Poetic meter classification using TMS320C6713 DSK

Soumya, S.; Saju, Solomon; Rajan, Rajeev; Sebastian, Nelsa

doi:10.1109/cspc.2017.8305859

Cited by 4 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In (Soumya et al, 2017) the authors present a poetic meter classi cation algorithm that is implemented on a TMS320C6713 oating point digital signal processor for the meters of the Malayalam language. A melodic pitch is computed from the audio les using a Sonic Visualizer plug-in called MELODIA.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Based System for Identification of Light and Heavy Syllables in Sanskrit Verse

Wali

2023

Preprint

View full text Add to dashboard Cite

Prosody provides information regarding a rhythmic structure present in poetry or a verse for any language. In particular, for the Sanskrit language, recognizing the prosody (called “Chandas” in Sanskrit literature) of the verse is entirely dependent on the identification of the light (“Laghu”) and heavy (“Guru”) syllables. In this regard, we propose an efficient and low-complexity two-step machine learning based system for identifying the Laghus and Gurus in Sanskrit verses, by developing an ingenious representation for the syllables. Our novel idea of preparing the first data-set and categorizing the syllables using the first machine learning model enables us to solve the sequence-to-sequence conversion problem as a simple classification problem in the second machine learning model, with two classes namely, the Laghu (denoted by a ‘0’) and the Guru (denoted by a ‘1’). The proposed data representation method and the two-step technique have been found to give an accuracy of more than 99%.

show abstract

Section: Related Workmentioning

confidence: 99%

Machine Learning Based System for Identification of Light and Heavy Syllables in Sanskrit Verse

Wali

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…However, further, enhancement can be made to improve a set of emotions for achieving more promising results. Soumya et al [11] conducted a classification of Persian poems based on sonic visualization. Emotions can be detected from audio streams.…”

Section: Related Workmentioning

confidence: 99%

Classification of Poetry Text Into the Emotional States Using Deep Learning Technique

et al. 2020

View full text Add to dashboard Cite

The classification of emotional states from poetry or formal text has received less attention by the experts of computational intelligence in recent times as compared to informal textual content like SMS, email, chat, and online user reviews. In this study, an emotional state classification system for poetry text is proposed using the latest and cutting edge technology of Artificial Intelligence, called Deep Learning. For this purpose, an attention-based C-BiLSTM model is implemented on the poetry corpus. The proposed approach classifies the text of poetry into different emotional states, like love, joy, hope, sadness, anger, etc. Different experiments are conducted to evaluate the efficiency of the proposed system as compared to other state-of-art methods as well as machine learning and deep learning methods. Experimental results depict that the proposed model outperformed the baselines studies with 88% accuracy. Furthermore, the analysis of the statistical experiment also validates the performance of the proposed approach.

show abstract