Unsupervised classification of the spectrogram zeros

TL;DR

This paper introduces an unsupervised method to classify spectrogram zeros into three types based on interference origin, using noise-assisted stability analysis, and applies it to denoising signals with promising results.

Contribution

It presents a novel unsupervised classification algorithm for spectrogram zeros and a zero-based denoising method, enhancing signal analysis and noise reduction techniques.

Findings

Effective classification of spectrogram zeros into three types.

Improved denoising performance over existing zero-based methods.

Validated on synthetic and real signals.

Abstract

The zeros of the spectrogram have proven to be a relevant feature to describe the time-frequency structure of a signal, originated by the destructive interference between components in the time-frequency plane. In this work, a classification of these zeros in three types is introduced, based on the nature of the components that interfere to produce them. Echoing noise-assisted methods, a classification algorithm is proposed based on the addition of independent noise realizations to build a 2D histogram describing the stability of zeros. Features extracted from this histogram are later used to classify the zeros using a non-supervised clusterization algorithm. A denoising approach based on the classification of the spectrogram zeros is also introduced. Examples of the classification of zeros are given for synthetic and real signals, as well as a performance comparison of the proposed denoising algorithm with another zero-based approach.