Sound source characterization using estimated signal components

Authors

  • Carl Kevin L. Mirhan ⋅ PH Department of Physics, University of San Carlos
  • Renante R. Violanda ⋅ PH Department of Physics, University of San Carlos

Abstract

In signal analysis, the time-frequency representation of a signal is of most importance as it provides an efficient framework for analyzing time-varying, multi-component signals. Performing the Short-time Fourier Transform (STFT) outputs the power spectrogram which maps a one-dimensional signal in time to a two-dimensional function in time as well as frequency with a trade-off in temporal and spectral resolutions. This paper presents a method in overcoming the energy smearing problem brought about by the uncertainty principle known as the Gabor Limit. Done through the utilization of the phase information found in acoustic signals, a trace of the original spectrogram without the resolution trade-off was successfully obtained using this method. The analysis and characterization of acoustic signatures was then carried out using an unsupervised machine learning algorithm. Achieving a silhouette score of 0.8419, the clustering algorithm was successful in characterizing signal trajectories as tonal, impulse-like as well as noise-like components.

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Article ID

SPP-2023-1D-06

Section

Complex Systems and Data Analytics

Published

2023-07-03

How to Cite

[1]
CKL Mirhan and RR Violanda, Sound source characterization using estimated signal components, Proceedings of the Samahang Pisika ng Pilipinas 41, SPP-2023-1D-06 (2023). URL: https://proceedings.spp-online.org/article/view/SPP-2023-1D-06.