Abstract: This application relates to the field of battery technology, and in particular, to a box assembly, a battery module, a battery, and an electrical device. The box assembly may include at least a partition piece and two end plates. In the box assembly disclosed herein, a stress relief piece may be disposed at two ends of a main body of the partition piece. The main body may be connected to the end plate by use of the stress relief piece. A corner may be formed between the stress relief piece and the main body. In this way, when the battery module is subjected to stress, at least a part of the stress can be relieved through the corner formed between the stress relief piece and the main body.

Abstract: This application relates to a method of extracting an inter channel feature from a multi-channel multi-sound source mixed audio signal performed at a computing device.

Abstract: A method for facilitating a remote conference includes receiving a digital video and a computer-readable audio signal. A face recognition machine is operated to recognize a face of a first conference participant in the digital video, and a speech recognition machine is operated to translate the computer-readable audio signal into a first text. An attribution machine attributes the text to the first conference participant. A second computer-readable audio signal is processed similarly, to obtain a second text attributed to a second conference participant. A transcription machine automatically creates a transcript including the first text attributed to the first conference participant and the second text attributed to the second conference participant.

Abstract: The technology of this application relates to a laser microphone, including a diaphragm, a laser device, a control circuit, a self-mixing signal obtaining apparatus, and a signal processing circuit. The laser device is configured to emit light to the diaphragm and receive a feedback light signal from the diaphragm, and the feedback light signal interferes with laser in a resonant cavity of the laser device to obtain a self-mixing light signal. A distance between the laser device and the diaphragm ranges from 30 to 300 m. The control circuit is connected to the laser device, and is configured to drive and control the laser device to emit light. The self-mixing signal obtaining apparatus is connected to the laser device, and is configured to obtain a target voltage signal related to the self-mixing light signal.

Abstract: A method for facilitating a remote conference includes receiving a digital video and a computer-readable audio signal. A face recognition machine is operated to recognize a face of a first conference participant in the digital video, and a speech recognition machine is operated to translate the computer-readable audio signal into a first text. An attribution machine attributes the text to the first conference participant. A second computer-readable audio signal is processed similarly, to obtain a second text attributed to a second conference participant. A transcription machine automatically creates a transcript including the first text attributed to the first conference participant and the second text attributed to the second conference participant.

Abstract: A backlight control circuit is provided, which includes a driver circuit and a power conversion circuit. The driver circuit includes a feedback output terminal and at least one channel port. The channel port is coupled to a first terminal of a light string group. The driver circuit is configured to obtain a voltage of each channel port, and enable the feedback output terminal to provide a current feedback signal based on the voltage Vch. The power conversion circuit is coupled to the feedback output terminal and includes a voltage output terminal. The voltage output terminal is configured to provide a supply voltage for a second terminal of each light string group. The power conversion circuit is configured to perform voltage conversion on an input voltage, and increase or decrease the supply voltage based on the current feedback signal.

Abstract: Computing devices and methods utilizing a joint speaker location/speaker identification neural network are provided. In one example a computing device receives an audio signal of utterances spoken by multiple persons. Magnitude and phase information features are extracted from the signal and inputted into a joint speaker location and speaker identification neural network. The neural network utilizes both the magnitude and phase information features to determine a change in the person speaking. Output comprising the determination of the change is received from the neural network. The output is then used to perform a speaker recognition function, speaker location function, or both.

Abstract: This application relates to a method of extracting an inter channel feature from a multi-channel multi-sound source mixed audio signal performed at a computing device.

Abstract: Examples are disclosed that relate to voice identification enrollment. One example provides a method of voice identification enrollment comprising, during a meeting in which two or more human speakers speak at different times, determining whether one or more conditions of a protocol for sampling meeting audio used to establish human speaker voiceprints are satisfied, and in response to determining that the one or more conditions are satisfied, selecting a sample of meeting audio according to the protocol, the sample representing an utterance made by one of the human speakers. The method further comprises establishing, based at least on the sample, a voiceprint of the human speaker.

Abstract: This disclosure relates to a cloud-local joint or collaborative data analytics framework that provides data analytics models trained and hosted in backend servers for processing data items preprocessed and encrypted by remote terminal devices. The data analytics models are configured to generate encrypted output data items that are then communicated to the local terminal devices for decryption and post-processing. This framework functions without exposing decryption keys of the local terminal devices to the backend servers and the communication network. The encryption/decryption and data analytics in the backend servers are configured to process and communicate data items efficiently to provide real-time or near real-time system response to requests for data analytics from the remote terminal devices.

Abstract: A method for facilitating a remote conference includes receiving a digital video and a computer-readable audio signal. A face recognition machine is operated to recognize a face of a first conference participant in the digital video, and a speech recognition machine is operated to translate the computer-readable audio signal into a first text. An attribution machine attributes the text to the first conference participant. A second computer-readable audio signal is processed similarly, to obtain a second text attributed to a second conference participant. A transcription machine automatically creates a transcript including the first text attributed to the first conference participant and the second text attributed to the second conference participant.

Abstract: This disclosure relates to a cloud-local joint or collaborative data analytics framework that provides data analytics models trained and hosted in backend servers for processing data items preprocessed and encrypted by remote terminal devices. The data analytics models are configured to generate encrypted output data items that are then communicated to the local terminal devices for decryption and post-processing. This framework functions without exposing decryption keys of the local terminal devices to the backend servers and the communication network. The encryption/decryption and data analytics in the backend servers are configured to process and communicate data items efficiently to provide real-time or near real-time system response to requests for data analytics from the remote terminal devices.

Abstract: A method for facilitating a remote conference includes receiving a digital video and a computer-readable audio signal. A face recognition machine is operated to recognize a face of a first conference participant in the digital video, and a speech recognition machine is operated to translate the computer-readable audio signal into a first text. An attribution machine attributes the text to the first conference participant. A second computer-readable audio signal is processed similarly, to obtain a second text attributed to a second conference participant. A transcription machine automatically creates a transcript including the first text attributed to the first conference participant and the second text attributed to the second conference participant.

Abstract: Computing devices and methods utilizing a joint speaker location/speaker identification neural network are provided. In one example a computing device receives an audio signal of utterances spoken by multiple persons. Magnitude and phase information features are extracted from the signal and inputted into a joint speaker location and speaker identification neural network. The neural network utilizes both the magnitude and phase information features to determine a change in the person speaking. Output comprising the determination of the change is received from the neural network. The output is then used to perform a speaker recognition function, speaker location function, or both.

Abstract: A speaker recognition system includes a previously-trained joint neural network. An enrollment machine of the speaker recognition system is configured to operate the previously-trained joint neural network to enroll a new speaker based on audiovisual data featuring the newly enrolled speaker. A recognition machine of the speaker recognition system is configured to operate the previously-trained joint neural network to recognize a previously-enrolled speaker based on audiovisual data featuring the previously-enrolled speaker.

Abstract: Computing devices and methods utilizing a joint speaker location/speaker identification neural network are provided. In one example a computing device receives a multi-channel audio signal of an utterance spoken by a user. Magnitude and phase information features are extracted from the signal and inputted into a joint speaker location/speaker identification neural network that is trained via utterances from a plurality of persons. A user embedding comprising speaker identification characteristics and location characteristics is received from the neural network and compared to a plurality of enrollment embeddings extracted from the plurality of utterances that are each associated with an identity of a corresponding person. Based at least on the comparisons, the user is matched to an identity of one of the persons, and the identity of the person is outputted.

Abstract: A computerized conference assistant includes a camera and a microphone. A face location machine of the computerized conference assistant finds a physical location of a human, based on a position of a candidate face in digital video captured by the camera. A beamforming machine of the computerized conference assistant outputs a beamformed signal isolating sounds originating from the physical location of the human. A diarization machine of the computerized conference assistant attributes information encoded in the beamformed signal to the human.

Abstract: Examples are disclosed that relate to voice identification enrollment. One example provides a method of voice identification enrollment comprising, during a meeting in which two or more human speakers speak at different times, determining whether one or more conditions of a protocol for sampling meeting audio used to establish human speaker voiceprints are satisfied, and in response to determining that the one or more conditions are satisfied, selecting a sample of meeting audio according to the protocol, the sample representing an utterance made by one of the human speakers. The method further comprises establishing, based at least on the sample, a voiceprint of the human speaker.

Abstract: Computing devices and methods utilizing a joint speaker location/speaker identification neural network are provided. In one example a computing device receives a multi-channel audio signal of an utterance spoken by a user. Magnitude and phase information features are extracted from the signal and inputted into a joint speaker location/speaker identification neural network that is trained via utterances from a plurality of persons. A user embedding comprising speaker identification characteristics and location characteristics is received from the neural network and compared to a plurality of enrollment embeddings extracted from the plurality of utterances that are each associated with an identity of a corresponding person. Based at least on the comparisons, the user is matched to an identity of one of the persons, and the identity of the person is outputted.

Abstract: A speaker recognition system includes a previously-trained joint neural network. An enrollment machine of the speaker recognition system is configured to operate the previously-trained joint neural network to enroll a new speaker based on audiovisual data featuring the newly enrolled speaker. A recognition machine of the speaker recognition system is configured to operate the previously-trained joint neural network to recognize a previously-enrolled speaker based on audiovisual data featuring the previously-enrolled speaker.