Adaptive Neural Network Frequency Transform for Audio Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing audio classification systems face reduced accuracy and performance due to the loss of important information during fixed-scale conversion of time domain features to frequency domain features, and hardcoded conversions may not provide an optimal mapping for specific recognition tasks.
Innovation Solution
An adaptive time to frequency domain conversion neural network system that learns an optimal mapping between time domain features and frequency domain features using techniques based on discrete Fourier transforms, allowing for different frequency mappings for various recognition tasks and input signal characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed-scale conversion of time domain features to frequency domain features is used, then the conversion process is simple and fast, but important information is lost and classification accuracy is reduced
Solution Approach 1:
The patent implements dynamic frequency mapping by training a neural network to learn optimal frequency transformations specific to each recognition task, replacing static fixed-scale conversions. The network adapts the frequency domain features dynamically based on task requirements and input signal characteristics, resolving the contradiction between accuracy and complexity by making the conversion process adaptive rather than fixed.
Solution Approach 2:
The patent changes the parameters of the frequency transformation by learning optimal frequency mappings through neural network training. Instead of using fixed scale parameters, the system adjusts frequency domain parameters dynamically to preserve important information while maintaining computational feasibility through learned transformations.
2Adaptability or versatility
If hardcoded frequency mappings are used, then the system is easy to implement, but the mapping is not optimal for specific recognition tasks
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on task-specific data to learn optimal frequency mappings before deployment. This preliminary training phase enables the system to adapt to specific recognition tasks while maintaining ease of implementation through a unified neural network framework that handles both learning and inference.
Solution Approach 2:
The system achieves self-service by automatically learning task-specific frequency mappings through neural network training without requiring manual configuration or hardcoded transformations. The network adapts itself to the specific recognition task by learning from training data, eliminating the need for task-specific manual tuning while maintaining implementation simplicity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for audio processing using neural networks. One of the systems includes multiple neural network layers, wherein the neural network system is configured to receive time domain features of an audio sample and to process the time domain features to generate a neural network output for the audio sample, the plurality of neural network layers comprising: a frequency-transform (F-T) layer that is configured to apply a transformation defined by a set of F-T layer parameters that transforms a window of time domain features into frequency domain features; and one or more other neural network layers having respective layer parameters, wherein the one or more neural network layers are configured to process frequency domain features to generate a neural network output.