A recording device provides bystander-centric privacy controls for authorizing the storage of a bystander's identifying information (e.g., video or audio recordings of the bystander). Before a recording device can store identifying information of bystanders, the bystanders may indicate to the recording device whether they authorize the storage. If the bystanders do not authorize the storage, the recording device may modify the identifying information captured by sensors, such as a video camera or a microphone, such that the identity of the non-authorizing bystander is not identifiable through the modified identifying information. Thus, bystanders are given increased agency over whether they want to be recorded. Further, if the bystanders do not want to be recorded, sensor data that may identify them is modified by the recording device to prevent unwanted exposure of their identity in recorded content.

Techniques are described in which sensor data is used to determine one or more of background noise or occupancy associated with a passenger cabin of a vehicle. The sensor data, in turn, is used to determine an operating state for one or more components of the vehicle (e.g., pumps, compressors, fans, blowers, etc.) such that an amount of background noise within the passenger cabin is reduced (e.g., when a passenger/occupant is present). In various examples, the operating state of the component may operate in a different, though louder, state (e.g., higher efficiency, greater power, etc.) when an occupant is not present or proximate the component.

Techniques for speech processing using a deep neural network (DNN) based acoustic model front-end are described. A new modeling approach directly models multi-channel audio data received from a microphone array using a first model (e.g., multi-geometry/multi-channel DNN) that is trained using a plurality of microphone array geometries. Thus, the first model may receive a variable number of microphone channels, generate multiple outputs using multiple microphone array geometries, and select the best output as a first feature vector that may be used similarly to beamformed features generated by an acoustic beamformer. A second model (e.g., feature extraction DNN) processes the first feature vector and transforms it to a second feature vector having a lower dimensional representation. A third model (e.g., classification DNN) processes the second feature vector to perform acoustic unit classification and generate text data. The DNN front-end enables improved performance despite a reduction in microphones.

A processing device according to an embodiment includes: a frequency characteristics acquisition unit configured to acquire frequency characteristics of at least one sound pickup signal; a smoothing processing unit configured to perform smoothing processing so as to generate second spectral data smoother than first spectral data based on the frequency characteristics; a first compression unit configured to calculate a first difference value corresponding to a difference between the second spectral data and the first spectral data in a first band, and to compress the second spectral data based on the first difference value; and a filter generation unit configured to generate a filter, based on the second spectral data.

A terrestrial acoustic sensor array for detecting and preventing airspace collision with an unmanned aerial vehicle (UAV) includes a plurality of ground-based acoustic sensor installations, each of the acoustic sensor installations including a sub-array of microphones. The terrestrial acoustic sensor array may further include a processor for detecting an aircraft based on sensor data collected from the microphones of at least one of the plurality of acoustic sensor installations and a network link for transmitting a signal based on the detection of the aircraft to a control system of the UAV.

An electronic device includes one or more microphones that generate audio signals and a wind noise detection subsystem. The electronic device may also include a wind noise reduction subsystem. The wind noise detection subsystem applies multiple wind noise detection techniques to the set of audio signals to generate corresponding indications of whether wind noise is present. The wind noise detection subsystem determines whether wind noise is present based on the indications generated by each detection technique and generates an overall indication of whether wind noise is present. The wind noise reduction subsystem applies one or more wind noise reduction techniques to the audio signal if wind noise is detected. The wind noise detection and reduction techniques may work in multiple domains (e.g., the time, spatial, and frequency domains).

A headphone, headphone system, and speech enhancing method is provided to enhance speech pick-up from the user of a headphone and includes receiving a plurality of signals from a set of microphones and generating a primary signal by array processing the microphone signals to steer a beam toward the user's mouth. A noise reference signal is also derived from one or more microphones via a delay-and-sum technique, and a voice estimate signal is generated by filtering the primary signal to remove components that are correlated to the noise reference signal.

In a microphone authentication method, a sound emitting device sends authentication information to a sound collecting device. The sound collecting device receives the authentication information and sends a collected sound signal to the sound emitting device. The sound emitting device receives the collected sound signal sent from the sound collecting device that has received the authentication information within a partitioned space. The sound emitting device emits a sound based on the collected sound signal.

A lighting device (31) is configured to determine a group of service-providing devices (41-44) and determine a schedule for the service-providing devices in the group. The group (101) comprises at least two service-providing devices capable of performing a similar or a same lighting service. The schedule indicates for each service-providing device of the group in which time period the service-providing device is able to receive messages such that at least one device of the determined group of service-providing devices is able to receive messages. The lighting device is configured to transmit the schedule to the service-providing devices.

Embodiments relate to binaural spatialization of more than two sound sources on two audio channels of an audio system. Sound signals each emitted from a corresponding sound source are collected, and a respective virtual position within an angular range of a sound scene is assigned to each sound source. Multi-source audio signals are generated by panning each sound signal according to the respective virtual position. A first multi-source audio signal is spatialized to a first direction to generate a first left signal and a first right signal. A second multi-source audio signal is spatialized to a second direction to generate a second left signal and a second right signal. A binaural signal is generated using the first left signal, the second left signal, the first right signal, and the second right signal. The binaural signal is such that each sound source appears to originate from its respective virtual position.