Deep learning-based automatic downbeat tracking: a brief review

Abstract

As an important format of multimedia, music has filled almost everyone's life. Automatic analyzing music is a significant step to satisfy people's need for music retrieval and music recommendation in an effortless way. Thereinto, downbeat tracking has been a fundamental and continuous problem in Music Information Retrieval (MIR) area. Despite significant research efforts, downbeat tracking still remains a challenge. Previous researches either focus on feature engineering (extracting certain features by signal processing, which are semi-automatic solutions); or have some limitations: they can only model music audio recordings within limited time signatures and tempo ranges. Recently, deep learning has surpassed traditional machine learning methods and has become the primary algorithm in feature learning; the combination of traditional and deep learning methods also has made better performance. In this paper, we begin with a background introduction of downbeat tracking problem. Then, we give detailed discussions of the following topics: system architecture, feature extraction, deep neural network algorithms, datasets, and evaluation strategy. In addition, we take a look at the results from the annual benchmark evaluation-Music Information Retrieval Evaluation eXchange (MIREX), as well as the developments in software implementations. Although much has been achieved in the area of automatic downbeat tracking, Jiancheng Lv some problems still remain. We point out these problems and conclude with possible directions and challenges for future research.

Spatial–Semantic and Temporal Attention Mechanism-Based Online Multi-Object Tracking

“…Tracking is a fundamental task in any video processing that requires some degree of reasoning about objects of interest [4][5][6]. The methods of object tracking can be divided into two categories: the method suitable for single-object tracking [7][8][9], and the method suitable for multi-object tracking [10][11][12].…”

“…A motion model analyzes the motion curve of the target in the history frames and predicts the position of the target in the current frame [37,38]. Most single-object trackers do not use a motion model [6][7][8][9]. However, the motion model has proven helpful in multi-object tracking, which can help locate targets and realize the correspondence of multi-target labels in different frames.…”

The use of deep learning algorithm and digital media art in all-media intelligent electronic music system

“…In Eq (11), P t represents a certain type of confidence corresponding to the current action time, and C t suggests the real category, and r A corresponds to the specific return value. The corresponding relationship between return value and strategy and ordinary action can be expressed as Eq (12).…”

“…In Eq (12), r e refers to the specific return value under the current situation. In other words, in the final action, if there is no fault in the classification of the image, the system will receive a positive return; otherwise, it will be a negative return.…”

“…Meantime, music, as a possible multimodal strategy, could reduce the impact of anger on driving performance and driver's subjective experience [10]. Kim et al (2020) evaluated the application of deep transfer learning method in the field of music information retrieval, and provided certain reference for the design of deep data method in music field by considering multiple target data sets [11]; Jia et al (2019) based on downbeat tracking problem, introduced the structure of music system, feature extraction, deep neural network algorithm, data set, and evaluation strategy, providing direction for related research of music information retrieval [12]; Song et al (2018) proposed an automatic annotation algorithm based on deep recurrent neural network, and applied it to music information retrieval. The results showed that the method had faster training speed and less memory consumption [13]; Baro et al [14], based on convolution recurrent neural network, put forward a complete handwritten music recognition system, which is expected to realize the conversion of music score image into computer-readable format.…”