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About 10,000 papers for “Spectrogram”

Exemplar 1 spectrogram and waveform.

Katie Campbell (2498896), Yelena Cerezke-Riemer (21169987), John H. Acorn (4859542) · Figshare · 2025

Recorded in Gull Lake, Alberta, Canada, July 22, 2023. Waveform view (top) shows time in seconds on the x-axis, and amplitude in kilounits (kU) on the y-axis. Spectrogram view (bottom) shows time in seconds on the x-axis, frequency in kilohertz (kHz) on the y-axis, and intensity represented by colour (blue = low intensity; yellow = medium; red = high). A short-time Fourier transform size with a 50% overlap parameter was used.

Cited by 0Open access

FAST SIGNAL RECONSTRUCTION FROM MAGNITUDE SPECTROGRAM OF CONTINUOUS WAVELET TRANSFORM BASED ON SPECTROGRAM CONSISTENCY

Tomohiko Nakamura, Hirokazu Kameoka · 2014

The continuous wavelet transform (CWT) can be seen as a filterbank having logarithmic frequency subbands spacing similar to the human auditory system. Thus, to make computers imitate the significant functions of the human auditory system, one promising approach would be to model, analyze and process magnitude spectrograms given by the CWT. To realize this approach, we must be able to convert a processed or modified magnitude CWT spectrogram, which contains no information about the phase, into a time domain signal specifically for those applications in which the aim is to generate audio signals. To this end, this paper proposes a fast algorithm for estimating the phase from a given magnitude CWT spectrogram to reconstruct an audio signal. The experimental results revealed that the proposed algorithm was around 100 times faster than a conventional algorithm, while the reconstructed signals obtained with the proposed algorithm had almost the same audio quality as those obtained with the previous study.

Cited by 8

Comparison between the performance of spectrogram and multi-window spectrogram in digital modulated communication signals

Tan Jo Lynn, Ahmad Zuri Sha’ameri · 2007

In this paper, we compare the performance of spectrogram and a new variation of multi-window (MW) spectrogram for various digital modulated signals. The windows used in the MW spectrogram are combination of Slepian sequences instead of using all the sequences. The comparison in the time-frequency representation is made in terms of main-lobe width (MLW) and peak to side-lobe ratio (PSLR) of the signals used. In the presence of noise, performance is compared in terms of variance in the estimated bit duration frequency. Bit duration and frequency are estimated from the instantaneous frequency (IF), which is derived from the time-frequency representation. The bias in the estimated bit duration is also calculated to compare the performance between spectrogram and MW spectrogram. In general, the time-frequency representation of the spectrogram is better than MW spectrogram. However, MW spectrogram is superior to spectrogram in terms of bit duration estimation.

Cited by 10

Smart Audio Editing in the Spectrogram

C. G. V. D. Boogaart, R. Lienhart, C. G. V. D. Boogaart et al. · 2006

Starting with a novel audio analysis and editing paradigm, a set of new and adaptive audio analysis and editing algorithms in the spectrogram are developed and integrated into a smart visual audio editing tool in a “what you see is what you hear ” style. At the core of our algorithms and methods is a very flexible audio spectrogram that goes beyond FFT and Wavelets and supports manipulating a signal at any chosen time-frequency resolution: the Gabor analysis and synthesis. It gives maximum accuracy of the representation, is fully invertible, and enables resolution zooming. Simple audio objects are localized in time and frequency. They can easily be identified visually and selected by simple geometric selection masks such as rectangles, combs and polygons. For many audio objects, however the structures in

Cited by 0

Oscillogram and spectrogram of nocturnal stridors.

Dae Lim Koo (2617030), Jee Young Lee (2617033), Eun Yeon Joo (787131) et al. · Figshare · 2016

<p>Two different types of waveforms in nocturnal stridor are described and the snoring signal of each patient was analyzed. Each sample has a duration of 40 ms. (A) Stridor in patient No. 3 presents a rhythmic sinusoidal waveform with a frequency of 250 Hz (left). The signal of snoring in patient No. 3 was irregular and non-periodic (right). (B) Oscillogram of patient No. 8 shows a semirhythmic signal (left). Analysis of snoring in patient No. 8 shows a chaotic signal (right). (C) Spectrogram of patient No. 2 shows three formants. The first (F1), second (F2), and third (F3) formants are indicated by black arrows.</p>

Cited by 0Open access

Spectrogram animation, 15.5 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 15.5 µm. The input pulse is a 15 kW 100 fs sech-pulse launched in the center core.

Cited by 0Open access

Deconvolutive Short-Time Fourier Transform Spectrogram

Wenkai Lu, Qiang Zhang · IEEE Signal Processing Letters · 2009

The short-time Fourier transform (STFT) spectrogram, which is the squared modulus of the STFT, is a smoothed version of the Wigner-Ville distribution (WVD). The STFT spectrogram is 2-D convolution of the the signal WVD and the window function WVD. In this letter, we propose a deconvolutive short-time Fourier transform (DSTFT) spectrogram method, which improves the time-frequency resolution and reduces the cross-terms simultaneously by applying a 2-D deconvolution operation on the STFT spectrogram. Compared to the STFT spectrogram, the spectrogram obtained by the proposed method shows a clear improvement in the time-frequency resolution. Computer simulations are provided to illustrate the good performance of the proposed method, compared with some traditional time-frequency representation (TFR) methods.

Cited by 100

Representative spectrogram and band power graphs.

Marko Vorkapić (8816435), Andrej Savić (8816438), Milica Janković (8816441) et al. · Figshare · 2020

<p>Spectrogram (A, B) and corresponding power changes in delta, theta, alpha and beta frequency bands (C, D) during 4 time-windows for one animal in the control group (left) and experimental group (right).</p>

Cited by 0Open access

Spectrogram analysis in respective bird sound

Purnama, Muhammad Thohawi Elziyad, Dewi, Cytra Meyliana Surya, Dhamayanti, Yeni et al. · Figshare · 2022

This file reported spectrogram analysis in respective bird sound.

Cited by 3Open access

ISEE-1 PWI SA Spectrogram Plots.

Donald A. Gurnett · Open MIND · 2026

This dataset contains dynamic spectrogram PNG plots of the ISEE-1/PWI Spectrum Analyzer data. Each plot spans 12 hours. The dataset consists of files from 0-12UT and 12-24UT. These data were obtained from the Ev long wire antenna in the spin plane and the Bz search coil along the spin axis. Each image consists of two panels. The title above the upper panel is "ISEE-1 PWI Spectrum Analyzer Ev Antenna and Bz Search Coil" Each panel is a plot of the power spectral density of received signal (color scale) as a function of operating frequency (in a logarithmic scale on the vertical axis) and time (horizontal axis). The upper panel is a plot of Electric Spectral Density (SD) the lower plot is Magnetic SD. Beneath the time labels on the horizontal axis of the spectrograms are ephemeris data: position of the spacecraft in radial distance (Earth radii), geomagnetic latitude, magnetic local time, and McIlwain L-shell. Overlaid on the upper plot are traces of the electron cyclotron frequencies. At the bottom of the image is the date, day number and time range. Located in the same directory are the daily ISEE-1 PWI Sweep Frequency Receiver (SFR) spectrograms.

Cited by 0Open access

Van Allen Probe A Spectrogram Data

Wygant, J.R · Space Physics Data Facility · 2022

This data set contains Level 2 wave electric field and search coil wave magnetic fields spectral data from the Electric Field and Waves Suite (EFW) onboard Van Allen Probe A. Spectrograms between ~40 Hz and ~8 kHz are calculated on-board the EFW instrument every 4 seconds. These data include one component of the ac-coupled electric field from the rotating V12 sensors (V12ac). The electric field quantity is labeled EFW/L2. Also included are two components of the wave magnetic field from the University of Iowa EMFISIS sensors. The search coil quantities are labeled EFW/L2 spec64 smu. In the labels "smu" and "smv", the suffixes "u" and "v" refer to the u and v sensor coordinates rotating with the spacecraft. All these quantities, the nature of the on board signal processing, their filtering, time resolution and their respective coordinate systems are described in the EFW instrument paper (Wygant et al. 2013). When the spectral CDF is opened, many other possible quantities are listed which have no data in them. Possible sources include: * E12dc * E34dc * E56dc * E12ac * E34ac * E56ac * E DC parallel * E DC perpendicular 1 * E DC perpendicular 2 * E AC parallel * E AC perpendicular 1 * E AC perpendicular 2 * V1+V2+V3+V4/4AC * SCMu * SCMv * SCMw * SCM parallel * SCM perpendicular 1 * SCM perpendicular 2 Those without data are "place holder quantities" which are used by other instrument modes not currently selected. Examples of the spectral data may be found in Principal Investigator's on-line survey data plots. EMFISIS instrument wave data may be found on the University of Iowa website. This includes valuable spectral electric field data up to 400 kHz and magnetic field data up to about ~6 kHz.

Cited by 0Open access

Performance of an expert spectrogram reader

Ronald A. Cole, Alexander I. Rudnicky, V. M. Zue · The Journal of the Acoustical Society of America · 1979

The subject in our study has spent between 2000–2500 h learning to read speech spectrograms. We tested the subject's ability to identify the phonetic content of broadband speech spectrograms of unknown utterances during eight separate sessions of about 4 h each. The expert was presented with 23 spectrograms of English sentences and sequences of words and nonsense words, and 45 English words embedded in a known carrier phrase. The phonetic labels produced by the expert agreed with the phonetic labels produced by trained phoneticians (who listened to the speech) between 80% and 90% of the time, depending upon the scoring method used. When presented with words in a known carrier phrase, labeling performance improved to about 93%. A linguist who was presented with the phonetic transcriptions produced by the spectrogram reader was unable to identify all of the words in ten of 15 utterances, and missed only a single work in each of the remaining five. A film will be shown of the expert segmenting and labeling two spectrograms of unknown utterances.

Cited by 8

Hybrid Spectrogram and Waveform Source Separation

Alexandre Défossez · arXiv (Cornell University) · 2021

Source separation models either work on the spectrogram or waveform domain. In this work, we show how to perform end-to-end hybrid source separation, letting the model decide which domain is best suited for each source, and even combining both. The proposed hybrid version of the Demucs architecture won the Music Demixing Challenge 2021 organized by Sony. This architecture also comes with additional improvements, such as compressed residual branches, local attention or singular value regularization. Overall, a 1.4 dB improvement of the Signal-To-Distortion (SDR) was observed across all sources as measured on the MusDB HQ dataset, an improvement confirmed by human subjective evaluation, with an overall quality rated at 2.83 out of 5 (2.36 for the non hybrid Demucs), and absence of contamination at 3.04 (against 2.37 for the non hybrid Demucs and 2.44 for the second ranking model submitted at the competition).

Cited by 71Open access

KERNEL SPECTROGRAM MODELS FOR SOURCE SEPARATION

2015

In this study, we introduce a new framework called Kernel Additive Modelling for audio spectrograms that can be used for multichannel source separation. It assumes that the spectrogram of a source at any time-frequency bin is close to its value in a neighbourhood indicated by a source-specific proximity kernel. The rationale for this model is to easily account for features like periodicity, stability over time or frequency, self-similarity, etc. In many cases, such local dynamics are indeed much more natural to assess than any global model such as a tensor factorization. This framework permits one to use different proximity kernels for different sources and to estimate them blindly using their mixtures only. Estimation is performed using a variant of the kernel backfitting algorithm that allows for multichannel mixtures and permits parallelization. Experimental results on the separation of vocals from musical backgrounds demonstrate the efficiency of the approach. Index Terms—audio source separation, spatial filtering, spec-trogram models I.

Cited by 11