INTUNE WITH PATTERNS: JOURNEYING THROUGH MUSIC METER RECOGNITION USING LSTM NETWORKS

Music meter, a piece's rhythmic backbone, determines how we interpret its phrases. Automatic MMR is essential for many music analysis jobs. Existing solutions frequently employ restrictive rule-based approaches or need considerable feature engineering. This research introduces Meter Maestros, a revolutionary MMR system based on LSTMs, a strong form of machine learning network. Meter Maestros specializes in capturing long-term rhythmic patterns in music. We train LSTM networks on musical sequences to distinguish meter types such as4/4and3/4. Meter Maestros performs comparably to existing MMR algorithms when tested on typical music datasets. We address the approach's benefits, limits, and future prospects, such as combining music theory knowledge and transferring learning across genres. Meter Maestros sets the door for substantial advances in MMR, promoting a greater knowledge of music rhythm using machine learning.