Abstract: Techniques for determining information associated with sounds detected in an environment based on audio data and map data or perception data are discussed herein. A vehicle can use map data and/or perception data to distinguish between multiple audio signals or sounds. A direct source of sound can be distinguished from a reflected source of sound by determining a direction of arrival of sounds and which objects the directions of arrival are associated with in the environment. A reflected sound can be received without receiving a direct sound. Based on the reflected sound and map data or perception data, characteristics of sound in an occluded region of the environment may be determined and used to control the vehicle.

Abstract: An apparatus includes a receiver and an up-mixer. The receiver is configured to receive a bitstream that includes an encoded mid signal and encoded stereo parameter information. The encoded stereo parameter information represents a first value of a stereo parameter and a second value of the stereo parameter. The first value is associated with a first frequency range. The second value is associated with a second frequency range that is distinct from the first frequency range. The up-mixer is configured to perform an up-mix operation on a frequency-domain decoded mid signal generated from the encoded mid signal. A particular value based on the first value and the second value is applied to the frequency-domain decoded mid signal during the up-mix operation.

Abstract: Techniques for utilizing microphone or audio data to detect and responding to low velocity impacts to a system such as an autonomous vehicle. In some cases, the system may be equipped with a plurality of microphones that may be used to detect impacts that fail to register on the data captured by the vehicle's inertial measurement units and may go undetected by the vehicle's perception system and sensors. In one specific example, the perception system of the autonomous vehicle may identify a period of time in which a potential low velocity impact may occur. The autonomous vehicle may then utilize the microphone or audio data associated with the period of time to determine if an impact occurred.

Abstract: An apparatus includes a receiver and a decoder. The receiver is configured to receive a bitstream that includes a first frame and a second frame. The first frame includes a first portion of a mid channel and a first quantized stereo parameter. The second frame includes a second portion of the mid channel and a second quantized stereo parameter. The decoder is configured to generate a first portion of a channel based on the first portion of the mid channel and the first quantized stereo parameter. The decoder is configured to, in response to the second frame being unavailable for decoding operations, estimate the second quantized stereo parameter based on stereo parameters of one or more preceding frames and generate a second portion of the channel based on the estimated second quantized stereo parameter. The second portion of the channel corresponds to a decoded version of the second frame.

Abstract: An apparatus includes a receiver and an up-mixer. The receiver is configured to receive a bitstream that includes an encoded mid signal and encoded stereo parameter information. The encoded stereo parameter information represents a first value of a stereo parameter and a second value of the stereo parameter. The first value is associated with a first frequency range. The second value is associated with a second frequency range that is distinct from the first frequency range. The up-mixer is configured to perform an up-mix operation on a frequency-domain decoded mid signal generated from the encoded mid signal. A particular value based on the first value and the second value is applied to the frequency-domain decoded mid signal during the up-mix operation.

Abstract: Techniques for determining a probability of a false negative associated with a location of an environment are discussed herein. Data from a sensor, such as a radar sensor, can be received that includes point cloud data, which includes first and second data points. The first data point has a first attribute and the second data point has a second attribute. A difference between the first and second attributes is determined such that a frequency distribution may be determined. The frequency distribution may then be used to determine a distribution function, which allows for the determination of a resolution function that is associated with the sensor. The resolution function may then be used to determine a probability of a false negative at a location in an environment. The probability can be used to control a vehicle in a safe and reliable manner.

Abstract: A method includes generating a synthesized non-reference high-band channel based on a non-reference high-band excitation corresponding to a non-reference target channel. The method further includes estimating one or more spectral mapping parameters based on the synthesized non-reference high-band channel and a high-band portion of the non-reference target channel. The method also includes applying the one or more spectral mapping parameters to the synthesized non-reference high-band channel to generate a spectrally shaped synthesized non-reference high-band channel. The method further includes generating an encoded bitstream based on the one or more spectral mapping parameters and the spectrally shaped synthesized non-reference high-band channel.

Abstract: Techniques are discussed herein for analyzing radar data to determine that radar noise from one or more target detections potentially conceals additional objects near the target detection. Determining whether an object may be concealed can be based at least in part on a radar noise level based on a target detection, as well as distributions of radar cross sections and/or doppler data associated with particular object types. For a location near a target detection, a radar system may determine estimated noise levels, and compare the estimated noise levels to radar cross section probabilities associated with object types to determine the likelihood that an object of the object type could be concealed at the location. Based on the analysis, the system may determine a vehicle trajectory or otherwise may control a vehicle based on the likelihood that an object may be concealed at the location.

Abstract: Techniques are discussed herein for analyzing radar data to determine that radar noise from one or more target detections potentially conceals additional objects near the target detection. Determining whether an object may be concealed can be based at least in part on a radar noise level based on a target detection, as well as distributions of radar cross sections and/or doppler data associated with particular object types. For a location near a target detection, a radar system may determine estimated noise levels, and compare the estimated noise levels to radar cross section probabilities associated with object types to determine the likelihood that an object of the object type could be concealed at the location. Based on the analysis, the system may determine a vehicle trajectory or otherwise may control a vehicle based on the likelihood that an object may be concealed at the location.

Abstract: Techniques for determining information associated with sounds detected in an environment based on audio data and map data or perception data are discussed herein. A vehicle can use map data and/or perception data to distinguish between multiple audio signals or sounds. A direct source of sound can be distinguished from a reflected source of sound by determining a direction of arrival of sounds and which objects the directions of arrival are associated with in the environment. A reflected sound can be received without receiving a direct sound. Based on the reflected sound and map data or perception data, characteristics of sound in an occluded region of the environment may be determined and used to control the vehicle.

Abstract: Techniques for adaptive cross-correlation are discussed. A first signal is received from a first audio sensor associated with a vehicle and a second signal is received from a second audio sensor associated with the vehicle. Techniques may include determining, based at least in part on the first signal, a first transformed signal in a frequency domain. Additionally, the techniques include determining, based at least in part on the second signal, a second transformed signal in the frequency domain. A parameter can be determined based at least in part on a characteristic associated with at least one of the vehicle, an environment proximate the vehicle, or one or more of the first or second signal. Cross-correlation data can be determined based at least in part on one or more of the first transformed signal, the second transformed signal, or the parameter.

Abstract: A device includes an encoder configured to generate a first high-band portion of a first signal based on a left signal and a right signal. The encoder is also configured to generate a set of adjustment gain parameters based on a high-band non-reference signal and a synthesized signal. The high-band non-reference signal corresponds to one of a left high-band portion of the left signal or a right high-band portion of the right signal.

Abstract: A device for signal processing includes a memory and a processor. The memory is configured to store a parameter associated with a bandwidth-extended audio stream. The processor is configured to select a plurality of non-linear processing functions based at least in part on a value of the parameter. The processor is also configured to generate a high-band excitation signal based on the plurality of non-linear processing functions.

Abstract: Techniques for determining information associated with sounds detected in an environment based on audio data are discussed herein. Audio sensors of a vehicle may determine audio data associated with sounds from the environment. Sounds may be caused by objects in the environment such as emergency vehicles, construction zones, non-emergency vehicles, humans, audio speakers, nature, etc. A model may determine a classification of the audio data and/or a probability value representing a likelihood that sound in the audio data is associated with the classification. A direction of arrival may be determined based on receiving classification values from multiple audio sensors of the vehicle, and other actions can be performed or the vehicle can be controlled based on the direction of arrival.

Abstract: Techniques for determining a probability of a false negative associated with a location of an environment are discussed herein. Data from a sensor, such as a radar sensor, can be received that includes point cloud data, which includes first and second data points. The first data point has a first attribute and the second data point has a second attribute. A difference between the first and second attributes is determined such that a frequency distribution may be determined. The frequency distribution may then be used to determine a distribution function, which allows for the determination of a resolution function that is associated with the sensor. The resolution function may then be used to determine a probability of a false negative at a location in an environment. The probability can be used to control a vehicle in a safe and reliable manner.

Abstract: Immersive experiences for users are described herein. In an example, audio data from a plurality of audio sensors associated with a vehicle can be received by an audio data processing system. The audio data processing system can combine individual captured audio channels (e.g., from the plurality of audio sensors) into two or more audio channels for output via two or more speakers proximate a user. A first audio channel of the two or more audio channels can be output via a first speaker and second audio channel of the two or more audio channels to be output via a second speaker, wherein output of the first audio channel and the second audio channel causes a resulting sound corresponding to at least a portion of a sound scene associated with the vehicle. In an example, a user computing device operable by the user can receive an input from the user.

Abstract: Techniques for adaptive cross-correlation are discussed. A first signal is received from a first audio sensor associated with a vehicle and a second signal is received from a second audio sensor associated with the vehicle. Techniques may include determining, based at least in part on the first signal, a first transformed signal in a frequency domain. Additionally, the techniques include determining, based at least in part on the second signal, a second transformed signal in the frequency domain. A parameter can be determined based at least in part on a characteristic associated with at least one of the vehicle, an environment proximate the vehicle, or one or more of the first or second signal. Cross-correlation data can be determined based at least in part on one or more of the first transformed signal, the second transformed signal, or the parameter.

Abstract: A device for signal processing includes a memory and a processor. The memory is configured to store a parameter associated with a bandwidth-extended audio stream. The processor is configured to select a plurality of non-linear processing functions based at least in part on a value of the parameter. The processor is also configured to generate a high-band excitation signal based on the plurality of non-linear processing functions.

Abstract: A device includes a receiver and a decoder. The receiver is configured to receive bitstream parameters corresponding to at least an encoded mid signal. The decoder is configured to generate a synthesized mid signal based on the bitstream parameters. The decoder is also configured to generate a synthesized side signal selectively based on the bitstream parameters in response to determining whether the bitstream parameters correspond to an encoded side signal.

Abstract: Techniques for estimating a direction of arrival of sound in an environment are discussed. First and second audio data are received from a first pair of audio sensors associated with a vehicle. A first region of ambiguity associated with the first pair of audio sensors is determined based on the first and second audio data. Third and fourth audio data are received from a second pair of audio sensors. A second region of ambiguity associated with the second pair of audio sensors is determined based on the third and fourth audio data. The regions of ambiguity can be further based on confidence levels associated with sensor or audio data. An area of intersection of the first region of ambiguity and the second region of ambiguity can be determined. A direction of arrival of an audio event can be determined based on the area of intersection.