Natural Automatic Musical Note Player using Time-Frequency Analysis on Human Play

This research aims to develop an automatic gamelan musical note player that can naturally play musical note as human does. A musician estimates time to hit an instrument button in an approximate time which is as close as to the target time. The tolerated time to play a note was identified based on the human play. A gamelan musician was selected to play five note sequences of songs, and the play was recorded to be analyzed. Execution time in hitting instrument buttons in human play was identified using time-frequency analysis and peak detection to define time range which can be tolerated as time value that not too fast or not too late in hitting buttons, and then the result of the analysis was used as parameters to randomize approximate time to play a note. The evaluation shows that the program played all note sequences in the approximate time as human does and the program played more natural and better than another program which played a note as exact as its time target

Addressing Tempo Estimation Octave Errors in Electronic Music by Incorporating Style Information Extracted From Wikipedia

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AI and Tempo Estimation: A Review

The author's goal in this paper is to explore how artificial intelligence (AI) has been utilised to inform our understanding of and ability to estimate at scale a critical aspect of musical creativity - musical tempo. The central importance of tempo to musical creativity can be seen in how it is used to express specific emotions (Eerola and Vuoskoski 2013), suggest particular musical styles (Li and Chan 2011), influence perception of expression (Webster and Weir 2005) and mediate the urge to move one's body in time to the music (Burger et al. 2014). Traditional tempo estimation methods typically detect signal periodicities that reflect the underlying rhythmic structure of the music, often using some form of autocorrelation of the amplitude envelope (Lartillot and Toiviainen 2007). Recently, AI-based methods utilising convolutional or recurrent neural networks (CNNs, RNNs) on spectral representations of the audio signal have enjoyed significant improvements in accuracy (Aarabi and Peeters 2022). Common AI-based techniques include those based on probability (e.g., Bayesian approaches, hidden Markov models (HMM)), classification and statistical learning (e.g., support vector machines (SVM)), and artificial neural networks (ANNs) (e.g., self-organising maps (SOMs), CNNs, RNNs, deep learning (DL)). The aim here is to provide an overview of some of the more common AI-based tempo estimation algorithms and to shine a light on notable benefits and potential drawbacks of each. Limitations of AI in this field in general are also considered, as is the capacity for such methods to account for idiosyncrasies inherent in tempo perception, i.e., how well AI-based approaches are able to think and act like humans.Comment: 9 page