# Complex Clipping for Improved Generalization in Machine Learning

## TL;DR

This paper introduces a novel complex clipping method for spectrograms that enhances the generalization ability of machine learning models, especially in noisy acoustic environments, by extending ReLU activation to complex STFT data.

## Contribution

It proposes a simple, regularized complex clipping technique for spectrograms that improves training stability and generalization in deep learning models for audio applications.

## Key findings

- Significant improvement in generalization performance on noisy acoustic data
- Enhanced training stability with the proposed complex clipping method
- Potential applicability to various time-frequency domain applications

## Abstract

For many machine learning applications, a common input representation is a spectrogram. The underlying representation for a spectrogram is a short time Fourier transform (STFT) which gives complex values. The spectrogram uses the magnitude of these complex values, a commonly used detector. Modern machine learning systems are commonly overparameterized, where possible ill-conditioning problems are ameliorated by regularization. The common use of rectified linear unit (ReLU) activation functions between layers of a deep net has been shown to help this regularization, improving system performance. We extend this idea of ReLU activation to detection for the complex STFT, providing a simple-to-compute modified and regularized spectrogram, which potentially results in better behaved training. We then confirmed the benefit of this approach on a noisy acoustic data set used for a real-world application. Generalization performance improved substantially. This approach might benefit other applications which use time-frequency mappings, for acoustic, audio, and other applications.

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Source: https://tomesphere.com/paper/2302.13527