23 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.

The application discloses a kind of automobile air conditioner control method, electronic equipment and storage medium.The automobile air conditioner control method includes: in response to the start request of air conditioner, control condenser cooling fan earlier than or equal to electric compressor start;In response to the stop request of air conditioner, control condenser cooling fan later than or equal to electric compressor stop.The present application is based on the noise masking effect of human hearing, using condenser cooling fan noise as the incremental source of air conditioning system working background noise, by staggering the start-stop time interval of cooling fan and electric compressor, let the cooling fan start in advance, delay closing, increase the background noise in the process of electric compressor start-stop, solve the abrupt feeling generated by noise mutation in the process of electric compressor start-stop.

Apparatuses, systems, and techniques to generate 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, the noise mask is able to handle the temporal domain (e.g., add time to the spatial domain) to improve image quality when rendering images over multiple frames.

The invention relates to a low-speed warning tone generation method and device of an electric vehicle, the vehicle and a storage medium, and the method comprises the steps that the sound style of the low-speed warning tone of the electric vehicle is determined; the whole vehicle frequency response of the electric vehicle and the pedestrian warning indicator frequency response are tested and analyzed, and the main frequency band of the low-speed warning tone is determined; on the basis of the main frequency band, respectively recording in-vehicle noise of the electric vehicle in a low-speed pedestrian warning tone road test and a low-speed pedestrian warning tone-free road test under the condition that the electric vehicle runs at a preset low speed to obtain a frequency range and noise masking data of the in-vehicle road noise, and outputting the in-vehicle road noise after the frequency range and the noise masking data meet preset conditions. And generating a low-speed prompt tone. Therefore, the problems that in the related technology, warning tone generated by an external loudspeaker is transmitted into a cabin through structural sound transmission and air sound transmission ways, so that the sound quality in a vehicle is poor, and the sound pressure level in the vehicle is increased once a self-adaptive control system is used for improving the warning loudness to compensate the external environment noise, and the sound quality is poor are solved. The driving comfort is influenced, and the like.

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 relates to a method and device for improving the dynamic range of analog-to-digital conversion input signals. The method for improving the dynamic range of the analog-to-digital conversion input signal comprises the following steps: dividing an analog audio signal to respectively obtain an amplified analog audio signal and a reduced analog audio signal, and respectively carrying out analog-to-digital conversion on the amplified analog audio signal and the reduced analog audio signal to respectively obtain an amplified digital signal and a reduced digital signal; judging whether the instantaneous amplitude of the amplified digital signal meets a full-scale threshold value or not; if not, adopting the amplified digital signal; if yes, gain compensation is carried out on the amplitude-reduced digital signal, and the digital signal after gain compensation is adopted. According to the method for improving the dynamic range of the analog-to-digital conversion input signal, the input dynamic range of the analog-to-digital converter is effectively expanded, the problems of large signal topping distortion and small signal noise masking are solved, and therefore the overall performance and the tone quality performance in the audio collection process are remarkably improved.

The invention relates to the technical field of music data processing, in particular to a violin playing auxiliary system based on artificial intelligence, and the system comprises an audio collection module which employs a microphone array to capture a violin playing sound field, and generates an original audio stream. A coding adaptation module generates a transmission code stream while extracting side information metadata including a noise masking threshold and a transient signal flag. The feature extraction module outputs a high-dimensional feature vector. The reconstruction module generates reconstructed audio. And the evaluation feedback module performs quantitative evaluation on the reconstructed audio, generates scores of intonation deviation degree, string rubbing depth and overtone purity, feeds the scores back to the coding adaptation module, and dynamically adjusts frequency weighting parameters of the psychological acoustic model. The music interface module outputs reconstructed audio and structured evaluation data. The system drives the coding adaptation module to adaptively optimize a compression strategy through score feedback of the evaluation feedback module, and the reconstruction module optimizes a generation process by using historical evaluation data to form a closed-loop data stream.

The application discloses a speech enhancement method, device and equipment. The method estimates the noise mask of the complex spectrum of the noisy speech by a noise mask prediction model based on complex value calculation. The model uses complex operation to better model the correlation between the amplitude spectrum and the phase spectrum by using prior knowledge, avoids treating the real part and the imaginary part as two unrelated parts for operation respectively like a real value network, generates enhanced speech data of the noisy speech data according to the masking value of the complex spectrum and the complex spectrum of the noisy speech, so that the amplitude spectrum and the phase spectrum can be enhanced at the same time. Therefore, the hearing quality of the enhanced speech can be effectively improved.

The invention discloses a hearing aid fitting method for matching noise reduction and personalized auditory filtering characteristics. The method comprises the following steps of: firstly, measuring an individual hearing filtering characteristic (such as frequency resolution) through a noise masking experiment method and the like so as to refine and simulate an individual hearing contour; and then, combining the audio state subjected to noise reduction processing with the simulated individual auditory contour to predict the actual auditory experience of the user so as to evaluate the hearing aid effect. And finally, based on a predicted auditory experience result, utilizing a reinforcement learning algorithm to drive a strategy network, and cooperatively optimizing noise reduction parameters and hearing aid gain configuration. The process aims to finally improve the auditory experience of the user by continuously adjusting the two types of parameters. Through denoising-gain collaborative optimization, the speech recognition rate and hearing comfort can be improved in a complex noise scene, and the method has higher adaptation precision, higher environmental robustness and wide clinical application value.

A system configured to improve audio processing by performing dereverberation, noise reduction, and residual echo suppression during a communication session. The system may include a deep neural network (DNN) configured to jointly mitigate additive noise, reverberation, and residual echo. The DNN may be a convolutional recurrent network with dense connectivity (CRN-DC) and may be configured to process complex-valued spectrograms corresponding to the isolated audio data and / or estimated echo data generated by during echo cancellation. The DNN may generate a speech mask and / or an ambient noise mask, enabling the device to generate output audio data representing target speech and a variable amount of ambient noise. For example, the device may separately reconstruct the target speech using the speech mask and the background noise using the ambient noise mask, which enables the device to control the amount of ambient noise represented in the output audio data.

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 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 embodiment of the invention provides a noise covering method and device, and relates to the technical field of noise processing. The method comprises the following steps: determining current to-be-covered noise information; generating a white noise control parameter corresponding to the to-be-covered noise information; and outputting white noise based on the white noise control parameter. According to the embodiment of the invention, the white noise is generated and played based on the noise information needing to be masked currently to mask the noise, so that the noise perceived by a user is effectively reduced.

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.