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
More filters
TL;DR: An architecture of the system for time-frequency signal analysis based on the S-method, whose special cases are two the most important distributions: the spectrogram and the Wigner distribution is presented.
Abstract: An architecture of the system for time-frequency signal analysis is presented. This system is based on the S-method, whose special cases are two the most important distributions: the spectrogram and the Wigner distribution. Systems with constant and signal-dependent window widths are presented.
63 citations
14 Nov 2017
TL;DR: This article proposed a hybrid Convolutional Recurrent Neural Network (CRNN) that operates on spectrogram images of the provided audio snippets, which is applicable to a range of noisy scenarios and can easily be extended to previously unknown languages.
Abstract: Language Identification (LID) systems are used to classify the spoken language from a given audio sample and are typically the first step for many spoken language processing tasks, such as Automatic Speech Recognition (ASR) systems. Without automatic language detection, speech utterances cannot be parsed correctly and grammar rules cannot be applied, causing subsequent speech recognition steps to fail. We propose a LID system that solves the problem in the image domain, rather than the audio domain. We use a hybrid Convolutional Recurrent Neural Network (CRNN) that operates on spectrogram images of the provided audio snippets. In extensive experiments we show, that our model is applicable to a range of noisy scenarios and can easily be extended to previously unknown languages, while maintaining its classification accuracy. We release our code and a large scale training set for LID systems to the community.
63 citations
Proceedings Article•
01 Oct 2016TL;DR: Results are obtained by the DCNN while outperforming a few conventional classifiers, showing the possible benefits of deep learning approaches in human gait classification.
Abstract: This paper presents the use of a deep convolutional neural network (DCNN) in distinguishing between absence of human gait and the presence of single or multiple instances of human gait by applying the DCNN to micro-Doppler spectrograms. The approach is evaluated for various radar frequencies and SNR levels using model data, while final validation is performed using X-band CW radar measurements. Satisfactorily results are obtained by the DCNN while outperforming a few conventional classifiers, showing the possible benefits of deep learning approaches in human gait classification.
63 citations
TL;DR: The authors propose an object-oriented dimension-reduction technique: subspace reliability analysis, which directly removes the unreliable feature dimensions of two class-conditional covariance matrices in two separate subspaces, which demonstrates better performance than the state-of-the-art approaches.
62 citations
TL;DR: This work proposes a method for bird acoustic activity detection, based on morphological filtering of the spectrogram seen as an image, validated on the automated acoustic recognition of Southern Lapwing Vanellus chilensis, a common Neotropical bird species.
62 citations