Google Inc.

Aren Jansen

Ryan Michael Rifkin

Daniel Ellis

Scott Wisdom

John R Hershey

Sourish Chaudhuri

Elad Eban

Manoj Plakal

Efthymios Tzinis

Shawn Hershey

Malcolm G Slaney

Katherine Irene Chou

Marco Tagliasacchi

Alvin Rishi Rajkomar

Dan Ellis

Beat Gfeller

Joel Eric Shor

Henry George Wei

Kevin I Kilgour

David Parish

Richard Channing Moore

Kasumi Widner

Michael Philip Brenner

Michael Dwight Howell

Dotan Emanuel

Felix De Chaumont Quitry

Ira Shavitt

Omry Tuval

Oran Lang

Ronnie Maor

Richard Channing Moore, Iii

Qingqing Huang

Jiayang Liu

Ratheet Pandya

Alvin Rajkomar

David Michael Parish

Example methods include receiving training data comprising a plurality of audio clips and a plurality of textual descriptions of audio. The methods include generating a shared representation comprising a joint embedding. An audio embedding of a given audio clip is within a threshold distance of a text embedding of a textual description of the given audio clip. The methods include generating, based on the joint embedding, a conditioning vector and training, based on the conditioning vector, a neural network to: receive (i) an input audio waveform, and (ii) an input comprising one or more of an input textual description of a target audio source in the input audio waveform, or an audio sample of the target audio source, separate audio corresponding to the target audio source from the input audio waveform, and output the separated audio corresponding to the target audio source in response to the receiving of the input.

Conditioned separation of arbitrary sounds based on machine learning models

A computer-implemented method for upmixing audiovisual data can include obtaining audiovisual data including input audio data and video data accompanying the input audio data. Each frame of the video data can depict only a portion of a larger scene. The input audio data can have a first number of audio channels. The computer-implemented method can include providing the audiovisual data as input to a machine-learned audiovisual upmixing model. The audiovisual upmixing model can include a sequence-to-sequence model configured to model a respective location of one or more audio sources within the larger scene over multiple frames of the video data. The computer-implemented method can include receiving upmixed audio data from the audiovisual upmixing model. The upmixed audio data can have a second number of audio channels. The second number of audio channels can be greater than the first number of audio channels.

Systems and methods for upmixing audiovisual data

Apparatus and methods related to separation of audio sources are provided. The method includes receiving an audio waveform associated with a plurality of video frames. The method includes estimating, by a neural network, one or more audio sources associated with the plurality of video frames. The method includes generating, by the neural network, one or more audio embeddings corresponding to the one or more estimated audio sources. The method includes determining, based on the audio embeddings and a video embedding, whether one or more audio sources of the one or more estimated audio sources correspond to objects in the plurality of video frames. The method includes predicting, by the neural network and based on the one or more audio embeddings and the video embedding, a version of the audio waveform comprising audio sources that correspond to objects in the plurality of video frames.

Audio-visual separation of on-screen sounds based on machine learning models

The present disclosure provides systems and methods that generating health diagnostic information from an audio recording. A computing system can include a machine-learned health model comprising that includes a sound model trained to receive data descriptive of a patient audio recording and output sound description data. The computing system can include a diagnostic model trained to receive the sound description data and output a diagnostic score. The computing system can include at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed, cause the processor to perform operations. The operations can include obtaining the patient audio recording; inputting data descriptive of the patient audio recording into the sound model; receiving, as an output of the sound model, the sound description data; inputting the sound description data into the diagnostic model; and receiving, as an output of the diagnostic model, the diagnostic score.

System and method for generating diagnostic health information using deep learning and sound understanding

Examples relate to on-device non-semantic representation fine-tuning for speech classification. A computing system may obtain audio data having a speech portion and train a neural network to learn a non-semantic speech representation based on the speech portion of the audio data. The computing system may evaluate performance of the non-semantic speech representation based on a set of benchmark tasks corresponding to a speech domain and perform a fine-tuning process on the non-semantic speech representation based on one or more downstream tasks. The computing system may further generate a model based on the non-semantic representation and provide the model to a mobile computing device. The model is configured to operate locally on the mobile computing device.

Methods and systems for implementing on-device non-semantic representation fine-tuning for speech classification

Generally, the present disclosure is directed to systems and methods that train machine-learned models (e.g., artificial neural networks) to perform perceptual or cognitive task(s) based on biometric data (e.g., brain wave recordings) collected from living organism(s) while the living organism(s) are performing the perceptual or cognitive task(s). In particular, aspects of the present disclosure are directed to a new supervision paradigm, by which machine-learned feature extraction models are trained using example stimuli paired with companion biometric data such as neural activity recordings (e g electroencephalogram data, electrocorticography data, functional near-infrared spectroscopy, and/or magnetoencephalography data) collected from a living organism (e.g., human being) while the organism perceived those examples (e.g., viewing the image, listening to the speech, etc.).

Training machine-learned models for perceptual tasks using biometric data

A computing system can include an embedding model and a clustering model. The computing system input each of the plurality of inputs into the embedding model and receiving respective embeddings for the plurality of inputs as outputs of the embedding model. The computing system can input the respective embeddings for the plurality of inputs into the clustering model and receiving respective cluster assignments for the plurality of inputs as outputs of the clustering model. The computing system can evaluate a clustering loss function that evaluates a first average, across the plurality of inputs, of a respective first entropy of each respective probability distribution; and a second entropy of a second average of the probability distributions for the plurality of inputs. The computing system can modify parameter(s) of one or both of the clustering model and the embedding model based on the clustering loss function.

Minimum-example/maximum-batch entropy-based clustering with neural networks

Methods are provided for generating training triplets that can be used to train multidimensional embeddings to represent the semantic content of non-speech sounds present in a corpus of audio recordings. These training triplets can be used with a triplet loss function to train the multidimensional embeddings such that the embeddings can be used to cluster the contents of a corpus of audio recordings, to facilitate a query-by-example lookup from the corpus, to allow a small number of manually-labeled audio recordings to be generalized, or to facilitate some other audio classification task. The triplet sampling methods may be used individually or collectively, and each represent a respective heuristic about the semantic structure of audio recordings.

Unsupervised learning of semantic audio representations

Implementations disclose filtering wind noises in video content. A method includes receiving video content comprising an audio component and a video component, detecting, by a processing device, occurrence of a wind noise artifact in a segment of the audio component, identifying an intensity of the wind noise artifact, wherein the intensity is based on a signal-to-noise ratio of the wind noise artifact, selecting, by the processing device, a wind noise replacement operation based on the identified intensity of the wind noise artifact, and applying, by the processing device, the selected wind noise replacement operation to the segment of the audio component to remove the wind noise artifact from the segment.

Filtering wind noises in video content

Implementations disclose filtering wind noises in video content. A method includes receiving video content comprising an audio component and a video component, detecting, by a processing device, occurrence of a wind noise artifact in a segment of the audio component, identifying duration of the wind noise artifact and intensity of the wind noise artifact, selecting, by the processing device, a wind noise replacement operation based on the identified duration and intensity of the wind noise artifact, and applying, by the processing device, the selected wind noise replacement operation to the segment of the audio component to remove the wind noise artifact from the segment.

Growing community of inventors

Mountain View, CA, United States of America

Aren Jansen