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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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Proceedings ArticleDOI
17 May 2015
TL;DR: The design of a stepped frequency continuous wave (SFCW) radar is discussed, which transmits long duration pulses with higher average power and much narrower instantaneous bandwidth than UWB waveforms, to facilitate comparable signal resolution.
Abstract: In this paper, we discuss the design of a stepped frequency continuous wave (SFCW) radar, which transmits long duration pulses with higher average power and much narrower instantaneous bandwidth than UWB waveforms, to facilitate comparable signal resolution. FFT spectrograms, typically used in the extraction of vital signs from radar measurements, produce several spurious peaks at harmonics and intermodulation frequencies of respiration and heart rates, thereby increasing the uncertainty of these estimates, especially the heart rate. We apply a signal processing algorithm based on the state-space method for the extraction of cardiac and respiration rates from the data measured on a human subject using SFCW radar. Results show that accurate estimates of vital signs (heart rate < 1.2% in static mode and < 5.7% in motion) can be obtained without producing inter-modulation products that plague signal resolution in FFT spectrograms.

39 citations

Proceedings ArticleDOI
09 Jun 2018
TL;DR: Nao robot can "try to figure out" a human's psychology through speech emotion recognition and also know about people's happiness, anger, sadness and joy, achieving a more intelligent human-computer interaction.
Abstract: The key to speech emotion recognition is extraction of speech emotion features. In this paper, a new network model (CNN-RF) based on convolution neural network combined with random forest is proposed. Firstly, the convolution neural network is used as the feature extractor to extract the speech emotion feature from the normalized spectrogram, used random forest classification algorithm to classify the speech emotion features. The result of experiment shows that CNN-RF model is superior to the traditional CNN model. Secondly, Improved the Record Sound command box of Nao and applied the CNN-RF model to Nao robot. Finally, Nao robot can "try to figure out" a human's psychology through speech emotion recognition and also know about people's happiness, anger, sadness and joy, achieving a more intelligent human-computer interaction.

39 citations

Proceedings ArticleDOI
19 Mar 2017
TL;DR: This paper measures seven hand gestures performed in front of Doppler radar while generating spectrograms and proposes the use of a deep convolutional neural network (DCNN) as a classifier, a powerful classifier that extracts features as well as class boundaries through a training process.
Abstract: In this paper, we investigate the optimal structure of deep convolutional neural networks for classifying human hand gestures using Doppler radar When hand motions are captured by Doppler radar, the unique micro-Doppler signatures can be observed in the spectrogram If the signature is distinguishable by a classifier, then the hand gesture can be used for controlling electronics and as an input modality for a human-computer interface To classiiy signatures in the spectrogram, we propose the use of a deep convolutional neural network (DCNN) as a classifier DCNN is a powerful classifier that extracts features as well as class boundaries through a training process We measured seven hand gestures performed in front of Doppler radar while generating spectrograms To identify an optimal structure, we trained several DCNNs by changing hyperparameters, such as the number of convolutional layers, the number of filters, and the filter size The classification accuracy obtained from the optimal DCNN structure was approximately 87%

39 citations

22 Jul 2011
TL;DR: The pitch estimation was developed for the identication of the predominant voice in polyphonic music recordings and was evaluated as part of a melody extraction algorithm during the Music Information Retrieval Evaluation eXchange (MIREX 2006 and 2009), where the algorithm reached the best overall accuracy as well as very good performance measures.
Abstract: In this paper, a new approach for pitch estimation in polyphonic musical audio is presented. The algorithm is based on the pair-wise analysis of spectral peaks. The idea of the technique lies in the identication of partials with successive (odd) harmonic numbers. Since successive partials of a harmonic sound have well dened frequency ratios, a possible fundamental can be derived from the instantaneous frequencies of the two spectral peaks. Consecutively, the identied harmonic pairs are rated according to harmonicity, timbral smoothness, the appearance of intermediate spectral peaks, and harmonic number. Finally, the resulting pitch strengths are added to a pitch spectrogram. The pitch estimation was developed for the identication of the predominant voice (e.g. melody) in polyphonic music recordings. It was evaluated as part of a melody extraction algorithm during the Music Information Retrieval Evaluation eXchange (MIREX 2006 and 2009), where the algorithm reached the best overall accuracy as well as very good performance measures.

39 citations

Journal ArticleDOI
TL;DR: In this paper, a computer program was developed to generate a spectrogram for a given observation point and illuminating region, which contained information on the region of origin, the number of hops, and the direction of group velocity of the wave packets which formed the spectrogram.

38 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
2023627
20221,396
2021488
2020595
2019593