Evidence map›Paper›PMID 35733556›Full record

ArticleComputational intelligence and neuroscience2022

Intelligent Analysis and Classification of Piano Music Gestures with Multimodal Recordings.

Xunyun Chang, Liangqing Peng

Abstract read
In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Xunyun ChangShaoyang University, Shaoyang, Hunan, China.ORCID https://orcid.org/0000-0002-0084-7856
Liangqing PengShaoyang University, Shaoyang, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the traditional recording system, recording any music includes a sizeable instrumental setup and allocates space for the music players. Lighter and fewer devices are replacing larger instruments due to technological advancement and epidemic environmental conditions. This research focuses on text, but audio and video types are also considered. Multiple signal classification with a 5G-based wireless communication network algorithm is implemented to perform the automatic recording and classification of the music data. In this research, a multi-modal gesture recognition dataset is considered for analysis. The dataset was obtained using sensor networks and an intelligent system to record the musical gestures and classify the recorded gestures. The development of machine learning algorithms is not limited to similar technological concepts. Still, it extends to almost all other technical resources such as the 5G network, signal processing, networking, and all other technical resources. This would lead to additional engineering challenges that are utilized in most cases, such as the development of gestures with multi-mode recording. This research has proposed MSA with WCN algorithm to perform intelligent analysis and classification of piano music gestures and is compared with the existing

Indexed as

GesturesMusicAlgorithmsPattern Recognition, AutomatedSignal Processing, Computer-Assisted

Identifiers

PMID35733556
PMCPMC9208914

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.