Automatic classification of married couples' behavior using audio features

Automatic classification of music based on the correlation between mood, linguistic and audio features

Bangladesh Journal of Scientific and Industrial Research ◽

10.3329/bjsir.v51i1.27063 ◽

2016 ◽

Vol 51 (1) ◽

pp. 55-60

Author(s):

NA Rakib ◽

Md MB Sobhan ◽

SMZ Farhan ◽

MI Zaber

Keyword(s):

Automatic Classification ◽

Divine Inspiration ◽

Audio Features ◽

The Individual

The emergence of the music in recent times has been enviable. Some people consider music to be an integral part of their regular lives, while others sometimes even consider music to be some divine inspiration setting the mood for them for the rest of the day. For such people, a well-trimmed precise playlist of the songs that they would love to listen to, based on genre or mood of the songs, is priceless. Genre of an individual song is very much available, as that information is mostly provided within the song, but getting to judge the mood of the song is much more of a challenge. If it is a challenge itself for one distinct song, then one can easily imagine the hassle that a person faces when selecting a playlist of songs from a huge library of music. This ultimately gives rise to the importance of the classification of music based on the mood of the individual songs.This paper establishes such a method, which ultimately works with a combination of features, such as the linguistic and audio features of a song to classify a song according to the mood the song represents or is appropriate for. These features are then used in conjunction with several metrics to find out their relevance or relationships and measured for validation purposes.Bangladesh J. Sci. Ind. Res. 51(1), 55-60, 2016

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Automatic Classification of Monosyllabic and Multisyllabic Birds Using PDHF

Electronics ◽

10.3390/electronics10050624 ◽

2021 ◽

Vol 10 (5) ◽

pp. 624

Author(s):

Abdullah Alghamdi ◽

Tooba Mehtab ◽

Rizwan Iqbal ◽

Mona Leeza ◽

Noman Islam ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Bird Species ◽

Cost Effective ◽

Automatic Classification ◽

Audio Recording ◽

Audio Features ◽

Acoustic Surveys ◽

Artificial Neural Network Ann

Bioacoustics plays an important role in the conservation of bird species. Bio-acoustic surveys based on autonomous audio recording are both cost-effective and time-efficient. However, there are many bird species with different patterns of vocalization, and it is a challenging task to deal with them. Previous studies have revealed that many authors focus on the segmentation of bird audio without considering specific patterns of bird vocalization. Based on the existing literature, currently there is no work on the segmentation of monosyllabic and multisyllabic birds, separately. Therefore, this research addresses the aforementioned concern and also proposes a collection of audio features named ‘Perceptual, Descriptive, and Harmonic Features (PDHFs)’ that gives promising results in the classification of bird vocalization. Moreover, the classification results improved when monosyllabic and multisyllabic birds were classified separately. To analyze the performance of PDHFs, different classifiers were used in which Artificial neural network (ANN) outperformed other classifiers and demonstrated an accuracy of 98%.

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Interpreting HVEM of muscle-cell impulse networks

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010012103x ◽

1992 ◽

Vol 50 (1) ◽

pp. 126-127

Author(s):

Paul DeCosta ◽

Kyugon Cho ◽

Stephen Shemlon ◽

Heesung Jun ◽

Stanley M. Dunn

Keyword(s):

Structural Analysis ◽

Muscle Cell ◽

Electron Micrographs ◽

Relative Size ◽

Automatic Classification ◽

Nerve Fibers ◽

Analysis Algorithm ◽

Structural Networks

Introduction: The analysis and interpretation of electron micrographs of cells and tissues, often requires the accurate extraction of structural networks, which either provide immediate 2D or 3D information, or from which the desired information can be inferred. The images of these structures contain lines and/or curves whose orientation, lengths, and intersections characterize the overall network.Some examples exist of studies that have been done in the analysis of networks of natural structures. In, Sebok and Roemer determine the complexity of nerve structures in an EM formed slide. Here the number of nodes that exist in the image describes how dense nerve fibers are in a particular region of the skin. Hildith proposes a network structural analysis algorithm for the automatic classification of chromosome spreads (type, relative size and orientation).

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Automatic classification of sleep stages

Electroencephalography and Clinical Neurophysiology ◽

10.1016/s0013-4694(97)88102-7 ◽

1997 ◽

Vol 103 (1) ◽

pp. 44 ◽

Cited By ~ 2

Author(s):

B Kemp

Keyword(s):

Automatic Classification ◽

Sleep Stages

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An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0107 ◽

2017 ◽

Vol 7 (7) ◽

pp. 408

Author(s):

Yashpal Jitarwal ◽

Tabrej Ahamad Khan ◽

Pawan Mangal

Keyword(s):

Image Processing ◽

Texture Features ◽

Automatic Classification ◽

Processing Technique ◽

Image Processing Technique ◽

Advance Technique ◽

Time Required ◽

Fruit Sorting ◽

Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

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