Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Bhuwan Bhattarai"'
Autor:
Bhuwan Bhattarai, Joonwhoan Lee
Publikováno v:
Applied Sciences, Vol 14, Iss 2, p 492 (2024)
In this paper, first, we delved into the experiment by comparing various attention mechanisms in the semantic pixel-wise segmentation framework to perform frame-level transcription tasks. Second, the Viterbi algorithm was utilized by transferring the
Externí odkaz:
https://doaj.org/article/8cec638c3bea437e8841103186c2d1d3
Autor:
Bhuwan Bhattarai, Joonwhoan Lee
Publikováno v:
Applied Sciences, Vol 13, Iss 21, p 11882 (2023)
Music transcription is the process of transforming recorded sound of musical performances into symbolic representations such as sheet music or MIDI files. Extensive research and development have been carried out in the field of music transcription an
Externí odkaz:
https://doaj.org/article/ae2d24c729c44eeba5990e861e253f12
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
Abstract Affective computing has suffered by the precise annotation because the emotions are highly subjective and vague. The music video emotion is complex due to the diverse textual, acoustic, and visual information which can take the form of lyric
Externí odkaz:
https://doaj.org/article/36d1baaf4f5f432b9e390dd08bb4c628
Publikováno v:
IEEE Access, Vol 8, Pp 162625-162633 (2020)
Sound event detection (SED) is a reasonable choice in a number of application domains including cattle sheds, dense forests, or any dark environments where visual objects are usually concealed or invisible. This study presents an autonomous monitorin
Externí odkaz:
https://doaj.org/article/39e7d794ef734fbcbc91a3dd2afb43ec
Publikováno v:
IEEE Access, Vol 8, Pp 206016-206027 (2020)
Music source separation is one of the old and challenging problems in music information retrieval society. Improvements in deep learning lead to big progress in decomposing music into its constitutive components with a variety of music. This research
Externí odkaz:
https://doaj.org/article/294c8988a87943aabd04a667df8fa6ad
Publikováno v:
Applied Sciences, Vol 12, Iss 19, p 9571 (2022)
This paper presents two methods to understand the rhythmic patterns of the voice in Korean traditional music called Pansori. We used semantic segmentation and classification-based structural analysis methods to segment the seven rhythmic categories o
Externí odkaz:
https://doaj.org/article/6d4815238af24e058d4002284ebc13a6
Publikováno v:
Sensors, Vol 21, Iss 19, p 6565 (2021)
Plant diseases must be identified at the earliest stage for pursuing appropriate treatment procedures and reducing economic and quality losses. There is an indispensable need for low-cost and highly accurate approaches for diagnosing plant diseases.
Externí odkaz:
https://doaj.org/article/6ee1d90377bf46b3b809ea3604d7d23f
Publikováno v:
Sensors, Vol 21, Iss 14, p 4927 (2021)
Music videos contain a great deal of visual and acoustic information. Each information source within a music video influences the emotions conveyed through the audio and video, suggesting that only a multimodal approach is capable of achieving effici
Externí odkaz:
https://doaj.org/article/4d355946e34740e580c5aa6f4b7869b7
Publikováno v:
Circuits, Systems, and Signal Processing. 42:1083-1104
Music source separation has traditionally followed the encoder-decoder paradigm (e.g., hourglass, U-Net, DeconvNet, SegNet) to isolate individual music components from mixtures. Such networks, however, result in a loss of location-sensitivity, as low
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::676eaa9339930f7f8a23a0e61adcb5b9
https://doi.org/10.1007/s00034-022-02166-5
https://doi.org/10.1007/s00034-022-02166-5
Publikováno v:
IEEE Access, Vol 8, Pp 206016-206027 (2020)
Music source separation is one of the old and challenging problems in music information retrieval society. Improvements in deep learning lead to big progress in decomposing music into its constitutive components with a variety of music. This research
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::85919ecc8187b21a98b7affa9c868d84
https://doi.org/10.1109/access.2020.3037773
https://doi.org/10.1109/access.2020.3037773