Adobe, Inc.

Mitsubishi Electric Research Laboratories, Inc.

Halliburton Energy Services, Inc.

Wingnut Films Productions Limited

Amazon Technologies, Inc.

Mitsubishi Electric Research Labs.

Paris Smaragdis

Gautham J Mysore

Brian John King

Minje Kim

Bhiksha Ramakrishnan

Nicholas J Bryan

Yinghui Lu

Avinash Vinayak Taware

Emile De La Rey

Petros T Boufounos

Nam Nguyen

Daniel Viassolo

Peter Merrill

Michael Mark Goodwin

David Alan Welsh

Clifford Lloyd Macklin

Madhusudana Shashanka

Wojciech Matusik

Oliver Wang

Sylvain Philippe Paris

Ajay Divakaran

Ce Liu

Kevin William Wilson

Jean-Marc Valin

Yi Yang Ang

Karim Helwani

Michael Casey

Ahmed Mustafa

Jan Buethe

Marco Antonio Martinez Ramirez

Madhusudara Shashanka

Nugroho Perhatianto

Shrikant Venkataramani

Juhan Nam

Stylianos Ioannis Mimilakis

Zhiyao Duan

Ramin Anushiravani

System and methods are provided for estimating the sound field from partial observations. Estimating an acoustic environment for virtual reality and augmented reality applications is a step in the creation of simulated acoustic sound scenes. In particular, the impulse responses of room can be estimated with a generative model. In a teleconferencing scenario with remote participants and a group of participants in a common physical space, giving the remote participants the impression that all other participants are sitting is in the same room acoustically requires filtering the speech of the remote participants with impulse responses estimated at the desired rendering position in the conference room.

Manifold learning for sound field estimation

Systems and methods for audio source separation include receiving an audio input stream including a mixture of audio signals generated from a plurality of audio sources; processing, through a trained audio source separation model, the audio input stream to generate a plurality of audio stems corresponding to one or more of the plurality of audio sources; updating, using a self-iterative processing and training system, the audio source separation model based at least in part on the plurality of audio stems; and re-processing, using the updated trained audio source separation model, the audio input stream to generate a plurality of enhanced audio stems.

Audio source separation systems and methods

Efficient voice synthesis using frame-based processing may be performed. An audio processing system converts an input speech waveform to an acoustic feature representation, which includes a sequence of frames at a lower resolution than the sampling resolution of the input waveform. The system propagates the acoustic feature representation through GRUs and fully-connected layers, while maintaining the lower resolution. At the end, the system performs a flattening operation on the frames of the final acoustic feature representation to generate an output waveform at a target sampling resolution.

Efficient voice synthesis using frame-based processing

Embodiments are disclosed for performing a using a neural network to optimize filter weights of an adaptive filter. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving, by a filter, an input audio signal, wherein the input audio signal is a far-end audio signal, the filter including a transfer function with adaptable filter weights, generating a response audio signal modeling the input audio signal passing through the acoustic environment, receiving a target response signal, including the input audio signal and near-end audio signals, calculating an adaptive filter loss, generating, by a trained recurrent neural network, a filter weight update using the calculated adaptive filter loss, updating the adaptable filter weights of the transfer function to create an updated transfer function, generating an updated response audio signal based on the updated transfer function, and providing the updated response audio signal as an output audio signal.

Meta-learning for adaptive filters

Systems and methods includes receiving a single-track audio input stream having a mixture of audio signals generated from a plurality of sources, training an audio source separation model using, at least in part, the received single-track audio input stream, and separating audio sources, using the audio source separation model, from the audio input stream in accordance with one or more processing recipes to generate a plurality of source separated output stems. The audio separation model is trained to receive the single-track audio input stream and generate a plurality of audio stems corresponding to one or more audio sources of the plurality of sources.

Audio source separation processing workflow systems and methods

Embodiments are disclosed for determining an answer to a query associated with a graphical representation of data. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including an unprocessed audio sequence and a request to perform an audio signal processing effect on the unprocessed audio sequence. The one or more embodiments further include analyzing, by a deep encoder, the unprocessed audio sequence to determine parameters for processing the unprocessed audio sequence. The one or more embodiments further include sending the unprocessed audio sequence and the parameters to one or more audio signal processing effects plugins to perform the requested audio signal processing effect using the parameters and outputting a processed audio sequence after processing of the unprocessed audio sequence using the parameters of the one or more audio signal processing effects plugins.

Deep encoder for performing audio processing

Systems and methods for audio source separation include receiving a single-track audio input sample having an unknown mixture of audio signals generated from a plurality of audio sources, and separating one or more of the audio sources from the single-track audio input sample using a sequential audio source separation model. Separating one or more of the audio sources may include defining a processing recipe comprising a plurality of source separation processes configured to receive an audio input mixture and output one or more separated source signals and a remaining complement signal mixture, and processing the single-track audio input sample in accordance with the processing recipe to generate a plurality of audio stems separated from the unknown mixture of audio signals.

Audio source separation processing pipeline systems and methods

Disclosed are tools, systems, and methods for detecting one or more underground acoustic sources and localizing them in depth and radial distance from a wellbore, for example, for the purpose of finding underground fluid flows, such as may result from leaks in the well barriers. In various embodiments, acoustic-source detection and localization are accomplished with an array of at least three acoustic sensors disposed in the wellbore, in conjunction with array signal processing.

Acoustic array signal processing for flow detection

One example method involves operations for receiving input to transform audio to a target style. Operations further include providing the audio to a predictive model trained to transform the audio into produced audio. Training the predictive model includes accessing representations of audios and unpaired audios. Further, training includes generating feature embeddings by extracting features from representations of an audio and an unpaired audio. The unpaired audio includes a reference production style, and the feature embeddings correspond to their representations. Training further includes generating a feature vector by comparing the feature embeddings using a comparison model. Further, training includes computing prediction parameters using a learned function. The prediction parameters can transform the feature vector into the reference style. Training further includes updating the predictive model with the prediction parameters. In addition, operations include generating the produced audio by modifying audio effects of the audio into the target style.

Audio production assistant for style transfers of audio recordings using one-shot parametric predictions

Described herein are tools, systems, and methods for detecting, classifying, and/or quantifying underground fluid flows based on acoustic signals emanating therefrom, using a plurality of acoustic sensors disposed in the wellbore in conjunction with array signal processing and systematic feature-based classification and estimation methods.

Acoustic downhole leak classification and quantification

Various apparatus or methods are arranged to operate a tool in a wellbore, where the tool has a number of acoustic sensors. Scanning images obtained by the tool sensors along the borehole can be combined to increase the contrast of leak location with respect to background noise. A sequence of beamformed images, generated from signals captured by the tool as it moves over a range of depths of the wellbore, can be acquired. Each beamformed image may overlap at least one other beamformed image of the sequence. The beamformed images can be processed and the processed beamformed images can be combined, forming a stitched image. Additional apparatus, systems, and methods operable in providing stitched images can operate in a variety of applications.

Stitching methods to enhance beamforming results

Time interval sound alignment techniques are described. In one or more implementations, one or more inputs are received via interaction with a user interface that indicate that a first time interval in a first representation of sound data generated from a first sound signal corresponds to a second time interval in a second representation of sound data generated from a second sound signal. A stretch value is calculated based on an amount of time represented in the first and second time intervals, respectively. Aligned sound data is generated from the sound data for the first and second time intervals based on the calculated stretch value.

Time interval sound alignment

In some aspects, errors are replaced within an audio file by receiving a first audio sequence and a second audio sequence. The first audio sequence includes an erroneous subsequence and the second audio sequence includes a corrected subsequence for inclusion in the first audio sequence to replace the erroneous subsequence. The location of the erroneous subsequence in the first audio sequence is determined by applying a suitable matching operation (e.g., dynamic time warping). One or more matching subsequences of the first audio sequence located proximate to the erroneous subsequence in the first audio sequence and matching corresponding subsequences of the second audio sequence are located proximate to the corrected subsequence. A corrected first audio sequence is generated by replacing the erroneous subsequence and a matching subsequence of the first audio sequence with the corrected subsequence and the matching corresponding subsequence of the second audio sequence.

Automatic voiceover correction system

Variable sound decomposition masking techniques are described. In one or more implementations, a mask is generated that incorporates a user input as part of the mask, the user input is usable at least in part to define a threshold that is variable based on the user input and configured for use in performing a sound decomposition process. The sound decomposition process is performed using the mask to assign portions of sound data to respective ones of a plurality of sources of the sound data.

Variable sound decomposition masks

Sound rate modification techniques are described. In one or more implementations, an indication is received of an amount that a rate of output of sound data is to be modified. One or more sound rate rules are applied to the sound data that, along with the received indication, are usable to calculate different rates at which different portions of the sound data are to be modified, respectively. The sound data is then output such that the calculated rates are applied.

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Urbana, IL, United States of America

Paris Smaragdis