Abstract: A recording system for a motor vehicle includes a communication interface receiving an audio feed from a telephone. A voice recognition module is communicatively coupled to the communication interface and responds to a command from a human user by converting at least a portion of the audio feed from the telephone into text data, and storing the text data in a memory device.

Abstract: Example techniques involve noise-robust acoustic echo cancellation. An example implementation may involve causing one or more speakers of the playback device to play back audio content and while the audio content is playing back, capturing, via the one or more microphones, audio within an acoustic environment that includes the audio playback. The example implementation may involve determining measured and reference signals in the STFT domain. During each nth iteration of an acoustic echo canceller (AEC): the implementation may involve determining a frame of an output signal by generating a frame of a model signal by passing a frame of the reference signal through an instance of an adaptive filter and then redacting the nth frame of the model signal from an nth frame of the measured signal. The implementation may further involve determining an instance of the adaptive filter for a next iteration of the AEC.

Abstract: The disclosed systems and methods provide a framework for a proactive prediction of the toxic propensity of an article. Prior to the publication and/or reception of comments to online content, the disclosed framework determines the toxic propensity of the content's context and/or specific words, sentences, sentiments, tone or other messages receivable from consumption of the content. Thus, disclosed framework performs proactive forecasting of the content's toxicity propensity”, which quantifies how likely the content is prone to incur or attract toxic comments. The framework can function and/or be configured to operate in a manner that can perform specifically adherent moderation actions that correspond to the content and control how the content can be interacted with, based on the toxic propensity determination, prior to the content's publication in an effort to thwart, prevent or stop toxic environments surrounding or stemming from the content from coming into existence.

Abstract: Diffuse or spatially large audio objects may be identified for special processing. A decorrelation process may be performed on audio signals corresponding to the large audio objects to produce decorrelated large audio object audio signals. These decorrelated large audio object audio signals may be associated with object locations, which may be stationary or time-varying locations. For example, the decorrelated large audio object audio signals may be rendered to virtual or actual speaker locations. The output of such a rendering process may be input to a scene simplification process. The decorrelation, associating and/or scene simplification processes may be performed prior to a process of encoding the audio data.

Abstract: A method for decoding an encoded audio bitstream is disclosed. The method includes receiving the encoded audio bitstream and decoding the audio data to generate a decoded lowband audio signal. The method further includes extracting high frequency reconstruction metadata and filtering the decoded lowband audio signal with an analysis filterbank to generate a filtered lowband audio signal. The method also includes extracting a flag indicating whether either spectral translation or harmonic transposition is to be performed on the audio data and regenerating a highband portion of the audio signal using the filtered lowband audio signal and the high frequency reconstruction metadata in accordance with the flag. The high frequency regeneration is performed as a post-processing operation with a delay of 3010 samples per audio channel.

Abstract: Implementations are directed to determining an audio delay, of a computing device, by causing an audio data stream to be transmitted to the computing device via a wireless communication channel. The computing device causes audio output generated using the audio data stream to be rendered via speaker(s). The rendered audio output is captured via microphone(s), and the audio delay determined by comparing the captured audio output with the audio data stream. A delay audio segment can be appended to an additional audio data stream transmitted to the computing device, where the length of the delay audio segment is determined using the audio delay. A noise reduction technique can additionally or alternatively be adapted based on the audio delay. Implementations are additionally or alternatively directed to determining if an audio data stream transmitted to a computing device for rendering through speaker(s) driven by the computing device—is actually being rendered.

Abstract: This disclosure describes a wide band array that uses wideband beamforming with interference cancellation at multiple independent frequencies and spatial locations and main lobe steering at multiple independent frequencies and spatial locations. One embodiment uses one through N analysis filter bands 1410 coupled to one through N narrowband beamformers 1414 with the output processed through one through N synthesis bands and summed together to produce the full spectrum output signal 1426. Another embodiment uses one through M sensors with Discrete Fourier Transforms (DFT) and one through N frequency bins 1412 coupled to one through N narrowband beamformers 1414 processed through an Inverse DFT to produce the full spectrum output signal 1428. Another embodiment uses one through N sensor subarrays and one through N frequency bands 1406 coupled to one through N narrowband beamformers 1414 summed together to produce the full spectrum output signal 1422.

Abstract: Techniques for configuring device-specific skills as top-level skills are described. When a system receives a user input the system performs NLU processing to determine an intent of the user input. In some instances, the system may identify a device-specific skill associated with the device interacted with by the user. At least partially in parallel to performing NLU processing to determine the intent of the user input, the system may also perform NLU processing to determine a likelihood that the user input corresponds to an intent actionable by the device-specific skill. Once the system has finished NLU processing, the system may implement one or more prioritization rules to determine whether the user input should be sent to the device-specific skill or another skill of the system.

Abstract: An apparatus for estimating an inter-channel time difference between a first channel signal and a second channel signal, includes a signal analyzer for estimating a signal characteristic of the first channel signal or the second channel signal or both signals or a signal derived from the first channel signal or the second channel signal; a calculator for calculating a cross-correlation spectrum for a time block from the first channel signal in the time block and the second channel signal in the time block; a weighter for weighting a smoothed or non-smoothed cross-correlation spectrum to obtain a weighted cross correlation spectrum using a first weighting procedure or using a second weighting procedure depending on a signal characteristic estimated by the signal analyzer, wherein the first weighting procedure is different from the second weighting procedure; and a processor for processing the weighted cross-correlation spectrum to obtain the inter-channel time difference.

Abstract: Techniques for filtering the output of supplemental content are described. When a supplemental output system (e.g., a supplemental content system or notification system) receives supplemental content for output, the supplemental output system sends a user identifier (of the recipient user) and the supplemental content to separately implemented filtering component. The filtering component uses a machine learning (ML) model to determine a topic of the supplemental content. The filtering component determines whether the supplemental content should not be output based on the ML model-determined topic, one or more guardrail policies of the supplemental output system, and user frustration data regarding previously output supplemental content. Use of the ML model to determine the topic prevents a content publisher from surreptitiously associating supplemental content with a specific topic in an effort to bypass topic-based output guardrails.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a tuple of respective input sequences to generate an output. In one aspect, one of the systems includes a neural network comprising a plurality of encoder neural networks and a head neural network, each encoder neural network configured to: receive a respective input sequence from the tuple; process the respective input sequence using one or more encoder network layers to generate an encoded representation comprising a sequence of tokens; and process each of some or all of the tokens in the sequence of tokens using a projection layer to generate a lower-dimensional representation, and the head neural network configured to: receive lower-dimensional representations of a respective proper subset of the sequence of tokens generated by the encoder neural network; and process the lower-dimensional representations to generate the output.

Abstract: A time-domain stereo parameter encoding method and a related product are provided. The time-domain stereo parameter encoding method includes: determining a channel combination scheme for a current frame; determining a time-domain stereo parameter of the current frame based on the channel combination scheme for the current frame; and encoding the determined time-domain stereo parameter of the current frame, where the time-domain stereo parameter includes at least one of a channel combination ratio factor or an inter-channel time difference.

Abstract: Embodiments relate to a system for translating design specifications of an electronic circuit. In one embodiment, the design specification is parsed to identify one or more sentences. From the one or more identified sentences, the system extracts semantic concepts. Additionally, for each sentence of the one or more identified sentences, the system determines whether the sentence is translatable. If a target sentence is translatable, the system generates a parse tree for the target sentence, and generates a probabilistic shift-reduce schedule for the generated parse tree. Using the generated probabilistic shift-reduce schedule and optionally the generated parse tree, the system generates code for verifying the condition specified in the target sentence. In some embodiments, to generate the code, the system parses the target sentence using the generated probabilistic shift-reduce schedule.

Abstract: A method and system for a headset with programmable modes, where the headset comprises a beamforming microphone: sensing an ambient sound level near the headset, and configuring the headset in one of a plurality of modes by configuring a beam pattern of the beamforming microphone based on at least the sensed ambient noise level. A user of the headset may configure the headset in the one of the plurality of modes or it may be automatically configured. The beamforming microphone may comprise an array of sound sensing elements. The headset may be configured in a quiet mode when the sensed ambient sound level is below that of a desired sound source and may be configured with a wide beam pattern. The headset may be configured in a loud mode when the sensed ambient sound level is above that of a desired sound source.

Abstract: A method for channel identification of a multi-channel audio signal comprising X>1 channels is provided. The method comprises the steps of: identifying, among the X channels, any empty channels, thus resulting in a subset of Y?X non-empty channels; determining whether a low frequency effect (LFE) channel is present among the Y channels, and upon determining that an LFE channel is present, identifying the determined channel among the Y channels as the LFE channel; dividing the remaining channels among the Y channels not being identified as the LFE channel into any number of pairs of channels by matching symmetrical channels; and identifying any remaining unpaired channel among the Y channels not being identified as the LFE channel or divided into pairs as a center channel.

Abstract: An overall endpointing measure can be generated based on an audio-based endpointing measure and (1) an accelerometer-based endpointing measure and/or (2) a gaze-based endpointing measure. The overall endpointing measure can be used in determining whether a candidate endpoint is an actual endpoint. Various implementations include generating the audio-based endpointing measure by processing an audio data stream, capturing a spoken utterance of a user, using an audio model. Various implementations additionally or alternatively include generating the accelerometer-based endpointing measure by processing a stream of accelerometer data using an accelerometer model. Various implementations additionally or alternatively include processing an image data stream using a gaze model to generate the gaze-based endpointing measure.

Abstract: A method for generating a language model for text generation by receiving a pre-trained language model having attributes with existing probability distributions over the pre-trained language model; receiving at least one target constraint; the target constraint specifying an expectation of a target attribute over a language model that approximates the pre-trained language model; computing parameters of an energy based model by applying the target constraint to the pre-trained language model; obtaining samples from a reference policy; updating parameters of a target policy using the obtained samples and the energy based model; updating the reference policy with the target policy if the target policy is superior to the reference policy; and outputting the target policy as a target language model. The target language model is adapted to generate text with the target attribute over a probability distribution that approximates the desired probability distribution specified by the target constraint.

Abstract: Audio communication apparatus comprises a set of two or more audio communication nodes; each audio communication node comprising: an audio encoder controlled by encoding parameters to generate encoded audio data to represent a vocal input generated by a user of that audio communication node, the encoded data being agnostic to which user who generated the vocal input; and an audio decoder controlled by decoding parameters to generate a decoded audio signal as a reproduction of a vocal signal generated by a user of another of the audio communication nodes, the decoding parameters being specific to the user of that other of the audio communication nodes.

Abstract: A computer system automatically generates and provides real-time feedback to a healthcare provider about a selected Evaluation and Management (E/M) level to assign to a patient encounter. The system provides the feedback while the healthcare provider is creating the clinical note that documents the patient encounter. The system may also automatically generate and suggest E/M levels to the healthcare provider for approval by the healthcare provider while the healthcare provider is creating the clinical note.

Abstract: A device includes one or more processors configured to obtain sound information from an audio source. The one or more processors are further configured to select, based on a latency criterion associated with a playback device, a compression mode in which a representation of the sound information is compressed prior to transmission to the playback device or a bypass mode in which the representation of the sound information is not compressed prior to transmission to the playback device. The one or more processors are further configured to generate audio data that includes, based on the selected one of the compression mode or the bypass mode, a compressed representation of the sound information or an uncompressed representation of the sound information. The one or more processors are also configured to send the audio data as streaming data, via wireless transmission, to the playback device.