A signal processing device according to an embodiment includes: two or more microphones each provided with a sound collection unit directed to an outside of a housing including a driver unit, a control unit (102b) that performs a hearing control of sound output from the driver unit to a listener based on sound signals collected and output by the two or more microphones, and an adjustment unit (200) that adjusts a degree of the hearing control between a degree for wind and a degree for non-wind based on a correlation between the sound signals.

A system includes a microphone unit coupled to a roof of an autonomous vehicle. The microphone unit includes a microphone board having a first opening. The microphone unit also includes a first microphone positioned over the first opening and coupled to the microphone board. The microphone unit further includes an accelerometer. The system also includes a processor coupled to the microphone unit.

A method of detecting singing of a user of a personal audio device, the method comprising: receiving a first audio signal comprising bone-conducted speech of the user from a first transducer of the personal audio device; monitoring a second audio signal output to a speaker of the personal audio device; and determining whether the user is singing based on the first audio signal and the second audio signal.

A request for transport services that identifies a rider, an origin, and a destination is received from a client device. Eligibility of the request to be serviced by a vertical take-off and landing (VTOL) aircraft is determined based on the origin and the destination. A transportation system determines a first and a second hub for a leg of the transport request serviced by the VTOL aircraft and calculates a set of candidate routes from the first hub to the second hub. A provisioned route is selected from among the set of candidate routes based on network and environmental parameters and objectives including pre-determined acceptable noise levels, weather, and the presence and planned routes of other VTOL aircrafts along each of the candidate routes.

An adaptive noise canceling (ANC) circuit adaptively generates an anti-noise signal from a reference microphone signal that is injected into the speaker or other transducer output to cause cancellation of ambient audio sounds. An error microphone proximate the speaker provides an error signal. A secondary path estimating adaptive filter estimates the electro-acoustical path from the noise canceling circuit through the transducer so that source audio can be removed from the error signal. Tones in the source audio, such as remote ringtones, present in downlink audio during initiation of a telephone call, are detected by a tone detector using accumulated tone persistence and non-silence hangover counting, and adaptation of the secondary path estimating adaptive filter is halted to prevent adapting to the tones. Adaptation of the adaptive filters is then sequenced so any disruption of the secondary path adaptive filter response is removed before allowing the anti-noise generating filter to adapt.

Acoustic noise reduction (ANR) headphones described herein have current detection circuitry that is used to detect current consumed by ANR circuitry as a result of pressure changes due to a tapping of a headphone, ear or head of a user. Tapping may be performed to change an audio feature or operating mode. The current detection circuitry senses a characteristic of the current that can be used to determine an occurrence of a tap event. Examples of a characteristic include an amplitude, waveform or duration of the sensed current. Advantageously, the ANR headphones avoid the need for control buttons to initiate the desired changes to the audio feature or operating mode. Error detection circuitry included in the ANR headphones can distinguish between a valid tap events and an occurrence of a different type of event that may otherwise be improperly be interpreted as a tap event.

An information handling system presents audio information as audible sounds that include noise cancellation generated in response to environmental noise patterns detected by a microphone. For example, a machine learning model generates noise cancellation for plural environmental noise patterns, such as a baby crying, a dog barking, and a door bell ringing. The noise cancellation engine selectively applies and disables one or more types noise cancellation with the model based upon context at the information handling system, such as an application running on the system, a time of day or other factors.

Various embodiments include a computer-implemented method comprising receiving an input signal representing an ambient auditory environment of a user, generating, from the input signal, a set of ambient audio signals that includes a first component signal and a second component signal, generating, based on the first component signal, a first inverse signal that is a polar inverse of the first component signal, removing the first component signal from the set of ambient audio signals, generating a first composite signal that includes at least the first inverse signal and the second component signal, and driving an audio output device to produce soundwaves based on the first composite signal.

A sound-modulating window assembly, as well as systems and methods of using and operating the same, are disclosed herein. The sound-modulating window assembly can include sensors, a controller, and a sound modulation assembly. The sound-modulating window assembly The sound-modulating window assembly can receive information about one or more sounds in a vehicular environment. Then, the sound-modulating window assembly can produce a modulation profile. The modulation profile can be used by the sound modulation assembly to alter one or more characteristics of the windows, such that the sounds are mitigated or minimized.

A request for transport services that identifies a rider, an origin, and a destination is received from a client device. Eligibility of the request to be serviced by a vertical take-off and landing (VTOL) aircraft is determined based on the origin and the destination. A transportation system determines a first and a second hub for a leg of the transport request serviced by the VTOL aircraft and calculates a set of candidate routes from the first hub to the second hub. A provisioned route is selected from among the set of candidate routes based on network and environmental parameters and objectives including pre-determined acceptable noise levels, weather, and the presence and planned routes of other VTOL aircrafts along each of the candidate routes.