5 results about "Auditory event" patented technology

The application discloses a neuromorphic auditory event-based anti-reverberation processing method and system for security scene abnormal voiceprint recognition, which comprises the following steps: acquiring an asynchronous auditory event sequence in a high-reverberation space; anchoring an initial pulse cluster triggered by a direct sound wave and extracting the same as a reference feature; constructing a channel-level dynamic refractory period mask window based on the reference feature and an estimated reverberation duration; determining a newly-arrived pulse cluster similar to the reference feature as a delayed ghost pulse generated by an echo and intercepting and filtering the same within the mask window action period, so as to generate a pure pulse stream; and inputting the pure pulse stream into a pulse neural network for recognition. The application effectively filters out redundant pulses generated by physical echoes from a data source in a native asynchronous pulse domain, avoids high-cost waveform reconstruction, can effectively prevent the subsequent pulse neural network from making a misjudgment and power saturation due to redundant data, improves the accuracy, robustness and real-time performance of the system in the abnormal voiceprint recognition under a high-reverberation environment, and meanwhile, retains the low-power consumption advantage of event-driven sensing.

The audio content (e.g., an audio track, an audio file, an audio signal, etc.) of a content item (e.g., multimedia content, a movie, streaming content, etc.) may be modified to augment and / or include one or more auditory events, such as a sound, a plurality of sounds, a sound effect(s), a voice(s), and / or music.

The invention relates to an auditory event related potential system of a self-adaptive hearing threshold, and the system comprises an acoustic stimulation module which is used for generating an adjustable sound pressure stimulation signal with the carrier frequency of 500-4000 Hz and the modulation frequency of 35-110 Hz; the electroencephalogram acquisition module is used for acquiring electroencephalogram signals of a subject in real time; the neural response detection module is used for performing spectral analysis on the electroencephalogram signals and calculating the signal-to-noise ratio SNR at the modulation frequency, namely SNRgt; when a threshold value is set, judging that neural response exists; the self-adaptive threshold value adjusting module is used for iteratively adjusting the sound pressure level based on the neural response result and determining the sound pressure level of the lowest extraction response as a hearing threshold value; and the ERP parameter calibration module is used for automatically setting an ERP stimulation sound pressure level according to the hearing threshold value. The system is a system which does not need subjective cooperation of a subject, can automatically measure a hearing threshold value and is used for ERP stimulation parameter calibration, so that the problems of subjective stimulation intensity setting, neglect of individual differences, poor data quality, difficulty in being suitable for special crowds and the like in an existing ERP system are solved.

Detection and identification of auditory events in distributed lighting networks is provided. Lighting fixtures or other devices in a distributed lighting network can incorporate an audio sensor (e.g., a microphone) through which auditory events (e.g., air leaks in compressed air systems, high noise events, shots fired, clapping, voice commands, etc.) are detected and measured. Through machine learning (e.g., a convolutional neural network), a type of auditory event can be identified, and action can be taken based on the type of the auditory event, such as to provide notification, alert nearby users, log events, provide sound cancelation, and so on. In some examples, the auditory event can be localized using multiple audio sensors. In some examples, a learning algorithm can fuse information from multiple sensor inputs, such as temperature sensors, cameras, occupancy sensors, light sensors, and so on.

Detection and identification of auditory events in distributed lighting networks is provided. Lighting fixtures or other devices in a distributed lighting network can incorporate an audio sensor (e.g., a microphone) through which auditory events (e.g., air leaks in compressed air systems, high noise events, shots fired, clapping, voice commands, etc.) are detected and measured. Through machine learning (e.g., a convolutional neural network), a type of auditory event can be identified, and action can be taken based on the type of the auditory event, such as to provide notification, alert nearby users, log events, provide sound cancelation, and so on. In some examples, the auditory event can be localized using multiple audio sensors. In some examples, a learning algorithm can fuse information from multiple sensor inputs, such as temperature sensors, cameras, occupancy sensors, light sensors, and so on.