Sound source characterization using estimated signal components


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


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.



Article ID



Complex Systems and Data Analytics



How to Cite

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: