13 results about "Noise masking" patented technology

This disclosure provides methods, devices, and systems for audio signal processing. The present implementations more specifically relate to speech enhancement techniques for separating microphone signals into speech, echo, and noise signals. In some aspects, a speech enhancement system may include a delay estimator and an acoustic echo and noise (AEN) decoupling filter. The delay estimator receives a microphone signal via a microphone and a far-end audio signal for output via a speaker and estimates a reference audio signal based on a delay between the microphone signal and the far-end audio signal. In some aspects, the AEN decoupling filter may determine a speech mask, an echo mask, and a noise mask based on the microphone signal and the reference audio signal and may suppress an echo component and a noise component of the microphone signal based on the determined set of masks.

The application discloses a kind of underwater glider acoustic data processing method and storage medium based on Conformer, belong to underwater acoustic signal processing technical field.The method includes: using MCR-AAD model to the original acoustic data collected by glider Effective signal detection, output the probability of each frame as effective signal, and using double threshold post-processing method to determine effective signal and noise signal;The detected effective signal is input into the causal BGformer noise reduction model, generates and reverses noise mask by audio feature fusion, DCC encoder, Conformer decoder and label embedding module, to obtain the audio result after noise reduction.MCR-AAD model is composed of mel feature extractor, multi-scale depth separable convolution, dynamic position coding, Conformer encoder and multi-task classification module.The application constructs efficient, real-time underwater acoustic data processing scheme, effectively improves the application performance of glider in marine observation and target identification.

A sound noise-shielding device and a masking earphone are provided in this disclosure. Earphones, hearing aids and noise-masking earplugs are integrated. The noise-masking earplugs are used to directly block real sounds, so that for patients with misophonia, stress response caused by sounds received by the earphones can be reduced, and these sounds can be converted into other acceptable sounds by the bone conduction earphones. Sometimes there may be some mixed sounds at the same time, and the computer can't distinguish them because of their fusion. A separate network is used to extract fused audio separately, and a spectrogram of an individual sound is obtained correspondingly and detected. By training the network, a sound recognition model that can accurately detect sound information can be obtained.

The invention provides a method, a device and a system for audio signal processing. The present implementations more particularly relate to speech enhancement techniques for separating microphone signals into speech, echo, and noise signals. In some aspects, a speech enhancement system may include a delay estimator and an acoustic echo and noise (AEN) decoupling filter. The delay estimator receives a microphone signal via the microphone and receives a far-end audio signal for output via the speaker, and estimates a reference audio signal based on a delay between the microphone signal and the far-end audio signal. In some aspects, an AEN decoupling filter may determine a speech mask, an echo mask, and a noise mask based on a microphone signal and a reference audio signal, and may suppress echo and noise components of the microphone signal based on the determined set of masks.

This invention belongs to the field of communication noise detection and processing technology, and relates to a method and system for testing low-noise power supplies using waveforms. The method connects the power supply under test (DUT) and a voice gateway, and powers them on in real time. The voice gateway is connected to an external noise-shielded environment via its voice interface. Within this environment, the voice gateway is modulated to a periodic state of comfortable noise or a dial tone, and the sound is played. The playing sound is acquired and processed, and based on the processed sound and the sound signal waveform, the noise level of the DUT is detected in real time. This transforms uncontrollable human-induced noise into an electrical signal measurable by an oscilloscope, accurately determining whether the power supply meets requirements. In other words, by utilizing a clean environment, the unmeasurable power frequency noise on the telephone line is converted into a differential-mode signal measurable by an oscilloscope through a physical sound isolation process, providing testers with an objective experimental result.

Techniques for enhancing an audio signal are provided herein. In some embodiments, a method for enhancing an audio segment may include receiving an input audio segment to be enhanced. The method may also include generating a feature indicative of an audio content type of the input audio segment. The method may also include generating a feature indicative of the presence of noise in the input audio segment by providing at least the feature indicative of the audio content type to the trained de-noising feature extraction model. The method may also include generating a de-noising mask based on a feature indicating the presence of noise in the input audio segment. The method may also include generating an enhanced audio segment at least by applying a de-noising mask to the input audio segment.

A vehicle noise masking device includes a database configured to store sound sources, a sound source classification unit configured to classify the sound sources into a first sound source group and a second sound source group, a communication unit, a first processing unit configured to analyze components of the noise measurement signals based on the traveling information and classify the noise measurement signals into vehicle-related noise and vehicle-unrelated noise, a second processing unit configured to extract a first masking sound source similar to the vehicle-related noise from the first sound source group, a third processing unit configured to extract a second masking sound source similar to the vehicle-unrelated noise from the second sound source group, and a fourth processing unit configured to generate a third masking sound source using at least one of the first masking sound source and the second masking sound source.

A vehicle noise masking apparatus and method are disclosed. The vehicle noise masking apparatus includes: a database configured to store a sound source; a sound source classification unit configured to classify sound sources into a first sound source group and a second sound source group; a communication unit; a first processing unit configured to analyze a composition of the noise measurement signal based on the driving information and classify the noise measurement signal into vehicle-related noise and vehicle-independent noise, a second processing unit configured to extract a first masking sound source similar to the vehicle-related noise from the first sound source group, a third processing unit configured to extract a second masking sound source similar to the vehicle-independent noise from the second sound source group, and a fourth processing unit configured to extract a second masked sound source similar to the vehicle-independent noise from the second sound source group, and a fourth processing unit configured to generate a third masked sound source using at least one of the first masked sound source and the second masked sound source.

This invention discloses a method and system for denoising magnetotelluric signals based on a self-supervised diffusion model, belonging to the field of magnetotelluric technology. The invention designs the TimeDART diffusion model as a two-stage architecture for constructing a noise classifier model and a diffusion denoising model. The initial input magnetotelluric data first passes through a noise classifier to generate a noise mask to identify target noise regions. Subsequently, the diffusion denoising model based on the diffusion model processes only these noise regions, generating denoised signal segments. Finally, the denoised noise regions are merged with the clean regions of the initial magnetotelluric data. By combining time-frequency analysis and VMD techniques to extract low-frequency trends from the magnetotelluric data, precise noise region localization, differentiated denoising, and smooth fusion can be achieved. While efficiently suppressing noise, it retains the effective signal characteristics to the greatest extent, improving the automation level of noise suppression, signal-to-noise identification accuracy, and denoising accuracy.

The described aspects and implementations enable privacy-respecting detection, separation, and localization of sounds in vehicle environments. The techniques include obtaining, using audio detector(s) of a vehicle, a sound recording that includes a plurality of elemental sounds (ESs) in a driving environment of the vehicle, and processing, using a sound separation model, the sound recording to separate individual ESs of the plurality of ESs. The techniques further include identifying a content of individual ESs and causing a driving path of the vehicle to be modified in view of the identified content of the individual ESs. Further techniques include rendering speech imperceptibly by redacting temporal portions of the speech, using sound recognition models to identify and discard recordings of speech, and driving at speeds that exceed threshold speeds at which speech becomes imperceptible from noise masking.

The invention discloses a target voice extraction method and system and a medium, and belongs to the technical field of multi-mode voice signal processing. Comprising the following steps: giving a mixed voice and a lip video of a target speaker, and extracting a voice logarithmic power spectrum, a cross-channel phase difference and a visual time sequence feature; splicing multi-modal features, inputting the spliced multi-modal features into an improved DPCRN network, estimating a voice mask and deducing a noise mask; calculating a beam forming weight through the covariance matrix and generalized eigenvalue decomposition to obtain beam features of voice and noise; the logarithmic power spectrum, the visual features and the beam features are fused, and high-dimensional representation of voice and noise is constructed; feature mutual exclusion enhancement is realized by using a frame-level cross attention mechanism, residual noise is removed from voice features, and leaked voice is removed from noise features; and finally, an optimization mask is generated through a decoder, and pure target voice is output through spectrum reconstruction. According to the invention, complex noise environment target voice can be accurately separated.

This disclosure relates to spatiotemporal noise masks for image processing, and more particularly to apparatus, systems, and techniques for generating blue noise masks for real-time image rendering and enhancement. In at least one embodiment, a noise mask is generated and applied to one or more images to generate one or more enhanced images for image processing (e.g., real-time image rendering). In at least one embodiment, when images are rendered over multiple frames, the noise mask is capable of processing the temporal domain (e.g., adding time to the spatial domain) to improve image quality.

The invention discloses a vehicle-mounted multi-sensory collaborative interaction method and system based on multi-dimensional audio features, and the method comprises the steps: obtaining a vehicle-mounted original audio source signal, employing a double-flow parallel analysis architecture, extracting the semantic features of lyrics through employing an NLP model, and extracting the acoustic physical features through employing a CNN model; weighted arbitration is carried out on the double-flow features based on the vehicle driving state, and a basic sensory control vector is generated; processing an in-vehicle environment audio signal by using an adaptive noise cancellation algorithm, calculating an environment masking coefficient, and performing dynamic gain correction on a control vector; and finally, in combination with reverse time sequence scheduling and PID closed-loop control, executing units such as fragrance, an atmosphere lamp and a seat vibration motor are driven to work cooperatively. According to the method, the problem of emotion deficiency of single physical feature control is solved through double-flow fusion, the road noise masking effect is eliminated through environmental perception compensation, and accurate immersive interactive experience is achieved through hardware time sequence synchronization.