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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TL;DR: The ConditionaL Neural Network (CLNN) 1 and its extension, the MaskedconditionaL neural network (MCLNN), designed to exploit the nature of sound in a time–frequency representation and surpasses neural networks based architectures including state-of-the-art Convolutional Neural Networks and several hand-crafted attempts.
32 citations
Patent•
16 Apr 2008
TL;DR: In this paper, an FPGA-based random signal generator comprising a PC, a USB controller, an MCU3, a MCU interface module, a crystal resonator, an EPC2, a time controller, a dual-channel DA output circuit, a frequency controller, register matrix unit, a keyboard, a key scanning module, FLASH, a Flash control module, TFT display, a TFT control module and a DDS signal generator, a waveform synthesis module are presented.
Abstract: The invention discloses an FPGA-based random signal generator comprising a PC, a USB controller, an MCU3, an MCU interface module, a crystal resonator, an EPC2, a time controller, a dual-channel DA output circuit, a frequency controller, a register matrix unit, a keyboard, a keyboard scanning module, a FLASH, a Flash control module, a TFT display, a TFT control module, a DDS signal generator, a waveform synthesis module and other waveform generators. When the invention is used, software can automatically complete frequency spectrum information identification and get frequency point amplitude and phase parameters after a frequency spectrogram and phase spectrogram parameters are inputted into a software control interface; then a time domain information table is obtained after sampling values are quantized and encoded, the time domain information table is downloaded to the RAM of a DDS generating circuit to realize periodic or nonperiodic time domain signal reduction output; furthermore, a waveform amplitude is online stepped and adjustable, thereby realizing the aims of frequency domain output and time domain output.
32 citations
TL;DR: This approach combines a preprocessing based on functional principles of the human auditory system and a probabilistic tracking scheme with an algorithm for adaptive frequency range segmentation as well as Bayesian smoothing to derive an efficient framework for estimating formant trajectories.
Abstract: We present a framework for estimating formant trajectories. Its focus is to achieve high robustness in noisy environments. Our approach combines a preprocessing based on functional principles of the human auditory system and a probabilistic tracking scheme. For enhancing the formant structure in spectrograms we use a Gammatone filterbank, a spectral preemphasis, as well as a spectral filtering using difference-of-Gaussians (DoG) operators. Finally, a contrast enhancement mimicking a competition between filter responses is applied. The probabilistic tracking scheme adopts the mixture modeling technique for estimating the joint distribution of formants. In conjunction with an algorithm for adaptive frequency range segmentation as well as Bayesian smoothing an efficient framework for estimating formant trajectories is derived. Comprehensive evaluations of our method on the VTR-formant database emphasize its high precision and robustness. We obtained superior performance compared to existing approaches for clean as well as echoic noisy speech. Finally, an implementation of the framework within the scope of an online system using instantaneous feature-based resynthesis demonstrates its applicability to real-world scenarios.
32 citations
TL;DR: The Locally Optimized Spectrogram (LOS) defines a novel method for obtaining a high-resolution time-frequency representation based on the short-time fractional Fourier transform (STFrFT) and shows the trade-off between the cross-terms suppression and auto-terms resolution.
32 citations
TL;DR: In this paper optical implementation of a generalized space-spatial frequency (GSF) function is discussed and experimental results using coherent space-integrating techniques for the realization of different GSF are presented.
Abstract: One-dimensional signals have complementary attributes, their space and spatial frequency properties. Recently, another signal representation was introduced in optics, the Wigner distribution (WD) function, which allows the simultaneous display of the two attributes of a 1-D signal. In this paper optical implementation of a generalized space–spatial frequency (GSF) function is discussed. Special cases of the GSF representations are the WD, the radar ambiguity function (AF), the energy distribution function, the various pseudo-WD functions, the spectrogram, the local frequency and the local Doppler frequency spectrum. The GSF representation allows the simultaneous filtering of both the spatial and the spatial frequency content of a 1-D signal. The 2-D filtering of a 1-D signal allows sidelobe reduction of the WD and AF of quasi-periodic 1-D signals, display of the instantaneous spatial frequency content of a 1-D signal, space–spatial frequency excision of a desired portion of a 1-D signal as well as the usual matched filter detection. Experimental results using coherent space-integrating techniques for the realization of different GSF are presented.
32 citations