Abstract:
A recording device records a video and an imaging time, and a voice. Based on the voice, a sound parameter calculator calculates a sound parameter for specifying magnitude of the voice in a monitoring area at the imaging time for each of pixels and for each of certain times. A sound parameter storage unit stores the sound parameter. A sound parameter display controller superimposes a voice heat map on a captured image of the monitoring area and displays the superimposed image on a monitor. At this time, the sound parameter display controller displays the voice heat map based on a cumulative time value of magnitude of the voice, according to designation of a time range.
Abstract:
A sound collecting control apparatus includes: a vehicle stop detector; a noise source direction specifier to specify a direction from the sound collector to a noise source of the vehicle stopped at the predetermined position; a search beam former that forms a plurality of search beams in the direction of the noise source specified by the noise source direction specifier and around the direction of the noise source so as to search for a sound source of a voice of a speaker in the vehicle; a search beam selector that selects a search beam corresponding to the sound source of the voice of the speaker in the vehicle from the plurality of search beams formed by the search beam former; and a directivity former that forms directivity of the sound collected by the sound collector in the direction corresponding to the search beam selected by the search beam selector.
Abstract:
A sound data processing method of a sound data processing device, the sound data processing device including a processing unit configured to acquire sound data of a target by input and to process the sound data, the sound data processing method including: a step of generating, by using acquired normal sound data of the target, simulated abnormal sound data that becomes a simulated abnormal sound of the target; and a step of performing machine learning by using the acquired normal sound data and the generated simulated abnormal sound data as learning sound data, and generating a learning model for determining an abnormal sound of the sound data of the target to perform abnormal sound detection.
Abstract:
A sound data processing method includes acquiring sound data of a target by input. The sound data processing method further includes: generating similar sound data that becomes a similar sound similar to the sound data of the target, based on the sound data of the target; and performing machine learning by using the acquired sound data of the target and the generated similar sound data as learning sound data, and generating a learning model for performing classification determination related to the sound data of the target.
Abstract:
A measurement terminal includes: a storage that stores, for each of one or more inspection objects, setting information including a parameter related to a feature of an inspection and an abnormality; a processor; and a memory having instructions that, when executed by the processor, cause the processor to perform operations. The operations include: acquiring audio data of sound from an inspection object; deriving, based on the setting information of a corresponding one of the one or more inspection objects, a required time for acquiring the audio data of sound from the inspection object to be used for determining a presence or absence of the abnormality in the inspection object; and determining the presence or absence of the abnormality in the inspection object based on the audio data of sound from the inspection object for the derived required time.
Abstract:
An abnormality predicting system includes a processor and a memory having instructions. The instructions, when executed by the at least one processor, cause the at least one processor to execute operations including: inputting processing target data acquired from a target device; storing information related to an abnormality prediction of the processing target data; calculating an abnormality degree of the processing target data; executing processing related to the abnormality prediction including a failure occurrence prediction using a latest abnormality degree transition and a past abnormality degree transition of the processing target data; and generating a display screen for displaying a processing result including an abnormality degree transition and a result of the failure occurrence prediction.