Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments of the present invention relate to enhancing sound through reverberation matching. In sonic implementations, a first sound recording recorded in a first environment is received. The first sound recording is decomposed to a first clean signal and a first reverb kernel. A second reverb kernel corresponding with a second sound recording recorded in a second environment is accessed, for example, based on a user indication to enhance the first sound recording to sound as though recorded in the second environment. An enhanced sound recording is generated based on the first clean signal and the second reverb kernel. The enhanced sound recording is a modification of the first sound recording to sound as though recorded in the second environment.

Abstract: 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.

Abstract: 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.

Abstract: Pattern identification using convolution is described. In one or more implementations, a representation of a pattern is obtained that is described using data points that include frequency coordinates, time coordinates, and energy values. An identification is made as to whether sound data described using irregularly positioned data points includes the pattern, the identifying including use of a convolution of the frequency or time coordinates to determine correspondence with the representation of the pattern.

Abstract: Online source separation may include receiving a sound mixture that includes first audio data from a first source and second audio data from a second source. Online source separation may further include receiving pre-computed reference data corresponding to the first source. Online source separation may also include performing online separation of the second audio data from the first audio data based on the pre-computed reference data.