Research on multi-frame beamforming and neural network supervision in speech enhancement systems can be found in academic journals and patents related to audio signal processing.
Frequently Updated Research
Ongoing studies focus on optimizing neural network models for more accurate speech detection and noise suppression in real-time audio processing.
Questions about Speech Enhancement Systems
How does multi-frame beamforming improve speech quality?
Multi-frame beamforming combines information from multiple audio frames to enhance speech signals, reducing noise and improving clarity.
What are the key components of a speech enhancement system?
A speech enhancement system typically includes a DNN, VAD, IFC calculator, and linear filter to process audio signals for noise suppression and speech enhancement.
Original Abstract Submitted
this disclosure provides methods, devices, and systems for audio signal processing. the present implementations more specifically relate to multi-frame beamforming using neural network supervision. in some aspects, a speech enhancement system may include a linear filter, a deep neural network (dnn), a voice activity detector (vad), and an ifc calculator. the dnn infers a probability of speech (p) in a current frame of a single-channel audio signal based on a neural network model. the vad determines whether speech is present or absent in the current audio frame based on the probability of speech p. the ifc calculator may estimate an ifc vector based on the output of the dnn (such as the probability of speech p) and the output of the vad (such as an indication of whether speech is present in the current frame). the linear filter uses the ifc vector to suppress noise in the current audio frame.
