Machine learning methods for evaluating vehicle conditions

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 vehicle defects 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;
• processing the first audio recording using the trained ML model to detect, from the first audio recording, presence of at least one vehicle defect, the processing comprising:
• generating an audio waveform from the first audio recording,
• generating a two-dimensional (2D) representation of the audio waveform, and
• processing the audio waveform using the trained ML model and processing the 2D representation of the audio waveform using the trained ML model to obtain output indicative of presence or absence of the at least one vehicle defect, wherein the trained ML model comprises:
• a first neural network portion comprising a plurality of one-dimensional (1D) convolutional layers configured to process the audio waveform;
• a second neural network portion comprising a plurality of 2D convolutional layers configured to process the 2D representation of the audio waveform;
• and
• a fusion neural network portion comprising one or more fully connected layers configured to combine outputs produced by the first neural network portion and the second neural network portion to obtain the output indicative of the presence or absence of the at least one vehicle defect.

At least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by a at least one computer hardware processor, cause the at least one processor to perform a method for using a trained machine learning (ML) model to detect presence of vehicle defects 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;
• processing the first audio recording using the trained ML model to detect, from the first audio recording, presence of at least one vehicle defect, the processing comprising:
• generating an audio waveform from the first audio recording,
• generating a two-dimensional (2D) representation of the audio waveform, and
• processing the audio waveform using the trained ML model and processing the 2D representation of the audio waveform using the trained ML model to obtain output indicative of presence or absence of the at least one vehicle defect, wherein the trained ML model comprises:
• a first neural network portion comprising a plurality of one-dimensional (1D) convolutional layers configured to process the audio waveform;
• a second neural network portion comprising a plurality of 2D convolutional layers configured to process the 2D representation of the audio waveform;
• and
• a fusion neural network portion comprising one or more fully connected layers configured to combine outputs produced by the first neural network portion and the second neural network portion to obtain the output indicative of the presence or absence of the at least one vehicle defect.