Hybrid Input Machine Learning Framework for Audio and Acceleration Data
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Solution Overview
Problem
Existing hybrid-input prediction systems face inefficiencies and inaccuracies when processing data objects with dissimilar formats, such as audio and acceleration data, as they require extensive resources and struggle to utilize sparse or limited data effectively.
Innovation Solution
The implementation of machine learning models, including FFT layers, one-dimensional convolutional layers, and up-sampling layers, to process audio and acceleration data objects separately and then integrate their features using a feature synthesis model to generate hybrid-input prediction data objects, reducing the need for additional processing power and enhancing data efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional hybrid-input prediction systems process multiple data objects with different formats, then prediction capability is provided, but resource consumption increases and processing efficiency decreases
Solution Approach 1:
The system segments the processing of different data types by applying type-specific machine learning models (audio model for audio data, acceleration model for acceleration data) before feature synthesis. This segmentation allows each data type to be processed optimally without requiring the entire system to handle all formats uniformly, reducing overall computational overhead while maintaining prediction accuracy.
Solution Approach 2:
The patent transforms input data into different parameter representations through specialized processing pipelines. Audio data undergoes FFT and spectrogram transformation, while acceleration data undergoes different feature extraction. These parameter changes optimize the data for subsequent synthesis and prediction, improving efficiency by working with transformed features rather than raw heterogeneous data.
2Reliability
If multiple data objects with dissimilar formats are processed jointly, then comprehensive prediction is achieved, but processing complexity increases
Solution Approach 1:
The processing architecture is segmented into distinct components: type-specific preprocessing models (audio model, acceleration model), a feature synthesis model, and a prediction model. This segmentation reduces complexity by handling each data type independently with specialized models before combining features, rather than using a single complex model to handle all data types simultaneously.
Solution Approach 2:
The feature synthesis model acts as an intermediary between the type-specific processing models and the final prediction model. It consolidates features from multiple data sources into a unified representation, simplifying the interface between heterogeneous input processing and the prediction task, thereby reducing overall system complexity.
3Measurement precision
If extensive processing is applied to utilize sparse data effectively, then prediction accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary feature extraction and transformation on each data type using specialized models before synthesis. Audio data is preprocessed with FFT and spectrogram generation, while acceleration data undergoes separate feature extraction. This preliminary action prepares the data in advance for efficient synthesis and prediction, reducing the computational burden during the actual prediction phase.
Solution Approach 2:
The patent applies parameter transformations (FFT, spectrogram conversion for audio; feature extraction for acceleration) that convert sparse raw data into denser feature representations. These parameter changes enhance the information content of sparse data, improving prediction accuracy without requiring exhaustive processing of the original sparse format.
Data Source
AI summary
There is a need for more accurate and more efficient hybrid-input prediction steps/operations. This need can be addressed by, for example, techniques for efficient joint processing of data objects. In one example, a method includes: processing an audio data object using an audio processing machine learning model to generate an audio-based feature data object, processing an acceleration data object using an acceleration processing machine learning model to generate an acceleration-based feature data object, processing the audio-based feature data object and the acceleration-based feature data object using an feature synthesis machine learning model in order to generate a hybrid-input prediction data object; and performing one or more prediction-based actions based at least in part on the hybrid-input prediction data object.