To observe sounds produced by aquatic organisms with less effort, this aquatic organism observation device is made to include an aquatic organism information detection unit for detecting, from sound data that has been acquired using an optical fiber installed in or at the bottom of water and is related to sound at various positions on the optical fiber, a sound emitted by an aquatic organism at a sound data acquisition time and location, and an output unit for outputting aquatic organism information.

Methods, apparatus, systems and articles of manufacture are disclosed to improve detection of audio signatures. An example apparatus includes a TDOA determiner to determine a first time difference of arrival for a first audio sensor of a meter and a second audio sensor of the meter, and a second time difference of arrival for the first audio sensor and a third audio sensor of the meter, a TDOA matcher to determine a match by comparing the first time difference of arrival to a first virtual source time difference of arrival and a second virtual source time difference of arrival, responsive to determining that the first time difference of arrival matches the first virtual source time difference of arrival, identify a first virtual source location as the location of a media presentation device, and remove an audio recording of the second audio sensor to reduce a computational burden on the processor.

Methods, apparatus, systems and articles of manufacture are disclosed to improve detection of audio signatures. An example apparatus includes a TDOA determiner to determine a first time difference of arrival for a first audio sensor of a meter and a second audio sensor of the meter, and a second time difference of arrival for the first audio sensor and a third audio sensor of the meter, a TDOA matcher to determine a match by comparing the first time difference of arrival to a first virtual source time difference of arrival and a second virtual source time difference of arrival, responsive to determining that the first time difference of arrival matches the first virtual source time difference of arrival, identify a first virtual source location as the location of a media presentation device, and remove an audio recording of the second audio sensor to reduce a computational burden on the processor.

The systems, devices, and processes described herein may identify a beam of a voice-controlled device that is directed toward a reflective surface, such as a wall. The beams may be created by a beamformer. An acoustic echo canceller (AEC) may create filter coefficients for a reference sound. The filter coefficients may be analyzed to identify beams that include multiple peaks. The multiple peaks may indicate presence of one or more reflective surfaces. Using the amplitude and the time delay between the peaks, the device may determine that it is close to a reflective surface in a direction of the beam.

The systems, devices, and processes described herein may identify a beam of a voice-controlled device that is directed toward a reflective surface, such as a wall. The beams may be created by a beamformer. An acoustic echo canceller (AEC) may create filter coefficients for a reference sound. The filter coefficients may be analyzed to identify beams that include multiple peaks. The multiple peaks may indicate presence of one or more reflective surfaces. Using the amplitude and the time delay between the peaks, the device may determine that it is close to a reflective surface in a direction of the beam.

Methods, apparatus, systems, and articles of manufacture to determine a location of an audio source are disclosed. Disclosed example apparatus are to determine respective error values for corresponding ones of a first dataset of time difference of arrival (TDOA) distance values between pairs of acoustic sensors, the error values based on a cost function, a first set of constraints and a second set of constraints. Disclosed example apparatus are also to adjust the corresponding ones of the first dataset of TDOA distance values based on the respective error values to determine a second dataset of TDOA distance values. Disclosed example apparatus are further to output the second dataset of TDOA distance values to source location circuitry that is to determine the location of the audio source.

Methods, apparatus, systems, and articles of manufacture to determine a location of an audio source are disclosed. Disclosed example apparatus are to determine respective error values for corresponding ones of a first dataset of time difference of arrival (TDOA) distance values between pairs of acoustic sensors, the error values based on a cost function, a first set of constraints and a second set of constraints. Disclosed example apparatus are also to adjust the corresponding ones of the first dataset of TDOA distance values based on the respective error values to determine a second dataset of TDOA distance values. Disclosed example apparatus are further to output the second dataset of TDOA distance values to source location circuitry that is to determine the location of the audio source.

An apparatus. The apparatus may include a device housing; a camera; a plurality of microphones inside or mounted to the device housing; and a processor inside the device housing and electrically coupled to the plurality of microphones. The processor can be configured to receive a set of audio data from each of the plurality of microphones; execute a machine learning model using each of the sets of audio data as input to determine whether the set of audio data corresponds to an actionable sound; determine a location of the actionable sound relative to a location of the camera based on a plurality of sets of audio data determined to correspond to the actionable sound; and rotate the camera towards the determined location of the actionable sound.

An apparatus. The apparatus may include a device housing; a camera; a plurality of microphones inside or mounted to the device housing; and a processor inside the device housing and electrically coupled to the plurality of microphones. The processor can be configured to receive a set of audio data from each of the plurality of microphones; execute a machine learning model using each of the sets of audio data as input to determine whether the set of audio data corresponds to an actionable sound; determine a location of the actionable sound relative to a location of the camera based on a plurality of sets of audio data determined to correspond to the actionable sound; and rotate the camera towards the determined location of the actionable sound.

A receiving unit is disclosed, including at least three receivers, each configured to receive an ultrasonic signal with a wavelength λ from the transmitting unit. A first receiver is arranged at a distance of at most one half wavelength λ/2 of the ultrasonic signal from a second receiver and from a third receiver. The at least three receivers are arranged in one plane. A processor is configured to determine the respective time-of-flight from the ultrasonic signal received at each of the at least three receivers. The respective time-of-flight is a time that the ultrasonic signal requires from the transmitting unit at a defined start time to the respective receiver. The processor is further configured to determine the three-dimensional position and/or direction of the transmitting unit from the determined times-of-flight and the arrangement of the at least three receivers.