The disclosed computer-implemented method may include receiving, from a client device, a request for multimedia content, where the request includes both a manifest request that includes client identification data and a license request that includes a license challenge. The method may further include validating the received request for multimedia content using the client identification data in the manifest request and generating a manifest response that includes an identification of a specified multimedia content stream that is to be provided to the client device. The method may also include acquiring at least one license in response to the license request, where the license includes a response to the license challenge having various content keys, and then providing the specified multimedia content stream, including the generated manifest response and the acquired license, to the client device. Various other methods, systems, and computer-readable media are also disclosed.

An advanced acoustic awareness platform configured to classify environmental sounds. The advanced acoustic awareness platform comprises an audio signal acquisition sensor array and one or more processors programmed to augment the audio signal, extract features for the audio signal, visualize, and classify the signal using one or more labels. In at least one embodiment, the signal is classified based on transfer learning associated with a pretrained machine learning model.

Various aspects include wearable audio devices wearable audio devices with a control platform for managing external device interaction. In some particular aspects, a wearable audio device includes: an accessory port; at least one processor; and memory including multiple sets of active noise reduction (ANR) configurations (or more generally, multiple profiles), the memory including instructions executable by the at least one processor, where the instructions are configured to: select a first ANR configuration (or more generally, a first profile) upon powering on the wearable audio device, the selection of the first ANR configuration (or first profile) based on an accessory connected to the accessory port, and automatically switch to a second ANR configuration (or more generally, a second profile) in response to a trigger, where the second ANR configuration (or second profile) is different from the first ANR configuration (or first profile).

A fan blade assembly having integrated microphones to measure acoustic energy levels uses speakers controlled by a control unit to output anti-acoustic energy vibrations. Specific embodiments wherein the speakers and microphones are radially displaced around the fan blade assembly are also disclosed.

The invention relates to a method for airborne-sound acoustic monitoring of an exterior and/or an interior of a vehicle, in which at least one microphone (1) is used to convert airborne sound into an electrical signal (S) and to route it for evaluation purposes to a device for voice and/or sound recognition (2). According to the invention, the electrical signal (S) is subjected to a pre-evaluation in a device for trigger detection (3), and detection of a trigger results in the device for voice and/or sound recognition (2) being moved from an inactive or partially active state to a fully active state by means of the device for trigger detection (3). Further, the invention relates to an apparatus for airborne-sound acoustic monitoring of an exterior and/or an interior of a vehicle and to a vehicle having such an apparatus. The subject matter of the invention is also a computer-readable storage medium.

An unmanned aircraft includes: rotor blades; a duct that shrouds the rotor blades and through which airflow generated by rotation of the rotor blades passes; and a processor that controls rotation of the rotor blades. The height to width ratio of an inner space of the duct in which the rotor blades are shrouded is at least 0.5.

An unmanned aerial vehicle (UAV) may emit masking sounds during operation of the UAV to mask other sounds generated by the UAV during operation. The UAV may be used to deliver items to a residence or other location associated with a customer. The UAV may emit sounds that mask the conventional sounds generated by the propellers and/or motors to cause the UAV to emit sounds that are pleasing to bystanders or do not annoy the bystanders. The UAV may emit sounds using speakers or other sound generating devices, such as fins, reeds, whistles, or other devices which may cause sound to be emitted from the UAV. Noise canceling algorithms may be used to cancel at least some of the conventional noise generated by operation of the UAV using inverted sounds, while additional sound may be emitted by the UAV, which may not be subject to noise cancelation.

A noise cancelation system for an unmanned aerial vehicle may have an audio capture module, a metadata module and a filter. The audio capture module may be configured to receive an audio signal captured from a microphone, e.g., on a camera. The metadata module may be configured to retrieve noise information associated with noise generating components operating on the unmanned aerial vehicle (UAV). The filter may be configured to receive the audio signal and noise information from the audio capture module. The filter also may be configured to retrieve a baseline profile from a database based on the noise information. The baseline profile includes noise parameter to filter out audio frequencies from the audio signal corresponding to the noise generating component. The filter may generate a filtered audio signal for output.

Rotor noise cancellation through the use of mechanical means for a personal aerial drone vehicle. Active noise cancellation is achieved by creating an antiphase amplitude wave by modulation of the propeller blades, by utilizing embedded magnets through an electromagnetic coil encircling the propeller blades. A noise level sensor signals the rotor control system to adjust the frequency of the electromagnetic field surrounding the rotor and control the speed of the rotor. An additional method comprises of incorporating a phase lock loop within the control system configured to determine the frequencies corresponding to the rotors and generate corrective audio signals to achieve active noise cancellation.

A system including a noise analyzer, a filter, and a memory. The noise analyzer configured to review a captured audio signal, wherein the captured audio signal comprises baseline noise parameters and clean audio. The filter configured to remove the baseline noise parameters from the captured audio signal so that the clean audio is free of the baseline noise parameters. The memory stores the clean audio.