Abstract: An acoustic event detection system may employ one or more recurrent neural networks (RNNs) to extract features from audio data, and use the extracted features to determine the presence of an acoustic event. The system may use self-attention to emphasize features extracted from portions of audio data that may include features more useful for detecting acoustic events. The system may perform self-attention in an iterative manner to reduce the amount of memory used to store hidden states of the RNN while processing successive portions of the audio data. The system may process the portions of the audio data using the RNN to generate a hidden state for each portion. The system may calculate an interim embedding for each hidden state. An interim embedding calculated for the last hidden state may be normalized to determine a final embedding representing features extracted from the input data by the RNN.

Abstract: A multifunctional near-infrared orthodontic accelerator for inhibiting dental plaque formation is provided, including a bottom brace, an outer periodontal brace, an inner periodontal brace, a tooth groove disposed between the outer periodontal brace and the inner periodontal brace, and micro vibrators disposed on a side close to the tooth groove. The outer periodontal brace and the inner periodontal brace are respectively provided with a holding groove therein, which is communicated with openings facing towards the tooth groove. The holding grooves are provided with tooth cleaning bars, which are connected through universal joints and disposed linearly. The tooth cleaning bars are provided with cavities, which are provided with blue light germicidal lamps fitted to inner walls of the cavities. The outer periodontal brace and the inner periodontal brace are equipped with infrared laser lights, and outer peripheries of the tooth cleaning bars are equipped with bristle strips.

Abstract: A system may include a first acoustic event detection (AED) component configured to detect a predetermined set of acoustic events, and include a second AED component configured to detect custom acoustic events that a user configures a device to detect. The first and second AED components are configured to perform task-specific processing, and may receive as input the same acoustic feature data corresponding to audio data that potentially represents occurrence of one or more events. Based on processing by the first and second AED components, a device may output data indicating that one or more acoustic events occurred, where the acoustic events may be a predetermined acoustic event and/or a custom acoustic event.

Abstract: A magnetic-field-adjustable micromagnetic orthodontic accelerator includes a cheek side plate, a tongue side plate and a supporting plate to form a structure of teeth sleeves, cheek side magnets and tongue side magnets are arranged on the cheek side plate and the tongue side plate respectively, the cheek side magnets and the tongue side magnets form a micro magnetic field environment around roots of to-be-moved teeth receiving orthodontic treatment, physiological magnetic field loss caused by traction of the orthodontic accessory is effectively compensated, metabolism and reconstruction of periodontal tissue of the to-be-moved teeth are promoted, and the orthodontic teeth moving process is accelerated.

Abstract: A system may include a first acoustic event detection (AED) component configured to detect a predetermined set of acoustic events, and include a second AED component configured to detect custom acoustic events that a user configures a device to detect. The first and second AED components are configured to perform task-specific processing, and may receive as input the same acoustic feature data corresponding to audio data that potentially represents occurrence of one or more events. Based on processing by the first and second AED components, a device may output data indicating that one or more acoustic events occurred, where the acoustic events may be a predetermined acoustic event and/or a custom acoustic event.

Abstract: A system may include a first acoustic event detection (AED) component configured to detect a predetermined set of acoustic events, and include a second AED component configured to detect custom acoustic events that a user configures a device to detect. The first and second AED components are configured to perform task-specific processing, and may receive as input the same acoustic feature data corresponding to audio data that potentially represents occurrence of one or more events. Based on processing by the first and second AED components, a device may output data indicating that one or more acoustic events occurred, where the acoustic events may be a predetermined acoustic event and/or a custom acoustic event.

Abstract: Described herein is a system for media presence detection in audio. The system analyzes audio data to recognize whether a given audio segment contains sounds from a media source as a way of differentiating recorded media source sounds from other live sounds. In exemplary embodiments, the system includes a hierarchical model architecture for processing audio data segments, where individual audio data segments are processed by a trained machine learning model operating locally, and another trained machine learning model provides historical and contextual information to determine a score indicating the likelihood that the audio data segment contains sounds from a media source.