Segregating Musical Chords for Automatic Music Transcription: A LSTM-RNN Approach

Mukherjee, Himadri; Dhar, Ankita; Obaidullah, Sk Md; Santosh, K. C.; Phadikar, Santanu

doi:10.1007/978-3-030-34872-4_47

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…It is suitable for multischeme research, can simulate the development of the community, has high prediction accuracy, and can meet the purpose of long-term planning. But, at the same time, the land-use simulation method requires a lot of spatial data and sufficient computing resources, and the computational workload is relatively large [25][26][27].…”

Section: Related Workmentioning

confidence: 99%

Multimedia Vocal Performance Automation Evaluation Model Based on RBF Network

Wang

2022

Mathematical Problems in Engineering

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Aiming at the problems of the radial basis network model, this paper proposes a multimedia vocal singing automation evaluation network model, combined with the characteristics of multimedia modeling innovation design, and proposes a two-level comprehensive model. First of all, the theory and algorithm of analytic hierarchy process and radial basis function network are researched and analyzed, and the RBF is predicted for the mature area of multimedia development based on the three indicators of the total amount of classified vocals. The prediction scheme evaluation system is then used to fit the prediction data and influencing factors using the RBF network, and then the classified vocals are adjusted and synthesized hierarchically, and a multimedia vocal classification prediction model is established. Finally, this paper uses an example to verify the feasibility performance and prediction accuracy. Based on the above theory, the experiment uses VC++ 6.0 and Matlab 6.5 combined with database technology to initially realize the evaluation system and achieves a good evaluation effect. The simulation results show that three different algorithms are used to establish RBFO content prediction models. The correlation coefficient limit, root mean square error prediction, and relative analysis error (RED) reached 0.9937, 15.5095, and 8.216, respectively. At the same time, the evaluation results have high accuracy and credibility, which not only provide designers with ideas and improvement basis for innovative designs but also ensure design quality, improve design efficiency, and show that RBF networks have good generalization capabilities.

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Section: Related Workmentioning

confidence: 99%

Multimedia Vocal Performance Automation Evaluation Model Based on RBF Network

Wang

2022

Mathematical Problems in Engineering

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“…Cheuk et al [11] presented a DL model for AMT by combining the U-Net and bidirectional long short-term memory (BiL-STM) neural network modules. Mukherjee et al [12] used statistical characteristics and an extreme learning machine for musical instrument segregation, where LSTM and the recurrent neural network (RNN) [13] were combined to differentiate the musical chords for AMT. Fan et al [14] proposed a deep neural network to extract the singing voice, followed by a dynamic unbroken pitch determination algorithm to track pitches.…”

Section: Introductionmentioning

confidence: 99%

A Music Cognition–Guided Framework for Multi-pitch Estimation

Yan

Soraghan

et al. 2022

Cogn Comput

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As one of the most important subtasks of automatic music transcription (AMT), multi-pitch estimation (MPE) has been studied extensively for predicting the fundamental frequencies in the frames of audio recordings during the past decade. However, how to use music perception and cognition for MPE has not yet been thoroughly investigated. Motivated by this, this demonstrates how to effectively detect the fundamental frequency and the harmonic structure of polyphonic music using a cognitive framework. Inspired by cognitive neuroscience, an integration of the constant Q transform and a state-of-the-art matrix factorization method called shift-invariant probabilistic latent component analysis (SI-PLCA) are proposed to resolve the polyphonic short-time magnitude log-spectra for multiple pitch estimation and source-specific feature extraction. The cognitions of rhythm, harmonic periodicity and instrument timbre are used to guide the analysis of characterizing contiguous notes and the relationship between fundamental frequency and harmonic frequencies for detecting the pitches from the outcomes of SI-PLCA. In the experiment, we compare the performance of proposed MPE system to a number of existing state-of-the-art approaches (seven weak learning methods and four deep learning methods) on three widely used datasets (i.e. MAPS, BACH10 and TRIOS) in terms of F-measure ($${F}_{1}$$ F 1 ) values. The experimental results show that the proposed MPE method provides the best overall performance against other existing methods.

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