Piano Education of Children Using Musical Instrument Recognition and Deep Learning Technologies Under the Educational Psychology

The objective of the study was to enhance quality education in the traditional pre-school piano education. Deep Learning (DL) technology is applied to piano education of children to improve their interest in learning music. Firstly, the problems of the traditional piano education of children were analyzed with the teaching patterns discussed under educational psychology, and a targeted music education plan was established. Secondly, musical instrument recognition technology was introduced, and the musical instrument recognition model was implemented based on DL. Thirdly, the proposed model was applied to the piano education of children to guide the music learning of students and improve their interest in piano learning. The feature recognition and acquisition of the proposed model were improved. Finally, the different teaching patterns were comparatively analyzed through the Questionnaire Survey (QS). The experimental results showed that the instrument recognition accuracy of Hybrid Neural Network (HNN) is 97.2%, and with the increase of iterations, the recognition error rate of the model decreases and stabilizes. Therefore, the proposed HNN based on DL for musical instrument recognition can accurately identify musical features. The QS results showed that the introduction of musical instrument recognition technology in the piano education of children can improve their interest in piano learning. Therefore, the establishment of the piano education patterns based on the piano education model can improve the effectiveness of teaching piano to students. This research provides a reference for the intelligentization of children's piano education.

Automatic Surgical Instrument Recognition—A Case of Comparison Study between the Faster R-CNN, Mask R-CNN, and Single-Shot Multi-Box Detectors

In various studies, problems with surgical instruments in the operating room are usually one of the major causes of delays and errors. It would be of great help, in surgery, to quickly and automatically identify and keep count of the surgical instruments in the operating room using only video information. In this study, the recognition rate of fourteen surgical instruments is studied using the Faster R-CNN, Mask R-CNN, and Single Shot Multi-Box Detectors, which are three deep learning networks in recent studies that exhibited near real-time object detection and identification performance. In our experimental studies using screen captures of real surgery video clips for training and testing, this study found that that acceptable accuracy and speed tradeoffs can be achieved by the Mask R-CNN classifier, which exhibited an overall average precision of 98.94% for all the instruments.

Knowledge Graph Embedding-Based Domain Adaptation for Musical Instrument Recognition

Convolutional neural networks raised the bar for machine learning and artificial intelligence applications, mainly due to the abundance of data and computations. However, there is not always enough data for training, especially when it comes to historical collections of cultural heritage where the original artworks have been destroyed or damaged over time. Transfer Learning and domain adaptation techniques are possible solutions to tackle the issue of data scarcity. This article presents a new method for domain adaptation based on Knowledge graph embeddings. Knowledge Graph embedding forms a projection of a knowledge graph into a lower-dimensional where entities and relations are represented into continuous vector spaces. Our method incorporates these semantic vector spaces as a key ingredient to guide the domain adaptation process. We combined knowledge graph embeddings with visual embeddings from the images and trained a neural network with the combined embeddings as anchors using an extension of Fisher’s linear discriminant. We evaluated our approach on two cultural heritage datasets of images containing medieval and renaissance musical instruments. The experimental results showed a significant increase in the baselines and state-of-the-art performance compared with other domain adaptation methods.

An Automatic Instrument Recognition Approach Based on Deep Convolutional Neural Network

Background: This paper presents an automatic instrument recognition method highlighting the deep learning aspect of instrument identification in order to advance the automatic process of video monitoring remotely equipment of substation. Methodology: This work utilizes the Scale Invariant Feature Transform approach (SIFT) and the Gaussian difference model for instrument positioning while proposing a design scheme of instrument identification system. Results: The experimental outcomes obtained proves that the proposed system is capable of automatically recognizing a modest graphical interface and study independently while improving the operation effectiveness of appliance and realizing the purpose of spontaneous self-check. The proposed approach is applicable for musical instrument recognition and it provides 92% of the accuracy rate, 87.5% precision value and recall rate of 91.2%. Conclusion: The comparative analysis with other state of the art methods justifies that the proposed deep learning based music recognition method outperforms the other existing approaches in terms of accuracy, thereby providing a practicable music instrument recognition solution.

Influence of input data representations for time-dependent instrument recognition

An important preprocessing step for several music signal processing algorithms is the estimation of playing instruments in music recordings. To this aim, time-dependent instrument recognition is realized by a neural network with residual blocks in this approach. Since music signal processing tasks use diverse time-frequency representations as input matrices, the influence of different input representations for instrument recognition is analyzed in this work. Three-dimensional inputs of short-time Fourier transform (STFT) magnitudes and an additional time-frequency representation based on phase information are investigated as well as two-dimensional STFT or constant-Q transform (CQT) magnitudes. As additional phase representations, the product spectrum (PS), based on the modified group delay, and the frequency error (FE) matrix, related to the instantaneous frequency, are used. Training and evaluation processes are executed based on the MusicNet dataset, which enables the estimation of seven instruments. With a higher number of frequency bins in the input representations, an improved instrument recognition of about 2 % in F1-score can be achieved. Compared to the literature, frame-level instrument recognition can be improved for different input representations.