Machine Learning Techniques in Automatic Music Transcription: A Systematic Survey

ABSTRACT In the domain of Music Information Retrieval (MIR), Automatic Music Transcription (AMT) emerges as a central challenge, aiming to convert audio signals into symbolic Figure 1. This review critically evaluates both Loudness estimation and quantization, Instrument recognition, fully automatic and semi-automatic AMT systems, emphasizing Extraction of rhythmic information, Time quantization, the importance of minimal user intervention and examining Extraction of velocity and dynamic various methodologies proposed to date. By addressing Figure 1 (represented in [7]), illustrates the data representations the limitations of prior techniques and suggesting in an AMT system. AMT system takes an audio avenues for improvement, our objective is to steer future waveform as input, computes a time-frequency representation research towards fully automated AMT systems capable of the audio, outputs a representation of pitches of accurately and efficiently translating intricate audio signals over time in a spectrogram, and generates a typeset music into precise symbolic representations. Previous studies have tackled Automatic Music only synthesizes the latest advancements but also lays out a Transcription (AMT) using two main approaches: Nonnegative road-map for overcoming existing challenges in AMT, providing Matrix Factorization (NMF) [8], and Neural Networks valuable insights for researchers aiming to narrow (NNs) [9] [2].