We introduce the use of DCTNet, an efficient approximation and alternative to PCANet, for acoustic signal classification. In PCANet, the eigenfunctions of the local sample covariance matrix (PCA) are used as filterbanks for convolution and feature extraction. When the eigenfunctions are well approximated by the Discrete Cosine Transform (DCT) functions, each layer of of PCANet and DCTNet is ess...

Signal processing methods have been developed over the last 60 years to detect and analyse complicated non-stationary signals, such as speech and seismic activity. The traditional method for analysing such signals is through a spectrogram based on the short-time Fourier transform (STFT). However, the STFT is not ideal since it reflects only the stationary properties contained in any short time-...

Magnetoencephalography (MEG) has a high temporal resolution well-suited for studying perceptual learning. However, to identify where learning happens in the brain, one needs to apply source localization techniques to project MEG sensor data into brain space. Previous source localization methods, such as the short-time Fourier transform (STFT) method by Gramfort et al.([6]) produced intriguing r...

Leaf identification is a challenging research. So far, many approaches have been proposed. In this paper, an approach that combines Fourier descriptors with other shape features was investigated to identify 100 hundred kinds of leaves. The result shows that the combination of Fourier descriptors and several other shape features can be used to identify leaves with the accuracy rate of 88%. This ...

Time-frequency techniques constitutes a major improvement in signal analysis, namely at the field of biomedical signals in which the interdisciplinary nature of the proposed questions implies the development of new strategies to answer to specific problems. Timefrequency analysis using Wavelets, Wigner-Ville transform and more recently the HilbertHuang Transform (HHT) constitutes the core of th...

We consider the problem of phase retrieval from magnitudes short-time Fourier transform (STFT) measurements. It is well-known that signals are uniquely determined (up to global phase) by their STFT magnitude when underlying window has an ambiguity function nowhere vanishing. less clear, however, what can be said in terms unique phase-retrievability vanishes on some time-frequency plane. In this...

The short-time Fourier transform (STFT) based spectrogram is commonly used to analyze the time-frequency content of a signal. Depending on window size, the STFT provides a trade-off between time and frequency resolutions. This paper presents a novel method that achieves high resolution simultaneously in both time and frequency. We extend Probabilistic Latent Component Analysis (PLCA) to jointly...

Phase vocoder approaches to time-scale modification of audio introduce a reverberant/phasy artifact into the time-scaled output due to a loss in phase coherence between short-time Fourier transform (STFT) bins. Recent improvements to the phase vocoder have reduced the presence of this artifact, however, it remains a problem. A method of time-scaling is presented that results in a further reduct...

Many single-channel signal decomposition techniques rely on a low-rank factorization of a time-frequency transform. In particular, nonnegative matrix factorization (NMF) of the spectrogram – the (power) magnitude of the short-time Fourier transform (STFT) – has been considered in many audio applications. In this setting, NMF with the Itakura-Saito divergence was shown to underly a generative Ga...

Spectrum sensing is a crucial technology for cognitive radio. The existing spectrum methods generally suffer from certain problems, such as insufficient signal feature representation, low efficiency, high sensibility to noise uncertainty, and drastic degradation in deep networks. In view of these challenges, we propose method based on short-time Fourier transform improved residual network (STFT...