Topic
Spectrogram
About: Spectrogram is a research topic. Over the lifetime, 5813 publications have been published within this topic receiving 81547 citations.
Papers published on a yearly basis
Papers
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TL;DR: A new method for time-frequency representation is presented, which combines a filter bank and the Wigner-Ville distribution, and the ability of the proposed non-Cohen's (1995) class TFD to reduce cross-terms as well as noise aswell as its ability to approximately reconstruct signals is illustrated.
Abstract: We present a new method for time-frequency representation, which combines a filter bank and the Wigner-Ville distribution (WVD). The filter bank decomposes a multicomponent signal into a number of single component signals before the WVD is applied. Cross-terms as well as noise are reduced significantly, whereas high time-frequency concentration is attained. Properties of the proposed time-frequency distribution (TFD) are investigated, and the requirements for the filter bank to fulfil these are given. The ability of the proposed non-Cohen's (1995) class TFD to reduce cross-terms as well as noise as well as its ability to approximately reconstruct signals are illustrated by examples. The results are compared with those from the WVD, the Choi-Williams (1989) distribution (CWD), and spectrogram.
30 citations
Posted Content•
TL;DR: A Wavenet-based model is proposed and Wave-U-Net can outperform DeepConvSep, a recent spectrogram-based deep learning model, and the results confirm that waveform-based models can perform similarly (if not better) than a spectrogram/deep learning model.
Abstract: Most of the currently successful source separation techniques use the magnitude spectrogram as input, and are therefore by default omitting part of the signal: the phase. To avoid omitting potentially useful information, we study the viability of using end-to-end models for music source separation --- which take into account all the information available in the raw audio signal, including the phase. Although during the last decades end-to-end music source separation has been considered almost unattainable, our results confirm that waveform-based models can perform similarly (if not better) than a spectrogram-based deep learning model. Namely: a Wavenet-based model we propose and Wave-U-Net can outperform DeepConvSep, a recent spectrogram-based deep learning model.
30 citations
19 Dec 2011
TL;DR: A micro-Doppler system and a computationally efficient classifier for the purpose of identifying individuals and gender are presented and Walking subjects are successfully classified based on their mD time-frequency signatures.
Abstract: The ability to identify an individual quickly and accurately is a critical parameter in surveillance. Conventional contactless systems are often complex and expensive to implement since video-based processing requires high computational resources. In this paper we present a micro-Doppler (mD) system and a computationally efficient classifier for the purpose of identifying individuals and gender. Walking subjects are successfully classified based on their mD time-frequency signatures. Recognition accuracies as high as 100% are obtained for some individuals and 92% for gender classification.
30 citations
TL;DR: In this paper, a robust time-frequency approach based on pseudo-Wigner-Ville distribution assisted Renyi entropy (PWVD-RE) for vehicle detection is presented.
30 citations
TL;DR: A noise suppression algorithm is proposed based on filtering the spectrotemporal modulations of noisy signals using a multiscale representation of the signal spectrogram generated by a model of sound processing in the auditory system to suppress noise that has distinctive modulation patterns, despite being spectrally overlapping with the signal.
Abstract: A noise suppression algorithm is proposed based on filtering the spectrotemporal modulations of noisy signals. The modulations are estimated from a multiscale representation of the signal spectrogram generated by a model of sound processing in the auditory system. A significant advantage of this method is its ability to suppress noise that has distinctive modulation patterns, despite being spectrally overlapping with the signal. The performance of the algorithm is evaluated using subjective and objective tests with contaminated speech signals and compared to traditional Wiener filtering method. The results demonstrate the efficacy of the spectrotemporal filtering approach in the conditions examined.
30 citations