Research on Music Classification Technology Based on Deep Learning

With the advent of the digital music era, digital audio sources have exploded. Music classification (MC) is the basis of managing massive music resources. In this paper, we propose a MC method based on deep learning to improve feature extraction and classifier design based on MIDI (musical instrument digital interface) MC task. Considering that the existing classification technology is limited by the shallow structure, it is difficult for the classifier to learn the time sequence and semantic information of music; this paper proposes a MIDIMC method based on deep learning. In the experiment, we use the MC method proposed in this paper to achieve 90.1% classification accuracy, which is better than the existing classification method based on BP neural network, and verify the music with its classification accuracy. The key point is that the music division method used in this paper has correct MC efficiency. However, due to the limited ability and time involved in the interdisciplinary field, the methodology of this paper has certain limitations, which still needs further research and improvement.

A Co-optimization PSO for Fuzzy Rule-Based Classifier Design Problem Based on Enlarged Hedge Algebras

Fuzzy Rule-Based Classifier (FRBC) design problem has been widely studied due to many practical applications. Hedge Algebras based Classifier Design Methods (HACDMs) are the outstanding and effective approaches because these approaches based on a mathematical formal formalism allowing the fuzzy sets based computational semantics generated from their inherent qualitative semantics of linguistic terms. HACDMs include two phase optimization process. The first phase is to optimize the semantic parameter values by applying an optimization algorithm. Then, in the second phase, the optimal fuzzy rule based system for FRBC is extracted based on the optimal semantic parameter values provided by the first phase. The performance of FRBC design methods depends on the quality of the applied optimization algorithms. This paper presents our proposed co-optimization Particle Swarm Optimization (PSO) algorithm for designing FRBC with trapezoidal fuzzy sets based computational semantics generated by Enlarged Hedge Algebras (EHAs). The results of experiments executed over 23 real world datasets have shown that Enlarged Hedge Algebras based classifier with our proposed co-optimization PSO algorithm outperforms the existing classifiers which are designed based on Enlarged Hedge Algebras methodology with two phase optimization process and the existing fuzzy set theory based classifiers.

Building an Open Source Classifier for the Neonatal EEG Background: A Systematic Feature-Based Approach From Expert Scoring to Clinical Visualization

Neonatal brain monitoring in the neonatal intensive care units (NICU) requires a continuous review of the spontaneous cortical activity, i.e., the electroencephalograph (EEG) background activity. This needs development of bedside methods for an automated assessment of the EEG background activity. In this paper, we present development of the key components of a neonatal EEG background classifier, starting from the visual background scoring to classifier design, and finally to possible bedside visualization of the classifier results. A dataset with 13,200 5-minute EEG epochs (8–16 channels) from 27 infants with birth asphyxia was used for classifier training after scoring by two independent experts. We tested three classifier designs based on 98 computational features, and their performance was assessed with respect to scoring system, pre- and post-processing of labels and outputs, choice of channels, and visualization in monitor displays. The optimal solution achieved an overall classification accuracy of 97% with a range across subjects of 81–100%. We identified a set of 23 features that make the classifier highly robust to the choice of channels and missing data due to artefact rejection. Our results showed that an automated bedside classifier of EEG background is achievable, and we publish the full classifier algorithm to allow further clinical replication and validation studies.

Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography

Preterm birth is the leading cause of death in newborns and the survivors are prone to health complications. Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy. The current methods used in clinical practice to diagnose preterm labor, the Bishop score or cervical length, have high negative predictive values but not positive ones. In this work we analyzed the performance of computationally efficient classification algorithms, based on electrohysterographic recordings (EHG), such as random forest (RF), extreme learning machine (ELM) and K-nearest neighbors (KNN) for imminent labor (<7 days) prediction in women with TPL, using the 50th or 10th–90th percentiles of temporal, spectral and nonlinear EHG parameters with and without obstetric data inputs. Two criteria were assessed for the classifier design: F1-score and sensitivity. RFF1_2 and ELMF1_2 provided the highest F1-score values in the validation dataset, (88.17 ± 8.34% and 90.2 ± 4.43%) with the 50th percentile of EHG and obstetric inputs. ELMF1_2 outperformed RFF1_2 in sensitivity, being similar to those of ELMSens (sensitivity optimization). The 10th–90th percentiles did not provide a significant improvement over the 50th percentile. KNN performance was highly sensitive to the input dataset, with a high generalization capability.

Combination of Pattern Classifiers Based on Naive Bayes and Fuzzy Integral Method for Biological Signal Applications

Background: Achieving the best possible classification accuracy is the main purpose of each pattern recognition scheme. An interesting area of classifier design is to design for biomedical signal and image processing. Materials and Methods: In the current work, in order to increase recognition accuracy, a theoretical frame for combination of classifiers is developed. This method uses different pattern representations to show that a wide range of existing algorithms could be incorporated as the particular cases of compound classification where all the pattern representations are used jointly to make an accurate decision. Results: The results show that the combination rules developed under the Naive Bayes and Fuzzy integral method outperforms other classifier combination schemes. Conclusion: The performance of different combination schemes has been studied through an experimental comparison of different classifier combination plans. The dataset used in the article has been obtained from biological signals.

IMPLEMENTASI TEORI NAIVE BAYES DALAM KLASIFIKASI CALON MAHASISWA BARU STMIK KHARISMA MAKASSAR

STMIK KHARISMA Makassar has graduated thousands of alumni since it was founded. However, the number of students registering is uncertain every year, although from 2016 to 2019 there has been an increase in the number of registrations. The problem is the percentage of the number of prospective students registering has actually decreased significantly. The purpose of this research is to implement the Naive Bayes theory in classification of STMIK KHARISMA Makassar prospective students. This research basically uses the Naive Bayes theory as a classifier, and is made using the Python programming language. At the classifier design stage, there were a total of 499 data collected from 2016 to 2019. The data was divided by a ratio of 80:20 for training data and test data. The result from the research indicate the level of accuracy of the classifier reaches 73%.