【鼎革‧革鼎】︰ Raspbian Stretch 《六之 J.3‧MIR 》

什麼是音樂資訊檢索 MIR 呢?維基百科詞條這樣說︰

Music information retrieval

Music information retrieval (MIR) is the interdisciplinary science of retrieving information from music. MIR is a small but growing field of research with many real-world applications. Those involved in MIR may have a background in musicology, psychology, academic music study, signal processing, machine learning or some combination of these.

 

它是一種新興跨學科的領域,能有眾多應用︰

Applications

MIR is being used by businesses and academics to categorize, manipulate and even create music.

Recommender systems

Several recommender systems for music already exist, but surprisingly few are based upon MIR techniques, instead making use of similarity between users or laborious data compilation. Pandora, for example, uses experts to tag the music with particular qualities such as “female singer” or “strong bassline”. Many other systems find users whose listening history is similar and suggests unheard music to the users from their respective collections. MIR techniques for similarity in music are now beginning to form part of such systems.

Track separation and instrument recognition

Track separation is about extracting the original tracks as recorded, which could have more than one instrument played per track. Instrument recognition is about identifying the instruments involved and/or separating the music into one track per instrument. Various programs have been developed that can separate music into its component tracks without access to the master copy. In this way e.g. karaoke tracks can be created from normal music tracks, though the process is not yet perfect owing to vocals occupying some of the same frequency space as the other instruments.

Automatic music transcription

Automatic music transcription is the process of converting an audio recording into symbolic notation, such as a score or a MIDI file.[1] This process involves several subtasks, which include multi-pitch detection, onset detection, duration estimation, instrument identification, and the extraction of rhythmic information. This task becomes more difficult with greater numbers of instruments and a greater polyphony level.

Automatic categorization

Musical genre categorization is a common task for MIR and is the usual task for the yearly Music Information Retrieval Evaluation eXchange(MIREX).[2] Machine learning techniques such as Support Vector Machines tend to perform well, despite the somewhat subjective nature of the classification. Other potential classifications include identifying the artist, the place of origin or the mood of the piece. Where the output is expected to be a number rather than a class, regression analysis is required.

Music generation

The automatic generation of music is a goal held by many MIR researchers. Attempts have been made with limited success in terms of human appreciation of the results.

 

昔日高牆深院難窺門徑,今年國際 ISMIR 大會已走過蘇州︰

The 18th International Society for Music Information Retrieval Conference will take place at National University of Singapore Research Institute (NUSRI) in Suzhou, China, October 23-27, 2017. It is organized by National University of Singapore.

Music-Information Retrieval (Music-IR) is a highly interdisciplinary field, incorporating elements from the disciplines of signal processing, machine learning, psychology, musicology, electrical engineering, computer science, and many more. This conference aims to cover the entire area of Music-IR, allowing for researchers, developers, educators, and other professionals to exchange ideas, share results, and gain new perspectives from each other. This in turn will provide sufficient room to foster collaborations and encourage new developments in the field.

For its technical content, ISMIR 2017 will include presentations of research papers, both orally and as posters, and will also feature in-depth tutorials and invited talks on topics of interest to the field. All submitted papers will be subject to rigorous peer-review in order to ensure that those ultimately presented are of the highest-quality and truly speak to the state-of-the-art in the field. Papers will be graded based on novelty, scientific quality, relevance, importance, and readability/organization. Furthermore, ISMIR 2017 will include a section for late-breaking demos (LBDs) to allow for interesting yet nascent ideas to be explored and discussed, as well as an informal brainstorming or discussion ‘unconference’ which will allow for and encourage future collaborations.

Finally, ISMIR 2017 will provide a musical program as well. This program will include a wide diversity of music, from western classical standards to traditional music of the Suzhou area, and will also focus on music incorporating aspects of Music-Information Retrieval. In this way we hope to encourage the use of such techniques in the creation of new music, as well as to explore music which can lead to novel research ideas in the field.

 

新發布一系列極佳教學材料及 IPython Jupyter 筆記︰

Welcome, ISMIR 2017 participants! Contributions are welcome. See GitHub for more info.

Introduction

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About This Site

musicinformationretrieval.com is a collection of instructional materials for music information retrieval (MIR). These materials contain a mix of casual conversation, technical discussion, and Python code.

These pages, including the one you’re reading, are authored using Jupyter notebooks. They are statically hosted using GitHub Pages. The GitHub repository is found here: stevetjoa/stanford-mir.

This material is used during the annual Summer Workshop on Music Information Retrieval at CCRMA, Stanford University. Click here for workshop description and registration.

This site is maintained by Steve Tjoa. For questions, please email steve@stevetjoa.com. Do you have any feedback? Did you find errors or typos? Are you a teacher or researcher and would like to collaborate? Please let me know.

People who use this site

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Instructional material for the Music Information Retrieval Workshop at CCRMA, Stanford University, 2014-17. http://musicinformationretrieval.com

stanford-mir

Instructional material for the Music Information Retrieval Workshop at CCRMA, Stanford University, 2014-17.

How to Use This Repo

This repository contains Jupyter notebooks related to music information retrieval (MIR). Inside these notebooks are Python code snippets that illustrate basic MIR systems.

The simplest way to use this repository is to (1) browse a read-only version of this repo at musicinformationretrieval.com, and (2) follow along using a blank Jupyter notebook of your own.

 

特專題介紹,希望有興趣者有個起步也。