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The "Thaat and Raga Forest (TRF) Dataset" represents a significant advancement in computational musicology, focusing specifically on Indian Classical Music (ICM). While Western music has seen substantial attention in this field, ICM remains relatively underexplored. This manuscript presents the utilization of Deep Learning models to analyze ICM, with a primary focus on identifying Thaats and Ragas within musical compositions. Thaats and Ragas identification holds pivotal importance for various applications, including sentiment-based recommendation systems and music categorization.
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Spoken Indian Language Identification Database
(9 languages, 8 different utterance lengths)
Languages
- Assamese
- Bengali
- Gujarati
- Hindi
- Kannada
- Malayalam
- Marathi
- Tamil
- Telugu
Durations
- 30 sec
- 10 sec
- 5 sec
- 3 sec
- 1 sec
- 0.5 sec
- 0.2 sec
- 0.1 sec
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1138 Views
The steganography and steganalysis of audio, especially compressed audio, have drawn increasing attention in recent years, and various algorithms are proposed. However, there is no standard public dataset for us to verify the efficiency of each proposed algorithm. Therefore, to promote the study field, we construct a dataset including 33038 stereo WAV audio clips with a sampling rate of 44.1 kHz and duration of 10s. And, all audio files are from the Internet through data crawling, which is for a better simulation of a real detection environment.
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3393 Views