Name: Songs Classification Based on Mood using Low and High Level Features (en Inglés)
SKU: 57230126
Price: 70.44 USD
Availability: InStock
Author: Nandanwar, Prof Sneha ; Gandhi, Prof Vaibhav C.
ISBN: 9786138938583

portada Songs Classification Based on Mood using Low and High Level Features (en Inglés)

Songs Classification Based on Mood using Low and High Level Features (en Inglés)

Formato

Libro Físico

Autor

Prof Sneha Nandanwar

Prof Vaibhav C. Gandhi

Editorial

Scholars' Press

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

22.9x15.2x0.4 cm

Peso

0.11 kg.

ISBN13

9786138938583

Categorías

Tecnología de la información: cuestiones generales

Songs Classification Based on Mood using Low and High Level Features (en Inglés)

Prof Sneha Nandanwar (Autor) · Prof Vaibhav C. Gandhi (Autor) · Scholars' Press · Tapa Blanda

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Reseña del libro "Songs Classification Based on Mood using Low and High Level Features (en Inglés)"

Music has been associate inherent a part of human life once it involves recreation; entertainment and far recently, even as a therapeutic medium. The means by which music is composed and listened to has witnessed a huge transition from the age of magnetic tape recorders to the recent age of digital music players streaming music from the cloud. What has remained intact is that the special relation that music shares with human emotions. We most frequently like better to hear a song or music which best suits our mood at that moment. In spite of this robust correlation, most of the music software existing nowadays are still barren of providing the ability of mood-aware play-list generation. This will increase the time music listeners soak in manually selecting category of songs fitting mood or occasion, which can be avoided by expanding upon songs with the relevant feeling class they convey. The downside, however, lies within the overhead of manual annotation of music with its corresponding mood and also the challenge is to spot this side mechanically and showing intelligence.We have analyzed many classification algorithms in order to be train and check the moods of audio songs.