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: It is shown with a perceptual test that the technique produces only a slightly lower degree of plausibility than the state-of-the-art acoustic scattering model that accounts for diffraction, and also that the proposed technique yields a prominently higher degree ofausible than a model that omits diffraction.
Abstract: In this work, a technique to render the acoustic effect of scattering from finite objects in virtual reality is proposed, which aims to provide a perceptually plausible response for the listener, rather than a physically accurate response. The effect is implemented using parametric filter structures and the parameters for the filters are estimated using artificial neural networks. The networks may be trained with modeled or measured data. The input data consist of a set of geometric features describing a large quantity of source-object-receiver configurations, and the target data consist of the filter parameters computed using measured or modeled data. A proof-of-concept implementation is presented, where the geometric descriptions and computationally modeled responses of three-dimensional plate objects are used for training. In a dynamic test scenario, with a single source and plate, the approach is shown to provide a similar spectrogram when compared with a reference case, although some spectral differences remain present. Nevertheless, it is shown with a perceptual test that the technique produces only a slightly lower degree of plausibility than the state-of-the-art acoustic scattering model that accounts for diffraction, and also that the proposed technique yields a prominently higher degree of plausibility than a model that omits diffraction.
27 citations
Posted Content•
TL;DR: In this paper, the authors proposed an approach to anomalous detection in which the model utilizes multiple frames of a spectrogram whose center frame is removed as an input, and it predicts an interpolation of the removed frame as an output.
Abstract: As the labor force decreases, the demand for labor-saving automatic anomalous sound detection technology that conducts maintenance of industrial equipment has grown. Conventional approaches detect anomalies based on the reconstruction errors of an autoencoder. However, when the target machine sound is non-stationary, a reconstruction error tends to be large independent of an anomaly, and its variations increased because of the difficulty of predicting the edge frames. To solve the issue, we propose an approach to anomalous detection in which the model utilizes multiple frames of a spectrogram whose center frame is removed as an input, and it predicts an interpolation of the removed frame as an output. Rather than predicting the edge frames, the proposed approach makes the reconstruction error consistent with the anomaly. Experimental results showed that the proposed approach achieved 27% improvement based on the standard AUC score, especially against non-stationary machinery sounds.
27 citations
TL;DR: A new fast algorithm is introduced which allows the recursive evaluation of classical spectrogram and spectrograms modified by the reassignment method to be extended to CTFDs and can be used to compute recursively reassigned smoothed pseudo-Wigner—Ville distributions.
27 citations
TL;DR: Bidirectional Long Short-Term Memory neural network and Wavelet Scattering Transform with Support Vector Machine classifier for detecting speech impairments of patients at the early stage of central nervous system disorders (CNSD) are adopted.
Abstract: We adopt Bidirectional Long Short-Term Memory (BiLSTM) neural network and Wavelet Scattering Transform with Support Vector Machine (WST-SVM) classifier for detecting speech impairments of patients at the early stage of central nervous system disorders (CNSD). The study includes 339 voice samples collected from 15 subjects: 7 patients with early stage CNSD (3 Huntington, 1 Parkinson, 1 cerebral palsy, 1 post stroke, 1 early dementia), other 8 subjects were healthy. Speech data is collected using voice recorder from Neural Impairment Test Suite (NITS) mobile app. Features are extracted from pitch contours, Mel-frequency cepstral coefficients (MFCC), Gammatone cepstral coefficients (GTCC), Gabor (analytic Morlet) wavelet and auditory spectrograms. 94.50% (BiLSTM) and 96.3% (WST-SVM) accuracy is achieved for solving healthy vs. impaired classification problem. The developed method can be applied for automated CNSD patient health state monitoring and clinical decision support systems as well as a part of Internet of Medical Things (IoMT).
27 citations
01 Dec 2007
TL;DR: In this article, a rule based system is developed to detect and classify the various types of power quality disturbances, such as swell, sag, interruption, harmonic, interharmonic, transient, notching and normal voltage.
Abstract: This paper presents the detection and classifications of power quality disturbances using time-frequency signal analysis. The method used is based on the pattern recognition approach. It consists of parameter estimation followed classification. Based on the spectrogram time-frequency analysis, a set of signal parameters are estimated as input to a classifier network. The power quality events that are analyzed are swell, sag, interruption, harmonic, interharmonic, transient, notching and normal voltage. The parameter estimation is characterized by voltage signal in rms per unit, waveform distortion, harmonic distortion and interharmonic distortion. A rule based system is developed to detect and classify the various types of power quality disturbances. The system has been tested with 100 data for each power quality event at SNR from OdB to 50dB to verify its performance. The results show that the system gives 100 percent accuracy of power quality signals at 30 dB of SNR.
27 citations