Automatic music transcription for traditional woodwind instruments sopele

Skoki, Arian; Ljubić, Sandi; Lerga, Jonatan; Štajduhar, Ivan

doi:10.1016/j.patrec.2019.09.024

Cited by 11 publications

(5 citation statements)

References 12 publications

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“…Even if someone proposes automatic musical instrument timbre recognition, the feature extraction of musical instrument timbre can achieve better accuracy in the identification of pure music environment. However, the extraction accuracy of simple musical instrument timbre features in vocal singing is not satisfactory [ 14 ]. Vocal singing is divided into various types of vocal singing classification, as shown in Figure 3 :…”

Section: Methods Of Timbre Feature Extraction Methods In Vocal Singingmentioning

confidence: 99%

Investigation on the Extraction Methods of Timbre Features in Vocal Singing Based on Machine Learning

Zang

2022

Computational Intelligence and Neuroscience

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With the continuous development of digital technology, music, as an important form of media, and its digital audio technology is also constantly developing, forcing the traditional music industry to start the road of digital transformation. What kind of method can be used to automatically retrieve music information effectively and quickly in vocal singing has become one of the current research topics that has attracted much attention. Aiming at this problem, it is of great research significance for the field of timbre feature recognition. With the in-depth research on timbre feature recognition, the research on timbre feature extraction by machine learning in vocal singing has also been gradually carried out, and its performance advantages are of great significance to solve the problem of automatic retrieval of music information. This paper aims to study the application of feature extraction algorithm based on machine learning in timbre feature extraction in vocal singing. Through the analysis and research of machine learning and feature extraction methods, it can be applied to the construction of timbre feature extraction algorithms to solve the problem of automatic retrieval of music information. This paper analyzed vocal singing, machine learning, and feature extraction, experimentally analyzed the performance of the method, and used related theoretical formulas to explain. The results have showed that the method for timbre feature extraction in the vocal singing environment was more accurate than the traditional method, the difference between the two was 24.27%, and the proportion of satisfied users was increased by 33%. It can be seen that this method can meet the needs of users for timbre feature extraction in the use of music software, and the work efficiency and user satisfaction are greatly improved.

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Section: Methods Of Timbre Feature Extraction Methods In Vocal Singingmentioning

confidence: 99%

Investigation on the Extraction Methods of Timbre Features in Vocal Singing Based on Machine Learning

Zang

2022

Computational Intelligence and Neuroscience

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“…The data presented here was successfully utilised for developing, training and testing an automatic music transcription (AMT) solution, capable of converting sopele audio recordings into musical scores [1].…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Sopele music dataset

et al. 2020

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Data presented in this article was created using a Croatian instrument called sopela – a traditional hand-made wooden aerophone of piercing sound, characteristic to the Istrian peninsula in western Croatia. The instrument is always played in pair (plural form: sopele), which consists of two voices: a small sopela and a great sopela. The data contains Waveform Audio File format (WAV) files, capturing every possible distinct tone of both sopele, as well as their polyphonic combinations. Additional data encompassed in the provided dataset are music scales and real music pieces, which contain specific traditional melodies. Every melody has a corresponding music sheet, presented in a Portable Document Format (PDF) file, which describes it in a human-readable manner. The specific Istrian scale music notation was applied while creating the music sheets. The data presented here was successfully utilised for developing, training and testing an automatic music transcription (AMT) solution, capable of converting sopele audio recordings into musical scores [1].

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“…[27] tarafından yapılmıştır. Literatürde gitar [28], basgitar [29], elektrogitar [30], piyano [31][32][33], davul [34]- [36], davul zili [37], zil çanları [38], sopele [39], Flamenko müziği [40] ve Hint klasik müziği [41] gibi farklı müzik enstrümanları ve türleri için yapılmış çeşitli otomatik müzik transkripsiyon çalışmaları bulunmaktadır.…”

Section: Literatür İncelemesiunclassified

Ses kayıtlarından otomatik keman müzik transkripsiyonu

AVCI¹,

Acuner

2023

DÜMF MD

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Bu çalışmada, başlangıç seviyesi keman eğitiminde kullanmak amacıyla keman öğrenenlere çalma performanslarıyla ilgili bir geri dönüt sağlayacak karmaşık spektral fark yöntemi tabanlı bir otomatik müzik transkripsiyon sistemi önerilmiştir. Ayrıca, önerilen müzik transkripsiyon sistemine dayalı ve kemandaki temel etütlerden olan dört boş tel, sol majör arpej ve sol majör dizi notalarını tespit eden Matlab yazılım tabanlı bir kullanıcı arayüzü gerçekleştirilmiştir. Önerilen sistemin performans analizi için iPad tablet tabanlı profesyonel kayıt sistemi kullanarak sekiz katılımcıdan elde edilmiş bir ses kayıt veri seti oluşturulmuştur. Önerilen sistemin keman ses kayıtlarının analizini doğru yapabilmesi için müzik parçasının kendisini oluşturan notalara uygun bölütlenmesi, bunun için de notaların başlangıç zamanının doğru bir şekilde tespit edilmesi gerekmektedir. Piyano ve gitar gibi diğer müzik çalgı seslerine kıyasla, keman sesinin nota başlangıç zamanı tespiti, sahip olduğu zarf karakteristiği nedeniyle daha zordur. Önerilen çalışmada nota başlangıç zamanı tespiti için karmaşık spektral fark yöntemi kullanılmaktadır. Daha sonra, çıkarılan bölüte hızlı Fourier dönüşümü uygulanarak keman sesinin notası ve oktavı belirlenecek şekilde bölütün temel frekansı bulunmaktadır. Ayrıca, geliştirilen arayüz üzerinde süre ve gürlük analizleri de yapılabilmektedir. Kıyaslamalı sonuçlar, önerilen sistemin önemli müzik analiz yazılımları olan MIRtoolbox ve Essentia’daki yöntemlere göre daha başarılı performans sergilediğini göstermektedir.

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Automatic music transcription for traditional woodwind instruments sopele

Cited by 11 publications

References 12 publications

Investigation on the Extraction Methods of Timbre Features in Vocal Singing Based on Machine Learning

Investigation on the Extraction Methods of Timbre Features in Vocal Singing Based on Machine Learning

Sopele music dataset

Ses kayıtlarından otomatik keman müzik transkripsiyonu

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