ISOLATED INSTRUMENT TRANSCRIPTION USING A DEEP BELIEF NETWORK

ABSTRACT Automatic music transcription is a difficult task that has provoked extensive research on transcription systems that are predominantly general purpose, processing any number or type of instruments sounding simultaneously. This paper presents a polyphonic transcription system that is constrained to processing the output of a single instrument with an upper bound on polyphony. For example, a guitar has six strings and is limited to producing six notes simultaneously. The transcription system consists of a novel pitch estimation algorithm that uses a deep belief network and multi-label learning techniques to generate multiple pitch estimates for each audio analysis frame, such that the polyphony does not exceed that of the instrument. The implemented transcription system is evaluated on a compiled dataset of synthesized guitar recordings. Comparing these results to a prior single-instrument polyphonic transcription system that received exceptional results, this paper demonstrates the effectiveness of deep, multi-label learning for the task of polyphonic transcription

Avaliação de um método de correlação para aplicação em um detector e classificador de acordes

Desenvolvimento de um sistema de identificação e classificação de frequências sonoras simultâneas (acordes) com aplicação voltada para o ramo musical baseado no coeficiente de correlação e em uma função linear de threshold. Utilizando o software MATLAB 2016a, são elaboradas funções que calculam a correlação entre um acorde e uma base de dados de notas individuais previamente gerada, contendo 36 notas gravadas de uma guitarra elétrica, utilizando uma interface de áudio para gravação em 96 kHz de amostragem, que é reduzida posteriormente para 48 kHz para fins de processamento. Todas as operações sobre os sinais são feitas no domínio frequência, utilizando a FFT (Fast Fourier Transform) para o tratamento dos sinais de áudio. São desenvolvidos dois tipos de seletores de notas a fim de fornecer a saída na forma de notas detectadas. No primeiro seletor, todas as notas consideradas detectadas são informadas na saída. No segundo seletor, dentre todos os valores de correlação, o sistema escolhe os 6 maiores valores para apontar como prováveis notas executadas no sinal de entrada. São realizados testes com 10 amostras de 20 acordes diferentes para caracterizar os classificadores quanto à sua taxa de acerto, falsos positivos e negativos. Também é feita análise da performance dos classificadores utilizando curvas ROC. De posse dos resultados finais, concluiu-se que o coeficiente de correlação pode ser considerado uma boa ferramenta para a comparação dos sinais na aplicação proposta. O primeiro seletor demonstrou melhor taxa de acerto do que o segundo seletor, por ser menos restritivo e informar todas as notas detectadas. As taxas de acerto máximas para o primeiro seletor variam de 0 a 100 % dependendo do acorde, enquanto as taxas do segundo variam de 0 a 66,6667% de acerto máximo. Foi mostrado que para acordes de tons diferentes e construídos de diferentes intervalos, a taxa de acerto dos testes variou de uma forma sem padrão específico. Todavia, a função utilizada como limiar de detecção para o classificador apresentou performance limitada para os dois seletores desenvolvidos, onde, na maioria dos casos, para altos valores de taxa de acerto, esta retorna alta taxa de falsos positivos e, para baixos valores de falsos positivos, esta obtém baixa taxa de verdadeiros positivos.Development of a system of detection and classification of simultaneous sound frequencies (chords) with application oriented to the musical field based on the correlation coefficient and a linear function for threshold. Using MATLAB 2016a, functions are developed to calculate the correlation between a chord and a previously generated individual notes database containing 36 recorded notes of an electric guitar using an audio interface, sampling at 96 kHz, which is subsequently reduced to 48 kHz for processing purposes. All operations on the signals are done in the frequency domain, using the FFT (Fast Fourier Transform) for the treatment of audio signals. Two types of note selectors are developed to provide output in the form of detected notes. In the first selector, all notes considered detected are reported on the output. In the second selector, of all correlation values, the system chooses the 6 largest values to point as probable notes executed on the input signal Tests are performed with 10 samples of 20 different chords to characterize the classifiers as to their hit rate, false positives and negatives. Also, the performance of the classifiers was analysed using ROC curve. With the final results, it was concluded that the correlation coefficient can be considered as a good tool for the comparison of the signals in the proposed application. The first selector showed a better hit rate than the second selector, because it was less restrictive, reporting all of the detected notes. The hit rates for the first selector range from 0 to 100 % depending on the chord, while the second hit rates vary from 0 to 66,6667 %. It was shown that for different tones and for different construction of chords, the hit rates vary without a specific pattern. However, the function used as the detection threshold for the classifier showed limited performance for the two developed selectors, where, in most cases, with high values of hit rate, it returns a high rate of false positives and with low values of false positives, it returns a low rate of true positives

Proceedings of the 19th Sound and Music Computing Conference

Proceedings of the 19th Sound and Music Computing Conference - June 5-12, 2022 - Saint-Étienne (France). https://smc22.grame.f

Automatic guitar tablature transcription online

Manually transcribing guitar tablature from an audio recording is a difficult and time-consuming process, even for experienced guitarists. While several algorithms have been developed to automatically extract the notes occurring in an audio recording, and several algorithms have been developed to produce guitar tablature arrangements of notes occurring in a music score, no frameworks have been developed to facilitate the combination of these algorithms. This work presents a web-based guitar tablature transcription framework capable of generating guitar tablature arrangements directly from an audio recording. The implemented transcription framework, entitled Robotaba, facilitates the creation of web applications in which polyphonic transcription and guitar tablature arrangement algorithms can be embedded. Such a web application is implemented, resulting in a unified system that is capable of transcribing guitar tablature from a digital audio recording and displaying the resulting tablature in the web browser. The performance of the implemented polyphonic transcription and guitar tablature arrangement algorithms are evaluated using several metrics on a new dataset of manual transcriptions gathered from tablature websites.Transcrire à la main une tablature pour guitare à partir d'un enregistrement audio est un processus difficile et long, même pour les guitaristes chevronnés. Bien que plusieurs algorithmes aient été créés pour extraire automatiquement les notes d'un enregistrement audio, et d'autres pour préparer des arrangements de notes de tablature pour guitare tels qu'on les retrouve dans la création musicale, aucun environnement n'a été mise en place pour faciliter l'association de ces algorithmes. Le travail qui suit présente un environnement accessible sur l'Internet, permettant la transcription et la préparation d'arrangements de tablatures de guitare, directement à partir d'un enregistrement audio. Cet environnement de transcription, nommée Robotaba, facilite la création d'applications Web, dans lesquelles la transcription polyphonique et les algorithmes d'arrangements de tablature pour guitare peuvent être intégrés. Une telle application Web permet d'obtenir un système unifié, capable de transcrire une tablature pour guitare à partir d'un enregistrement audio numérique, et d'afficher la tablature obtenue dans un navigateur Web. La performance de la transcription polyphonique mise en place et des algorithmes d'arrangements de tablature pour guitare est évaluée à l'aide de plusieurs paramètres et d'un nouvel ensemble de données, constitué de transcriptions manuelles recueillies dans des sites Web consacrés aux tablatures