Spectrograms provide a visual representation of the vibrations of civil aircraft engines. The vibrations contain information relative to damage in the engine, if any. This representation is noisy, high dimensional and the relevant signatures relative to damages concern only a small part of the spectrogram. All these arguments lead to difficulties to automatically detect anomalies in the spectro...

In this paper we propose a method for automatic local time adaptation of the spectrogram of an audio signal, based on its decomposition within a Gabor multi-frame. The sparsity of the analyses within each individual frame is evaluated through the Rényi entropies measures. According to the sparsity of the decompositions, an optimal resolution and a reduced multi-frame are determined, defining an...

We present a robust environmental sound classification approach, based on reassignment method and logGabor filters. In this approach the reassigned spectrogram is passed through a bank of 12 log-Gabor filter concatenation applied to three spectrogram patches, and the outputs are averaged and underwent an optimal feature selection procedure based on a mutual information criterion. The proposed m...

Deciding the appropriate representation to use for modeling human auditory processing is a critical issue in auditory science. While engineers have successfully performed many single-speaker tasks with LPC and spectrogram methods, more difficult problems will need a richer representation. This paper describes a powerful auditory representation known as the correlogram and shows how this non-lin...

Mel-frequency spectral coefficients (MFSCs), calculated by averaging the spectrogram along a mel-frequency scale, are used in many audio classification tasks. Their efficiency can be partly explained by their stability to deformation in a Euclidean norm. However, averaging the spectrogram loses high-frequency information. This loss is reduced by keeping the window size small, around 20 ms, whic...

We present a method that analyzes a two-dimensional magnitude spectrogram S(f, t) into its local constituent spectro-temporal amplitudes A(f, t), frequencies F (f, t), orientations Θ(f, t), and phases φ(f, t). The method operates by performing a twodimensional local Gabor-like analysis of the spectrogram, retaining only the parameters of the 2D-Gabor filter with maximal amplitude response withi...

In order to apply speech spectrogram reading heuristics to an automatic speech recognition system, a more accurate expression of the heuristics must be developed. In particular, the transformation between acoustic feature measurements and phoneme candidates must be developed in a quantitative manner. In this paper, a visual acoustic-feature labeland a phoneme identification approach using this ...

End-to-end neural network based approaches to audio modelling are generally outperformed by models trained on high-level data representations. In this paper we present preliminary work that shows the feasibility of training the first layers of a deep convolutional neural network (CNN) model to learn the commonlyused log-scaled mel-spectrogram transformation. Secondly, we demonstrate that upon i...

The objective of this study is to establish the effectiveness of four different time-frequency representations (TFRs)--the reassigned spectrogram, the reassigned scalogram, the smoothed Wigner-Ville distribution, and the Hilbert spectrum--by comparing their ability to resolve the dispersion relationships for Lamb waves generated and detected with optical techniques. This paper illustrates the u...

This paper presents the method that underlies our submission to the untrimmed video classification task of ActivityNet Challenge 2016. We follow the basic pipeline of very deep two-stream CNN [16] and further raise the performance via a number of other techniques. Specifically, we use the latest deep model architecture, e.g. ResNet and Inception V3 and introduce a new aggregation scheme (top-k ...