Classification of acoustic emissions using modified matching pursuit
EURASIP Journal on Applied Signal Processing
Segmentation of killer whale vocalizations using the Hilbert-Huang transform
EURASIP Journal on Advances in Signal Processing
Towards a New Image-Based Spectrogram Segmentation Speech Coder Optimised for Intelligibility
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
Masking of time-frequency patterns in applications of passive underwater target detection
EURASIP Journal on Advances in Signal Processing - Special issue on advances in signal processing for maritime applications
Multiple fundamental frequency estimation and polyphony inference of polyphonic music signals
IEEE Transactions on Audio, Speech, and Language Processing
A new probabilistic spectral pitch estimator: exact and MCMC-approximate strategies
CMMR'04 Proceedings of the Second international conference on Computer Music Modeling and Retrieval
SPREAD: sound propagation and perception for autonomous agents in dynamic environments
Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation
| Hi-index | 35.68 |
Time-frequency representations (TFRs) are suitable tools for nonstationary signal analysis, but their reading is not straightforward for a signal interpretation task. This paper investigates the use of TFR statistical properties for classification or recognition purposes, focusing on a particular TFR: the spectrogram. From the properties of a stationary process periodogram, we derive the properties of a nonstationary process spectrogram. It leads to transform the TFR to a local statistical features space from which we propose a method of segmentation. We illustrate our matter with first- and second-order statistics and identify the information they, respectively, provide. The segmentation is operated by a region growing algorithm, which does not require any prior knowledge on the nonstationary signal. The result is an automatic extraction of informative subsets from the TFR, which is relevant for the signal understanding. Examples are presented concerning synthetic and real signals.