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Results of the studies analyzed show that, in general, the process of time signature estimation is a difficult one. More than 110 publications from top journals and conferences written in English were reviewed, and each of the research selected was fully examined to demonstrate the feasibility of the approach used, the dataset, and accuracy obtained. The results of the research have been divided into two categories: classical and deep learning techniques, and are summarized in order to make suggestions for future study. The purpose of this review is to investigate the effectiveness of these methods and how they perform on different types of input signals (audio and MIDI). This paper presents a thorough review of methods used in various research articles published in the field of time signature estimation and detection from 2003 to the present. This result shows that the integration of crowd-sourced annotations from heterogeneous symbolic music representations using data fusion is a suitable strategy for addressing challenging MIR tasks such as ACE. DECIBEL improves all tested state-of-the-art ACE methods by 0.5 to 13.6 percentage points. Next, DECIBEL uses data fusion to integrate all estimated chord sequences into one final output sequence. Tab files are transformed into untimed chord sequences and then aligned to the audio. MIDI files are aligned to the audio, followed by a MIDI chord estimation step.
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For audio, state-of-the-art audio ACE methods are used. From an audio file and a set of MIDI and tab files corresponding to the same popular music song, DECIBEL first estimates chord sequences. We propose DECIBEL, a new ACE system that exploits heterogeneous musical representations, specifically MIDI and tab files, to improve audio-based ACE methods. Although it has been a task in the annual benchmarking evaluation MIREX for over 10 years, ACE is not yet a solved problem, since performance has stagnated and modern systems have started to tune themselves to subjective training data. The task consists of segmenting a music recording or score and assigning a chord label to each segment. Automatic Chord Estimation (ACE) is a fundamental task in Music Information Retrieval (MIR) and has applications in both music performance and MIR research.