Musical Instrument Recognition by XGBoost Combining Feature Fusion

06/02/2022
by   Yijie Liu, et al.
0

Musical instrument classification is one of the focuses of Music Information Retrieval (MIR). In order to solve the problem of poor performance of current musical instrument classification models, we propose a musical instrument classification algorithm based on multi-channel feature fusion and XGBoost. Based on audio feature extraction and fusion of the dataset, the features are input into the XGBoost model for training; secondly, we verified the superior performance of the algorithm in the musical instrument classification task by com-paring different feature combinations and several classical machine learning models such as Naive Bayes. The algorithm achieves an accuracy of 97.65 experiments provide a reference for feature selection in feature engineering for musical instrument classification.

READ FULL TEXT

page 3

page 5

page 10

research
11/30/2019

Predominant Musical Instrument Classification based on Spectral Features

This work aims to examine one of the cornerstone problems of Musical Ins...
research
08/08/2021

Deep Single Shot Musical Instrument Identification using Scalograms

Musical Instrument Identification has for long had a reputation of being...
research
07/11/2023

Musical Excellence of Mridangam: an introductory review

This is an introductory review of Musical Excellence of Mridangam by Dr....
research
05/12/2018

Extended pipeline for content-based feature engineering in music genre recognition

We present a feature engineering pipeline for the construction of musica...
research
11/17/2015

Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks

Traditional methods to tackle many music information retrieval tasks typ...
research
09/16/2019

Musical Instrument Classification via Low-Dimensional Feature Vectors

Music is a mysterious language that conveys feeling and thoughts via dif...

Please sign up or login with your details

Forgot password? Click here to reset