Fast Frequency Estimation by Zero Crossings of Differential Spline Wavelet Transform

Zero crossings or extrema of a wavelet transform constitute important signatures for signal analysis with the advantage of great simplicity. In this paper, we introduce a fast frequency-estimation method based on zero-crossing counting in the transform domain of a family of differential spline wavelets. The resolution and order of the vanishing moments of the chosen wavelets have a close relation with the frequency components of a signal. Theoretical results on estimating the highest and the lowest frequency components are derived, which are particularly useful for frequency estimation of harmonic signals. The results are illustrated with the help of several numerical examples. Finally, we discuss the connection of this approach with other frequency estimation methods, with the high-order level-crossing analysis in statistics, and with the scaling theorem in computer vision.Peer Reviewe

Fast Frequency Estimation by Zero Crossings of Differential Spline Wavelet Transform

Zero crossings or extrema of a wavelet transform constitute important signatures for signal analysis with the advantage of great simplicity. In this paper, we introduce a fast frequency-estimation method based on zero-crossing counting in the transform domain of a family of differential spline wavelets. The resolution and order of the vanishing moments of the chosen wavelets have a close relation with the frequency components of a signal. Theoretical results on estimating the highest and the lowest frequency components are derived, which are particularly useful for frequency estimation of harmonic signals. The results are illustrated with the help of several numerical examples. Finally, we discuss the connection of this approach with other frequency estimation methods, with the high-order level-crossing analysis in statistics, and with the scaling theorem in computer vision.</p

Speech/Music Discrimination: Novel Features in Time Domain

This research aimed to find novel features that can be used to discriminate between speech and music in the time domain for the purpose of data retrieval. The study used speech and music data that were recorded in standard anechoic chambers and sampled at 44.1 kHz. Two types of new features were found and thoroughly examined: the Ratio of Silent Frames (RSF) feature and the Time Series Events (TSE) set of features. The Receiver Operating Characteristics (ROC) curves were used to assess each one of the proposed features as well as certain relevant features from the literature for the purpose of comparison. The RSF feature introduced up to 8% enhancement when compared to a couple of relevant features from the literature. One of the TSE set of features provided close to 100% speech/music discrimination