TECHNIQUES FOR DETECTING ANOMALIES IN AUDIO RECORDINGS FOR PREDICTIVE MAINTENANCE

Number: 20260287420
Published: 2026-09-24
Filed: 2025-03-21
Assignee: DISNEY ENTERPRISES, INC.
Inventors: PAGANO; Sarah Josephine, PAULSEN; Amber Elizabeth, EATON; Jeremy, MACLEAN; David, WANG; Isabelle Victoria
CPC: G01H3/08; G10L25/18; G10L25/30; G10L25/51
Verdict: Set aside cpc prior 0.44
In edition: 2026-W39
Source: Google Patents · FreePatentsOnline

The keeper's note

Abstract

A computer-implemented technique for detecting audio anomalies includes processing a first frequency representation of a first audio recording using a first trained machine learning model to generate a second frequency representation of at least one voice and a third frequency representation of at least one sound produced by an equipment; detecting, using a second trained machine learning model, an anomaly based on the third frequency representation; and transmitting an alert to at least one computing device based on the anomaly.

Background

BACKGROUND Field of the Various Embodiments

The various embodiments relate generally to computer science, artificial intelligence (AI) and machine learning, and predictive maintenance and, more specifically, to techniques for detecting anomalies in audio recordings for predictive maintenance. Description of the Related Art

Predictive maintenance involves monitoring the performance and condition of in-service equipment in order to estimate when maintenance should be performed on the equipment. By monitoring the performance and condition of in-service equipment, corrective maintenance on the equipment can be better planned with shorter and fewer pauses of service, in turn increasing the availability of the equipment.

In the context of amusement parks, one approach for predictive maintenance of equipment at amusement park attractions is for human operators to listen for unusual sounds being made by the equipment. The drawback of requiring operators to listen for unusual sounds is those operators may not hear unusual sounds at certain locations within an amusement park attraction. In addition, operators may not always understand which sounds are unusual, resulting in inconsistencies in identifying unusual sounds that could indicate the need for maintenance.

Few, if any, effective techniques currently exist for automatically detecting unusual sounds made by equipment at amusement park attractions. One challenge of detecting unusual sounds at amusement p

Claims

1. A computer-implemented method for detecting audio anomalies, the method comprising: processing a first frequency representation of a first audio recording using a first trained machine learning model to generate a second frequency representation of at least one voice and a third frequency representation of at least one sound produced by an equipment; detecting, using a second trained machine learning model, an anomaly based on the third frequency representation; and transmitting an alert to at least one computing device based on the anomaly. || 11. One or more non-transitory computer-readable media storing instructions that, when executed by at least one processor, cause the at least one processor to perform the steps of: processing a first frequency representation of a first audio recording using a first trained machine learning model to generate a second frequency representation of at least one voice and a third frequency representation of at least one sound produced by an equipment; detecting, using a second trained machine learning model, an anomaly based on the third frequency representation; and transmitting an alert to at least one computing device based on the anomaly. || 20. A system, comprising: a memory storing instructions; and one or more processors, that when executing the instructions, are configured to perform the steps of: processing a first frequency representation of a first audio recording using a first trained machine learning model to generate a second frequency representation of at least one voice and a third frequency representation of at least one sound produced by an equipment, detecting, using a second trained machine learning model, an anomaly based on the third frequency representation, and transmitting an alert to at least one computing device based on the anomaly.