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Bird classification based on their sound patterns
International Journal of Speech Technology, 2016In this paper we focus on automatic bird classification based on their sound patterns. This is useful in the field of ornithology for studying bird species and their behavior based on their sound. The proposed methodology may be used to conduct survey of birds.
M. A. Raghuram +3 more
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Attention-Based Sound Classification Pipeline with Sound Spectrum
2023 IEEE Sensors Applications Symposium (SAS), 2023Ki In Tan, Seanglidet Yean, Bu-Sung Lee
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Analysis and Automatic Classification of Breath Sounds
IEEE Transactions on Biomedical Engineering, 1984Analysis of breath sounds (auscultation) is an important part of the diagnosis of pulmonary diseases. An automatic breathsounds classification scheme is suggested. Types of normal and abnormal breath sounds are classified, with the goal of providing the physician with a diagnostic assist device. The classification is performed in two levels.
A, Cohen, D, Landsberg
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The Use of Sound Symbolism in Sentiment Classification
2012In this paper, we present a method for estimating the sentiment polarity of Japanese sentences including onomatopoeic words. Onomatopoeic words imitate the sounds they represent and can help us understand the sentiment of the sentence. Although there are many onomatopoeic words in Japanese, conventional sentiment classification methods have not taken ...
Takuma Igarashi +3 more
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Automatic Classification of Musical Instrument Sounds
The Journal of the Acoustical Society of America, 1999The aim of the present study is to show that the process of automatic classification of musical instrument sounds is possible on the basis of a limited number of parameters. However, due to the complexity as well as to the unrepeatable nature of musical sounds, both steady and transient states should be taken into account while creating feature vectors.
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Classification of Sounds Indicative of Respiratory Diseases
2019This work presents a system achieving classification of respiratory sounds directly related to various diseases of the human respiratory system, such as asthma, COPD, and pneumonia. We designed a feature set based on wavelet packet analysis characterizing data coming from four sound classes, i.e. crack, wheeze, normal, crack+wheeze.
Stavros Ntalampiras, Ilyas Potamitis
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Environment sound classification using an attention-based residual neural network
Neurocomputing, 2021Achyut Mani Tripathi
exaly
Heart sound classification with signal instant energy and stacked autoencoder network
Biomedical Signal Processing and Control, 2021Ömer Deperlioglu
exaly