Spectral-Temporal Receptive Field-Based Descriptors and Hierarchical Cascade Deep Belief Network for Guitar Playing Technique Classification.

Music information retrieval is of great interest in audio signal processing. However, relatively little attention has been paid to the playing techniques of musical instruments. This work proposes an automatic system for classifying guitar playing techniques (GPTs). Automatic classification for GPTs is challenging because some playing techniques differ only slightly from others. This work presents a new framework for GPT classification: it uses a new feature extraction method based on spectral-temporal receptive fields (STRFs) to extract features from guitar sounds. This work applies a supervised deep learning approach to classify GPTs. Specifically, a new deep learning model, called the hierarchical cascade deep belief network (HCDBN), is proposed to perform automatic GPT classification. Several simulations were performed and the datasets of: 1) data on onsets of signals; 2) complete audio signals; and 3) audio signals in a real-world environment are adopted to compare the performance. The proposed system improves upon the F-score by approximately 11.47% in setup 1) and yields an F-score of 96.82% in setup 2). The results in setup 3) demonstrate that the proposed system also works well in a real-world environment. These results show that the proposed system is robust and has very high accuracy in automatic GPT classification.

Deep Learning-Based Violin Bowing Action Recognition.

We propose a violin bowing action recognition system that can accurately recognize distinct bowing actions in classical violin performance. This system can recognize bowing actions by analyzing signals from a depth camera and from inertial sensors that are worn by a violinist. The contribution of this study is threefold: (1) a dataset comprising violin bowing actions was constructed from data captured by a depth camera and multiple inertial sensors; (2) data augmentation was achieved for depth-frame data through rotation in three-dimensional world coordinates and for inertial sensing data through yaw, pitch, and roll angle transformations; and, (3) bowing action classifiers were trained using different modalities, to compensate for the strengths and weaknesses of each modality, based on deep learning methods with a decision-level fusion process. In experiments, large external motions and subtle local motions produced from violin bow manipulations were both accurately recognized by the proposed system (average accuracy > 80%).

Baseball Player Behavior Classification System Using Long Short-Term Memory with Multimodal Features.

In this paper, a preliminary baseball player behavior classification system is proposed. By using multiple IoT sensors and cameras, the proposed method accurately recognizes many of baseball players' behaviors by analyzing signals from heterogeneous sensors. The contribution of this paper is threefold: (i) signals from a depth camera and from multiple inertial sensors are obtained and segmented, (ii) the time-variant skeleton vector projection from the depth camera and the statistical features extracted from the inertial sensors are used as features, and (iii) a deep learning-based scheme is proposed for training behavior classifiers. The experimental results demonstrate that the proposed deep learning behavior system achieves an accuracy of greater than 95% compared to the proposed dataset.