Abstract
Automatic extraction of features from harmonic information of music audio is considered in this paper. Automatically obtaining of relevant information is necessary not just for analysis but also for the commercial issue such as music program of tutoring and generating of lead sheet. Two aspects of harmony are considered, chord and global key, facing the issue of the extraction problem by the algorithm of machine learning. Contribution here is to recognize chords in the music by the feature extraction method (voiced models) that performd better than manually one. The modelling carried out chord sequence, getting from frame-by-frame basis, which is known in recognition of the chord system. Technique of machine learning such the convolutional neural network (CNN) will systematically extract the chord sequence to achieve the superiority context model. Then, traditional classification is used to create the key classifier which is better than others or manually one. Datasets used to evaluate the proposed model show good achievement results compared with existing one.
Highlights
Complexity these shortcomings. e basic idea is to train the model to predict string designations and chord functions, as shown in Figure 1 [5]
Extract sequences of chords aligned with time from a given. e acoustic music signal is commonly referred to as automatic string estimation (ACE), and it is a well-studied topic in Music Information Retrieval (MIR)
ACE systems consist of some variation in extracting acoustic features followed by a pattern matching step where the acoustic features are attached to the chord labels [8]
Summary
Complexity these shortcomings. e basic idea is to train the model to predict string designations and chord functions, as shown in Figure 1 [5]. Study has shown that such models have been applied to the low hierarchical level (directly on audio frames) that prevents learning musical relationships, including expressive models such as recurring neural networks (RNNs). Ey feature an acoustic model that extracts features from a context of audio and often predicts a chord label for the center frame of this context.
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