Topic
Spectrogram
About: Spectrogram is a research topic. Over the lifetime, 5813 publications have been published within this topic receiving 81547 citations.
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18 May 2008
TL;DR: Results from the RS-AIC hardware implementation demonstrate successful reconstruction of signals that are sampled at half the Nyquist-rate while maintaining up to a 51 dB signal-to-noise ratio (SNR), which is equivalent to an 8.5 bit resolution analog to digital converter.
Abstract: In this paper, we successfully demonstrate the feasibility of hardware implementation of a sub-Nyquist random- sampling based analog to information converter (RS-AIC). The RS-AIC is based on the theory of information recovery from random samples using an efficient information recovery algorithm to compute the spectrogram of the signal. Our RS-AIC enables sub-Nyquist acquisition and processing of wideband signals that are sparse in a local Fourier representation. Results from our RS-AIC hardware implementation demonstrate successful reconstruction of signals that are sampled at half the Nyquist-rate while maintaining up to a 51 dB signal-to-noise ratio (SNR), which is equivalent to an 8.5 bit resolution analog to digital converter.
70 citations
TL;DR: In this paper, the ptychographic reconstruction algorithm was proposed for frequency-resolved optical gating (FROG) and demonstrated robustness of two unknown pulses from a single measured spectrogram and power spectrum of only one of the pulses.
Abstract: Frequency-resolved optical gating (FROG) is probably the most popular technique for complete characterization of ultrashort laser pulses. In FROG, a reconstruction algorithm retrieves the pulse from a measured spectrogram, yet current FROG reconstruction algorithms require and exhibit several restricting features that weaken FROG performances. For example, the delay step must correspond to the spectral bandwidth measured with large enough SNR a condition that limits the temporal resolution of the reconstructed pulse, obscures measurements of weak broadband pulses, and makes measurement of broadband mid-IR pulses hard and slow because the spectrograms become huge. We develop a new approach for FROG reconstruction, based on ptychography (a scanning coherent diffraction imaging technique), that removes many of the algorithmic restrictions. The ptychographic reconstruction algorithm is significantly faster and more robust to noise than current FROG algorithms, which are based on generalized projections (GP). We demonstrate, numerically and experimentally, that ptychographic reconstruction works well with very partial spectrograms, e. g. spectrograms with reduced number of measured delays and spectrograms that have been substantially spectrally filtered. In addition, we implement the ptychogrpahic approach to blind second harmonic generation (SHG) FROG and demonstrate robust and complete characterization of two unknown pulses from a single measured spectrogram and power spectrum of only one of the pulses. We believe that the ptychograpy-based approach will become the standard reconstruction procedure in FROG and related diagnostics methods, allowing successful reconstructions from so far unreconstructable spectrograms.
70 citations
TL;DR: After converting highly overlapped spectrograms into linear quantized images and reducing dimensions by applying various image resizing methods, feature extraction and classification are performed with convolutional neural networks (CNN), which have very high performance in image classification.
70 citations
TL;DR: A straightforward tacho-less order tracking method based on order spectrogram visualization is proposed in this paper, which has been validated by both simulated and experimental rolling bearing vibration signals.
69 citations
19 Apr 2015
TL;DR: It is shown how KAM can be combined with a fast compression algorithm of its parameters to address the scalability issue, thus enabling its use on small platforms or mobile devices.
Abstract: Recently, Kernel Additive Modelling (KAM) was proposed as a unified framework to achieve multichannel audio source separation. Its main feature is to use kernel models for locally describing the spectrograms of the sources. Such kernels can capture source features such as repetitivity, stability over time and/or frequency, self-similarity, etc. KAM notably subsumes many popular and effective methods from the state of the art, including REPET and harmonic/percussive separation with median filters. However, it also comes with an important drawback in its initial form: its memory usage badly scales with the number of sources. Indeed, KAM requires the storage of the full-resolution spectrogram for each source, which may become prohibitive for full-length tracks or many sources. In this paper, we show how it can be combined with a fast compression algorithm of its parameters to address the scalability issue, thus enabling its use on small platforms or mobile devices.
69 citations