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About 10,000 papers for “Spectrogram”

IF Estimation Using Spectrogram and Scalogram

REIZ Romulus, MORGOŞ Lucian · SHILAP Revista de lepidopterología · 2013

This paper presents the results obtained using an instantaneous frequency estimation method, based upon the detection of the peaks of the spectrogram and the scalogram. A comparison is made between these two representations, showing the advantages and the disadvantages of both. Some instantaneous frequency estimation examples are presented, using as test signals nonstationary frequency modulated signals covered with zero mean gaussian noise.

Cited by 1Open access

Small tank spectrogram and power spectrum.

Jack A. Culotta (21387226), Marie L. Ervin (21387229), Brooke J. Vetter (4925914) et al. · Figshare · 2025

Upper: Spectrogram and Lower: power spectrum. Hydrophone recording of the broadband sound stimulus played in the small choice tank for conditioning and phonotaxis experiments. Recording was taken in the middle of the water column, between the speaker and the shuttle exit. (TIFF)

Cited by 0Open access

Spectrogram animation, 12 µm core separation

Antikainen, Aku, Agrawal, Govind · Figshare · 2019

The total spectrogram over all cores of a hexagonal seven-core fiber at various propagation distances. The cores have a diameter of 6 µm and are separated by 25 µm. The input pulse is a 15 kW 100 fs sech-pulse launched in the center core.

Cited by 0Open access

Spectrogram animation, 12 µm core separation

Antikainen, Aku, Agrawal, Govind · Figshare · 2019

The total spectrogram over all cores of a hexagonal seven-core fiber at various propagation distances. The cores have a diameter of 6 µm and are separated by 12 µm. The input pulse is a 15 kW 100 fs sech-pulse launched in the center core.

Cited by 0Open access

Modification of mapping methods of spectrogram

Koula, Ivan · Digital Repository (National Repository of Grey Literature) · 2005

Algorithms enhancing the useful audio signal at the expense of background interference noise have become a topical problem. Most important of all is to improve methods working in real time. The spectrogram mapping method is a comparatively simple and for some kinds of additive noise very effective method.

Cited by 0

Robust speech recognition with spectrogram factorisation

Antti Hurmalainen · Tampere University Institutional Repository (Tampere University) · 2014

Communication by speech is intrinsic for humans. Since the breakthrough of mobile devices and wireless communication, digital transmission of speech has become ubiquitous. Similarly distribution and storage of audio and video data has increased rapidly. However, despite being technically capable to record and process audio signals, only a fraction of digital systems and services are actually able to work with spoken input, that is, to operate on the lexical content of speech. One persistent obstacle for practical deployment of automatic speech recognition systems is inadequate robustness against noise and other interferences, which regularly corrupt signals recorded in real-world environments.\n\nSpeech and diverse noises are both complex signals, which are not trivially separable. Despite decades of research and a multitude of different approaches, the problem has not been solved to a sufficient extent. Especially the mathematically ill-posed problem of separating multiple sources from a single-channel input requires advanced models and algorithms to be solvable. One promising path is using a composite model of long-context atoms to represent a mixture of non-stationary sources based on their spectro-temporal behaviour. Algorithms derived from the family of non-negative matrix factorisations have been applied to such problems to separate and recognise individual sources like speech.\n\nThis thesis describes a set of tools developed for non-negative modelling of audio spectrograms, especially involving speech and real-world noise sources. An overview is provided to the complete framework starting from model and feature definitions, advancing to factorisation algorithms, and finally describing different routes for separation, enhancement, and recognition tasks. Current issues and their potential solutions are discussed both theoretically and from a practical point of view. The included publications describe factorisation-based recognition systems, which have been evaluated on publicly available speech corpora in order to determine the efficiency of various separation and recognition algorithms. Several variants and system combinations that have been proposed in literature are also discussed. The work covers a broad span of factorisation-based system components, which together aim at providing a practically viable solution to robust processing and recognition of speech in everyday situations.

Cited by 2Open access

Gabor spectrogram based risk of aliasing

Dimitri Van De Ville, Koen Denecker, Wilfried Philips et al. · Ghent University Academic Bibliography (Ghent University) · 2000

Resampling is a part of many image processing tasks. Traditional linear resampling techniques must compromise between edge-preservation and anti-aliasing. This paper presents a method to locally estimate the risk of aliasing based on a space-frequency representation by means of the Gabor spectrogram. The proposed approach can be applied to general lattice arrangements of the source and the target lattice. To show the feasibility of this new method, results of a practical application are shown, i.e., resampling an image for gravure printing reproduction by a non-linear edge-preserving resampling technique based on the risk of aliasing. Key-Words: Gabor spectrogram, joint time-frequency analysis, aliasing, non-linear resampling

Cited by 0

Spectrogram of great spotted woodpecker drumming.

Michał Budka (608553), Krzysztof Deoniziak (4836633), Tomasz Tumiel (4836639) et al. · Figshare · 2018

<p>(a) Spectrogram represents one roll of drumming. (b) Pulse Train Analysis window with visible strokes within drumming roll (first six stroke-to-stroke durations are indicated). Spectrogram settings: FFT length = 512; Frame size = 75%, Window = Hamming.</p>

Cited by 0Open access

Spectrogram of the clean signal 2.

Nattapol Aunsri (15307064), Prasara Jakkaew (17920995), Chanin Kuptametee (20135811) · Figshare · 2024

<div><p>Non-linear and non-stationary signals are analyzed and processed in the time-frequency (TF) domain due to interpretation simplicity. Wigner-Ville distribution (WVD) delivers a very sharp resolution of non-stationary signals in the TF domain. However, cross-terms occur between true frequency modes due to their bilinear nature. Masked WVD reduces cross-terms by multiplying the time-frequency representation (TFR) obtained from the WVD with the TFR of the same signal obtained from another method, while S-transform (ST) is a linear signal analysis method that combines the advantages of short-time Fourier transform (STFT) and wavelet transform (WT). This paper investigated WVD masking with both original and modified STs to compare their cross-term reduction results. Moreover, additional parameters are integrated into the ST to deliver the better resolution of the ST and, consequently, more satisfactory cross-term reduction. However, these parameters must be carefully optimized by expert users in their respective application fields.</p></div>

Cited by 0Open access

Spectrogram of a corncrake broadcast call.

Lucyna Ewa Wojas (5236598), Paweł Wojciech Podkowa (5236595), Tomasz Stanisław Osiejuk (5236601) · Figshare · 2018

<p>(A) The corncrake call consists of two syllables (SYL1 and SYL2) separated by an interval (INT1), and the calling rhythm is defined as RHYTHM = INT2 / (SYL1 + INT1 + SYL2). (B) An envelope of a broadcast call syllable showing pulse-to-pulse duration, which is used for individual discrimination, with the first 4 pulses marked.</p>

Cited by 0Open access

Instantaneous bandwidth for signals and spectrogram

Leon Cohen, Che Hyun Lee · International Conference on Acoustics, Speech, and Signal Processing · 2002

The concept of instantaneous bandwidth, which is an indication of the frequency spread at a given time, is developed. It is shown that the global bandwidth of a signal can naturally be broken up into an AM and an FM contribution and that the AM contribution comes from averaging the instantaneous bandwidth. The instantaneous bandwidth for the spectrogram is derived, and series expansions in terms of the derivatives of the phase are obtained.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 60

Speech signal analysis with reallocated spectrogram

F. Plante, Georg Meyer, W.A. Ainsworth · 2002

The limited joint time and frequency resolution of Fourier analysis makes an accurate analysis of speech signals difficult. Fourier analysis offers either good temporal accuracy or good frequency resolution, never both. Many methods have been proposed to overcome this limitation. The results do not show a great improvement in the readability of the representation due to the presence of many components in the speech signal. The reallocation defined by Kodera and al. (1978) seems a good way to improve the localisation of the spectrogram. Recent work simplified the implementation of this method, which makes it attractive. This paper explores the applicability of this method to the analysis of speech signals.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 8

Including parametric models in spectrogram decomposition.

Bertrand David, Hennequin Romain, Roland Badeau et al. · The Journal of the Acoustical Society of America · 2010

Regarding the decomposition of an audio stream in elementary sound objects, the non-negative matrix factorization (NMF) is now one of the prominent tools that can be employed. For instance, in the context of an automatic music transcription application, the interest of the technique relies on its ability to use redundancies over a whole piece to hopefully be able to identify separated sound objects as musical notes. In this ideal case, the spectrogram factorizes into a matrix of inferred spectral atoms and a matrix of temporal activations associated to these atoms. However, the unconstrained version of the NMF does not guarantee that the resulting atoms are actually musical notes: the spectrum of a single note can be split into several atoms or, conversely, a single atom could represent more than one note. The work that will be presented here deals with parametric models designed to describe the spectral modifications that occur along the progress of a single audio event (e.g., a musical note) in order to obtain a more meaningful decomposition from a musical point of view.

Cited by 0

The spectrogram expansion of Wigner functions

Keller, Johannes · arXiv (Cornell University) · 2016

Wigner functions generically attain negative values and hence are not probability densities. We prove an asymptotic expansion of Wigner functions in terms of Hermite spectrograms, which are probability densities. The expansion provides exact formulas for the quantum expectations of polynomial observables. In the high frequency regime it allows to approximate quantum expectation values up to any order of accuracy in the high frequency parameter. We present a Markov Chain Monte Carlo method to sample from the new densities and illustrate our findings by numerical experiments.

Cited by 0Open access

Adaptive Spectrogram for Surface EEnG Analysis

Javier Garcia‐Casado, J.L. Martínez-de-Juan, Gema Prats-Boluda et al. · 2005

Electroenterogram (EEnG) is the myoelectrical signal of the small bowel. It is the result of a permanent slow wave (low frequency component) and series of spike bursts (high frequency component) that are only present when a bowel contraction occurs. This means spectral content of EEnG changes throughout time. The definition of a t-f distribution that adapts to changes in EEnG spectrum could be very helpful to study this signal. Nine recording sessions of surface EEnG were carried out in three Beagle dogs in fast state. It was proposed an algorithm that selects the window length of spectrogram depending on the maximum frequency of interest of signal in previous window. The proposed adaptive spectrogram was applied to surface EEnG. When only slow waves are present, the adaptive spectrogram enlarges window length in order to present good frequency resolution to characterize the low frequency component of the EEnG. When spike bursts appear, windows of analysis are shorter reducing time resolution to identify individual units of slow waves accompanied by SB. The proposed adaptive spectrogram can be a helpful tool to identify surface EEnG activity in time and frequency domain. It is also likely to be adapted to study other multicomponent signals.

Cited by 0

Spectrogram vs. timbral code-word example.

Martín Haro (176360), Joan Serrà (176363), Perfecto Herrera (176365) et al. · Figshare · 2015

&lt;p&gt;a) Spectrogram representation for a sinusoidal sweep in logarithmic progression over time going from 0 to 9,500 Hz. The color intensity represents the energy of the signal (white = no energy, black = maximum energy). This standard representation is obtained by means of the short-time Fourier transform. b) Timbral code-word representation of the same audio signal. The horizontal axis corresponds to temporal windows of 186 ms and the vertical axis shows the quantized values per Bark-band (black = 1 and white = 0). For instance, in the first 40 temporal windows only the first Bark-band is quantized as one (the first Bark-band corresponds to frequencies between 0 and 100 Hz). A total of 37 different code-words are used to encode this sinusoidal sweep.&lt;/p&gt;

Cited by 0Open access