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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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Journal ArticleDOI
TL;DR: The presented TF decomposition method produces a noise-resistant high definition TF representation of RS signals as compared to the conventional linear TF analysis methods, yet preserving the low computational complexity as compared with those quadraticTF analysis methods.
Abstract: Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious sounds (ASs). Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused on the analysis of the evolution of symptom-related signal components in joint time-frequency (TF) plane. This paper proposes a new signal identification and extraction method for various ASs based on instantaneous frequency (IF) analysis. The presented TF decomposition method produces a noise-resistant high definition TF representation of RS signals as compared to the conventional linear TF analysis methods, yet preserving the low computational complexity as compared to those quadratic TF analysis methods. The discarded phase information in conventional spectrogram has been adopted for the estimation of IF and group delay, and a temporal-spectral dominance spectrogram has subsequently been constructed by investigating the TF spreads of the computed time-corrected IF components. The proposed dominance measure enables the extraction of signal components correspond to ASs from noisy RS signal at high noise level. A new set of TF features has also been proposed to quantify the shapes of the obtained TF contours, and therefore strongly, enhances the identification of multicomponents signals such as polyphonic wheezes. An overall accuracy of 92.4±2.9% for the classification of real RS recordings shows the promising performance of the presented method.

78 citations

Posted Content
TL;DR: A simple audio classification algorithm based on treating sound spectrograms as texture images, inspired by an earlier visual classification scheme particularly efficient at classifying textures achieves surprisingly good performance in musical instrument classification experiments.
Abstract: Time-frequency representations of audio signals often resemble texture images. This paper derives a simple audio classification algorithm based on treating sound spectrograms as texture images. The algorithm is inspired by an earlier visual classification scheme particularly efficient at classifying textures. While solely based on time-frequency texture features, the algorithm achieves surprisingly good performance in musical instrument classification experiments.

78 citations

Journal ArticleDOI
TL;DR: Results demonstrate that the proposed probabilistic model for multiple-instrument automatic music transcription outperforms leading approaches from the transcription literature, using several error metrics.
Abstract: In this work, a probabilistic model for multiple-instrument automatic music transcription is proposed. The model extends the shift-invariant probabilistic latent component analysis method, which is used for spectrogram factorization. Proposed extensions support the use of multiple spectral templates per pitch and per instrument source, as well as a time-varying pitch contribution for each source. Thus, this method can effectively be used for multiple-instrument automatic transcription. In addition, the shift-invariant aspect of the method can be exploited for detecting tuning changes and frequency modulations, as well as for visualizing pitch content. For note tracking and smoothing, pitch-wise hidden Markov models are used. For training, pitch templates from eight orchestral instruments were extracted, covering their complete note range. The transcription system was tested on multiple-instrument polyphonic recordings from the RWC database, a Disklavier data set, and the MIREX 2007 multi-F0 data set. Results demonstrate that the proposed method outperforms leading approaches from the transcription literature, using several error metrics.

76 citations

Journal ArticleDOI
TL;DR: An extensive survey and an algorithm taxonomy is presented and each algorithm is reviewed according to a set of criteria relating to their success in application, concluding that none of these algorithms fully meets these criteria.

76 citations

Journal ArticleDOI
TL;DR: A novel radar-based human motion recognition technique that exploits the temporal sequentiality of human motions by employing a stacked recurrent neural network with long short-term memory units to extract sequential features for automatic motion classification.

76 citations


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