Machine learning methods for evaluating vehicle conditions

A method for using a trained machine learning (ML) model to detect presence of abnormal transmission noise from audio acquired at least in part during operation of an engine of a vehicle, the method comprising:

• using at least one computer hardware processor to perform:
• obtaining, via at least one communication network,
• a first audio recording that was acquired, using at least one acoustic sensor, at least in part during operation of the engine, and
• metadata indicating one or more properties of the vehicle;
• processing the first audio recording using the trained ML model to detect, from the first audio recording, presence of the abnormal transmission noise, the processing comprising:
• generating an audio waveform from the first audio recording,
• generating a two-dimensional (2D) representation of the audio waveform,
• generating metadata features from the metadata, and
• processing the audio waveform, the 2D representation of the audio waveform, and the metadata features, using the trained ML model to obtain output indicative of presence or absence of the abnormal transmission noise.

A system, comprising:

• at least one computer hardware processor;
• and
• at least one non-transitory computer-readable storage medium storing processor executable instructions that when executed by the at least one computer hardware processor perform a method for using a trained machine learning (ML) model to detect presence of abnormal transmission noise from audio acquired at least in part during operation of an engine of a vehicle, the method comprising:
• obtaining, via at least one communication network,
• a first audio recording that was acquired, using at least one acoustic sensor, at least in part during operation of the engine, and
• metadata indicating one or more properties of the vehicle;
• processing the first audio recording using the trained ML model to detect, from the first audio recording, presence of the abnormal transmission noise, the processing comprising:
• generating an audio waveform from the first audio recording,
• generating a two-dimensional (2D) representation of the audio waveform,
• generating metadata features from the metadata, and
• processing the audio waveform, the 2D representation of the audio waveform, and the metadata features, using the trained ML model to obtain output indicative of presence or absence of the abnormal transmission noise.

A system for detecting presence of abnormal transmission noise from audio acquired at least in part during operation of an engine of a vehicle, the system comprising:

• at least one mobile vehicle diagnostic device (MVDD),
• the MVDD being configured to be coupled to the vehicle,
• the MVDD comprising at least one acoustic sensor and configured to acquire, using the at least one acoustic sensor, a first audio recording at least in part during operation of the engine, and
• the MVDD being configured to transmit the first audio recording;
• at least one mobile device configured to receive the first audio recording from the MVDD and transmit the first audio recording, via at least one communication network, to at least one computing device;
• and
• the at least one computing device, the at least one computing device configured to perform:
• processing the first audio recording using a trained ML model to detect, from the first audio recording, presence of the abnormal transmission noise, the processing comprising:
• generating an audio waveform from the first audio recording,
• generating a two-dimensional (2D) representation of the audio waveform,
• generating metadata features from metadata indicating one or more properties of the vehicle, and
• processing the audio waveform, the 2D representation of the audio waveform, and the metadata features, using the trained ML model to obtain output indicative of presence or absence of the abnormal transmission noise.