Robert Boschgmbh

Educational Testing Service

Shabnam Ghaffarzadegan

Zhe Feng

Liu Ren

Samarjit Das

Luca Bondi

Zeng Dai

Benzun Pious Wisely Babu

Lei Chen

Yao Qian

David Suendermann-Oeft

Nanxiang Li

Jidong Tao

Yiwei Sun

Attila Reiss

Taufiq Hasan

Mirko Ruhs

Robert Duerichen

Asif Salekin

A method data augmentation includes receiving audio stream data associated with at least one impulse event, receiving a label associated with the audio stream data, and detecting, using an onset detector, at least one peak of the at least one impulse event. The method also includes extracting at least one positive sample of the audio stream data associated with the at least one impulse event. The method also includes applying, to the at least one positive sample, the label associated with the audio stream data and extracting at least one negative sample of the audio stream data associated with the at least one impulse event. The method also includes augmenting training data based on the at least one positive sample and the at least one negative sample and training at least one machine-learning model using the augmented training data.

Systems and methods for false positive mitigation in impulsive sound detectors

A method includes receiving audio stream data associated with a data capture environment, and receiving sensor data associated with the data capture environment. The method also includes identifying at least some events in the sensor data, and calculating at least one offset value for at least a portion of the audio stream data that corresponds to at least one event of the sensor data. The method also includes synchronizing at least a portion of the sensor data associated with the portion of the audio stream data that corresponds to the at least one event of the sensor data, and labeling at least the portion of the audio stream data that corresponds to the at least one event of the sensor data. The method also includes generating training data using at least some of the labeled portion of the audio stream data, and training a machine learning model using the training data.

Systems and methods for automatic alignment between audio recordings and labels extracted from a multitude of asynchronous sensors in urban settings

A method for labeling audio data includes receiving video stream data and audio stream data that corresponds to at least a portion of the video stream data. The method also includes labeling, at least some objects of the video stream data. The method also includes calculating at least one offset value for at least a portion of the audio stream data that corresponds to at least one labeled object of the video stream data. The method also includes synchronizing at least a portion of the video stream data with the portion of the audio stream data. The method also includes labeling at least the portion of the audio stream data that corresponds to the at least one labeled object of the video stream data and generating training data using at least some of the labeled portion of the audio stream data.

Systems and methods for automatic extraction and alignment of labels derived from camera feed for moving sound sources recorded with a microphone array

A method and system for detecting and localizing a target audio event in an audio clip is disclosed. The method and system use utilizes a hierarchical approach in which a dilated convolutional neural network to detect the presence of the target audio event anywhere in an audio clip based on high level audio features. If the target audio event is detected somewhere in the audio clip, the method and system further utilizes a robust audio vector representation that encodes the inherent state of the audio as well as a learned relationship between state of the audio and the particular target audio event that was detected in the audio clip. A bi-directional long short term memory classifier is used to model long term dependencies and determine the boundaries in time of the target audio event within the audio clip based on the audio vector representations.

System and method for audio event detection in surveillance systems

A method of detecting occupancy in an area includes obtaining, with a processor, an audio sample from an audio sensor and determining, with the processor, feature functional values of a set of selected feature functionals from the audio sample. The determining of the feature functional values includes extracting features in the set of selected feature functionals from the audio sample, and determining the feature functional values of the set of selected features from the extracted features. The method further includes determining, with the processor, occupancy in the area using a classifier based on the determined feature functional values.

System and method for determining occupancy

A helmet includes a transceiver configured to receive vehicle data from one or more sensors located on a vehicle, an inertial measurement unit (IMU) configured to collect helmet motion data of the helmet associated with a rider of the vehicle, and a processor in communication with the transceiver and IMU, and programmed to receive, via the transceiver, vehicle data from the one or more sensors located on the vehicle, determine a gesture in response to the vehicle data from the one or more sensors located on the vehicle and the helmet motion data from the IMU, and output on a display of the helmet a status interface related to the vehicle, in response to the gesture.

System and method of vehicle aware gesture recognition in vehicles with smart helmets

A system for ontology-aware sound classification. The system includes an electronic processor that is configured to create a first graph based on relationships between fine audio classification labels and create a second graph based on relationships between coarse audio classification labels. The electronic processor is also configured to receive an audio clip including one or more sounds, execute a first graph convolutional network with the first graph as input, and execute a second graph convolutional network with the second graph as input. Using the outputs of the first graph convolutional network and the second graph convolutional network, the electronic processor is configured to determine one or more coarse labels, one or more fine labels, or both to classify the one or more sounds in the audio clip.

Ontology-aware sound classification

A computer-program product storing instructions which, when executed by a computer, cause the computer to receive an input data, encode the input via an encoder, during a first sequence, obtain a first latent variable defining an attribute of the input data, generate a sequential reconstruction of the input data utilizing the decoder and at least the first latent variable, obtain a residual between the input data and the reconstruction utilizing a comparison of at least the first latent variable, and output a final reconstruction of the input data utilizing a plurality of residuals from a plurality of sequences.

Apparatus and system for unsupervised disentangled representation learning with a residual variational autoencoder

A system for providing a rider of a saddle-ride vehicle, such as a motorcycle, with information about helmet usage is provided. A camera is mounted to the saddle-ride vehicle and faces the rider and monitor a rider of the vehicle and collect rider image data. A GPS system is configured to detect a location of the saddle-ride vehicle. A controller is in communication with the camera and the GPS system. The controller is configured to receive an image of the ruder from the camera, determine if the rider is wearing a helmet based on the rider image data, and output a helmet-worn indicator to the rider, in which the helmet-worn indicator varies based on the determined location of the saddle-ride vehicle.

Apparatus and warning system for intelligent helmet

A system for end-to-end automated scoring is disclosed. The system includes a word embedding layer for converting a plurality of ASR outputs into input tensors; a neural network lexical model encoder receiving the input tensors; a neural network acoustic model encoder implementing AM posterior probability, word duration, mean value of pitch and mean value of intensity based on a plurality of cues; and a linear regression module, for receiving concatenated encoded features from the neural network lexical model encoder and the neural network acoustic model encoder.

End-to-end neural network based automated speech scoring

A system for monitoring dietary activity of a user includes a wearable device having at least one audio input unit configured to record an audio sample corresponding to audio from a user's neck. The system further includes a processor configured to execute programmed instructions stored in a memory to obtain an audio sample from the audio input unit of a wearable device, determine segmental feature values of a set of selected features from the audio sample by extracting short-term features in the set of selected features from the audio sample and determining the segmental feature values of the set of selected features from the extracted short-term features. The processor is further configured to, using a classifier, classify a dietary activity based on the determined segmental feature values of the audio sample and generate an output corresponding to the classified dietary activity.

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San Mateo, CA, United States of America

Shabnam Ghaffarzadegan