[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126355-en":3,"doc-seo-126355-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":20,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126355,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Machine Learning Techniques in Automatic Music Transcription - A Systematic Survey","Automatic Music Transcription (AMT) converts audio signals into symbolic musical representations such as pitches, durations, note onsets/offsets, and sheet-music scores. This systematic survey reviews machine-learning techniques applied to AMT, emphasizing how AMT’s difficulty stems from the intricate, overlapping spectral structure of musical harmonies. It analyzes progress and constraints of current models, comparing fully automatic and semi-automatic approaches while focusing on reducing user intervention. The review evaluates limitations and outlines research directions toward more accurate, efficient fully automated transcription that narrows the gap with human-level accuracy.","MACHINE LEARNING TECHNIQUES IN AUTOMATIC MUSIC TRANSCRIPTION: A SYSTEMATIC SURVEY  \nFatemeh Jamshidi 1 Gary Pike 1 Amit Das 1 Richard Chapman 1  \n1 Department of Computer Science, Auburn University, USA  \n{ fzj0007, pikegl, azd0123, [chapmro } @auburn.edu](chapmro } @auburn.edu)  \narXiv :2406 . 15249v1 [ cs . SD] 20 Jun 2024  \nABSTRACT  \nIn the domain of Music Information Retrieval (MIR), Automatic Music Transcription (AMT) emerges as a central challenge, aiming to convert audio signals into symbolic notations like musical notes or sheet music. This systematic review accentuates the pivotal role of AMT in music signal analysis, emphasizing its importance due to the intricate and overlapping spectral structure of musical harmonies. Through a thorough examination of existing machine learning techniques utilized in AMT, we explore the progress and constraints of current models and methodologies. Despite notable advancements, AMT systems have yet to match the accuracy of human experts, largely due to the complexities of musical harmonies and the need for nuanced interpretation. This review critically evaluates both fully automatic and semi-automatic AMT systems, emphasizing the importance of minimal user intervention and examining various methodologies proposed to date. By addressing the limitations of prior techniques and suggesting avenues for improvement, our objective is to steer future research towards fully automated AMT systems capable of accurately and efficiently translating intricate audio signals into precise symbolic representations. This study not only synthesizes the latest advancements but also lays out a road-map for overcoming existing challenges in AMT, providing valuable insights for researchers aiming to narrow the gap between current systems and human-level transcription accuracy.  \n1. INTRODUCTION  \nAutomatic Music Transcription (AMT) is the process of converting an acoustic signal into its equivalent notation, pitch, duration, onset and offset time, musical score or sheet, or any other musical representation [1] [2] [3] [4]  \n[5] . The applications of AMT include music education (e.g., through systems for automatic instrument tutoring), music creation (e.g., dictating improvised musical ideas  \n © Fatemeh Jamshidi, Gary Pike, Amit Das, and Richard Chapman. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0) . Attribution: Fatemeh Jamshidi, Gary Pike, Amit Das, and Richard Chapman,“Machine Learning Techniques in Automatic Music Transcription: A Systematic Survey”, in Proc. of the 25th Int. Society for Music Information Retrieval Conf., San Francisco, United States, 2024 .  \nFigure 1 . Automatic music transcription system [7] .  \nand automatic music accompaniment), music production (e.g., music content visualization and intelligent contentbased editing), music search (e.g., indexing and recommendation of music by melody, bass, rhythm, or chord progression), and musicology (e.g., analyzing jazz improvisations and other annotated music) [4] .  \nThe AMT problem can be divided into several sub-tasks [2] [6]: Multi-pitch detection, Note onset/offset detection, Loudness estimation and quantization, Instrument recognition, Extraction of rhythmic information, Time quantization, Extraction of velocity and dynamic  \nFigure 1 (represented in [7]), illustrates the data representations in an AMT system. AMT system takes an audio waveform as input, computes a time-frequency representation of the audio, outputs a representation of pitches over time in a spectrogram, and generates a typeset music score [3] . Previous studies have tackled Automatic Music Transcription (AMT) using two main approaches: Nonnegative Matrix Factorization (NMF) [8], and Neural Networks (NNs) [9][2] . NN techniques typically involve processing spectrograms with various neural network architectures, such as long short-term memory layers or Convolutional Neural Networks (CNNs) . Many AMT studies rely on NNs, particularly","cbCaircOigsFpRAc","https://ap.wps.com/l/cbCaircOigsFpRAc","pdf",357643,9,1,"English","en",105,"# Introduction\n## AMT pipeline and sub-tasks\n## Main approaches: NMF and neural networks\n## Multitask and source separation methods\n# Frame-level Transcription\n## Multi-Pitch Estimation (MPE)\n## Methods: signal processing, probabilistic/Bayesian, NMF, neural networks","[{\"question\":\"What problem does automatic music transcription (AMT) address?\",\"answer\":\"AMT converts an audio signal into symbolic musical representations such as pitch, duration, and timing (onset/offset), producing outputs like musical scores.\"},{\"question\":\"How is the AMT task commonly decomposed?\",\"answer\":\"The survey describes AMT as a set of sub-tasks including multi-pitch detection, onset/offset detection, loudness estimation and quantization, instrument recognition, rhythmic extraction, time quantization, and dynamic/velocity estimation.\"},{\"question\":\"Why is AMT still less accurate than human transcription?\",\"answer\":\"AMT accuracy is constrained by the complex spectral structure of musical harmonies, which require nuanced interpretation beyond many current automated systems.\"}]","Machine Learning Techniques in Automatic Music Transcription - A Systematic Survey | PDF",1785904638,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-techniques-in-automatic-music-transcription-a-systematic-survey","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-techniques-in-automatic-music-transcription-a-systematic-survey/126355/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does automatic music transcription (AMT) address?","Question",{"text":76,"@type":77},"AMT converts an audio signal into symbolic musical representations such as pitch, duration, and timing (onset/offset), producing outputs like musical scores.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the AMT task commonly decomposed?",{"text":81,"@type":77},"The survey describes AMT as a set of sub-tasks including multi-pitch detection, onset/offset detection, loudness estimation and quantization, instrument recognition, rhythmic extraction, time quantization, and dynamic/velocity estimation.",{"name":83,"@type":74,"acceptedAnswer":84},"Why is AMT still less accurate than human transcription?",{"text":85,"@type":77},"AMT accuracy is constrained by the complex spectral structure of musical harmonies, which require nuanced interpretation beyond many current automated systems.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]