Amazon Technologies, Inc.

Chieh-Chi Kao

Chao Wang

Ming Sun

Qingming Tang

Viktor Rozgic

James G Droppo

Spyridon Matsoukas

Sumit Garg

Rong Chen

Chia-Jung Chang

Qin Zhang

Weiran Wang

Xiuye Chen

Venkata Naga Krishna Chaitanya Puvvada

Sumig Garg

James Garnet Droppo

A system configured to detect custom acoustic events, where the system generates an acoustic event profile for the custom acoustic event based on a natural language description and without a sample of the sound. The system may generate a profile for a new custom sound based on a natural language description provided by the user; for example, a 'microwave beep.' If the system does not have an existing event profile for a microwave beep, the system may ask the user questions to determine whether any existing event profiles are close (e.g., is the sound similar to a “fan,” “alarm,” “appliance beep,” etc.). The system may detect an event that may be a possible match for the custom sound and ask the user to verify whether the detected event corresponds to the custom sound. The system may update the event profile based on the user's response.

Acoustic event detection

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.

An acoustic event detection system may employ self-supervised federated learning to update encoder and/or classifier machine learning models. In an example operation, an encoder may be pre-trained to extract audio feature data from an audio signal. A decoder may be pre-trained to predict a subsequent portion of audio data (e.g., a subsequent frame of audio data represented by log filterbank energies). The encoder and decoder may be trained using self-supervised learning to improve the decoder's predictions and, by extension, the quality of the audio feature data generated by the encoder. The system may apply federated learning to share encoder updates across user devices. The system may fine-tune the classifier to improve inferences based on the improved audio feature data. The system may distribute classifier updates to the user device(s) to update the on-device classifier.

Self-supervised federated learning

A system may include an acoustic event detection component for detecting acoustic events, which may be non-speech sounds. Upon detection of a command to detect a new sound, a device may prompt a user to cause occurrence of the sound one or more times. The acoustic event detection component may then be reconfigured, using audio data corresponding to the occurrences, to detect future occurrences of the event.

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.

Media presence detection

An audio event detection system that processes audio data into audio feature data and processes the audio feature data using pre-configured candidate interval lengths to identify top candidate regions of the feature data that may include an audio event. The feature data from the top candidate regions are then scored by a classifier, where the score indicates a likelihood that the candidate region corresponds to a desired audio event. The scores are compared to a threshold, and if the threshold is satisfied, the top scoring candidate region is determined to include an audio event.

Audio event detection

An audio event detection system that subsamples input audio data using a series of recurrent neural networks to create data of a coarser time scale than the audio data. Data frames corresponding to the coarser time scale may then be upsampled to data frames that match the finer time scale of the original audio data frames. The resulting data frames are then scored with a classifier to determine a likelihood that the individual frames correspond to an audio event. Each frame is then weighted by its score and a composite weighted frame is created by summing the weighted frames and dividing by the cumulative score. The composite weighted frame is then scored by the classifier. The resulting score is taken as an overall score indicating a likelihood that the input audio data includes an audio event.

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Somerville, MA, United States of America

Chieh-Chi Kao