Recently, neural networks based purely on self-attention, such as the Vision Transformer (ViT), have been shown to outperform deep learning models constructed with convolutional (CNNs) various vision tasks, thus extending success of Transformers, which were originally developed for language processing, domain. A recent study showed that a similar methodology can also be applied audio Specifical...

This paper presents new approach to enhance the quality of sound signal perceived by the listener in a concert hall considering Spectrogram and Single Spectrum Plot. We simulate a natural concert hall using Digital Echo and Schroeder Reverberator models. We analyze the results using Spectrogram and Single Spectrum Plots and finally propose a model for better analysis of room acoustics.

Interference detection is very important for Global Navigation Satellite System (GNSS) receivers. Current work on interference detection in GNSS receivers has mainly focused on time-frequency (TF) analysis techniques, such as spectrogram and Wigner-Ville distribution (WVD), where the spectrogram approach presents the TF resolution trade-off problem, since the analysis window is used, and the WV...

We perform frequency-domain analysis in the genomes of various organisms using tricolor spectrograms, identifying several types of distinct visual patterns characterizing specific DNA regions. We relate patterns and their frequency characteristics to the sequence characteristics of the DNA. At times, the spectrogram patterns can be related to the structure of the corresponding protein region by...

We experimentally demonstrate that an interferometric spectrogram of the signal pulse can be processed differently to obtain the spectral amplitude and(or) phase profile(s). Consistency between the results obtained by frequencyresolved optical gating (FROG) and modified interferometric field autocorrelation (MIFA) methods is confirmed. We also show that the robustness of MIFA technique against ...

In this paper a new method of modulation classification based on spectrogram analysis is described. The classification method can discriminate between various digital signal modulations such as FSK, BPSK, MSK, QPSK and QAM-16. This classification method is examined on signals corrupted by Gaussian noise, phase noise and on signals after their transmission through multipath Rayleigh fading chann...

I consider the problem of decomposing a time series into its constituent components. In general, this is underdetermined, and assumptions must be made. Previous attempts include applying nonnegative matrix factorization to the spectrograms of isolated sources to learn a basis for each. A new time series composed of a sum of these sources is then projected onto these bases. For this project, I a...

Spectrogram analysis frequently suffers from inadequate time or frequency resolution, respectively. However, time-frequency reassigned spectrograms offer an attractive extension regarding the resolution trade-off. Specifically, this method deduces an improved time-frequency localization from the derivatives of the spectrogram phase, with respect to time and frequency. For high-quality images of...