From heuristics-based to data-driven audio melody extraction

The identification of the melody from a music recording is a relatively easy task for humans, but very challenging for computational systems. This task is known as "audio melody extraction", more formally defined as the automatic estimation of the pitch sequence of the melody directly from the audio signal of a polyphonic music recording. This thesis investigates the benefits of exploiting knowledge automatically derived from data for audio melody extraction, by combining digital signal processing and machine learning methods. We extend the scope of melody extraction research by working with a varied dataset and multiple definitions of melody. We first present an overview of the state of the art, and perform an evaluation focused on a novel symphonic music dataset. We then propose melody extraction methods based on a source-filter model and pitch contour characterisation and evaluate them on a wide range of music genres. Finally, we explore novel timbre, tonal and spatial features for contour characterisation, and propose a method for estimating multiple melodic lines. The combination of supervised and unsupervised approaches leads to advancements on melody extraction and shows a promising path for future research and applications

On the Use of Perceptual Properties for Melody Estimation

cote interne IRCAM: Liao11aInternational audienceThis paper is about the use of perceptual principles for melody estimation. The melody stream is understood as generated by the most dominant source. Since the source with the strongest energy may not be perceptually the most dominant one, it is proposed to study the perceptual properties for melody estimation: loudness, masking effect and timbre similarity. The related criteria are integrated into a melody estimation system and their respective contributions are evaluated. The effectiveness of these perceptual criteria is confirmed by the evaluation results using more than one hundred excerpts of music recordings

Evaluation and combination of pitch estimation methods for melody extraction in symphonic classical music

The extraction of pitch information is arguably one of the most important tasks in automatic music description systems. However, previous research and evaluation datasets dealing with pitch estimation focused on relatively limited kinds of musical data. This work aims to broaden this scope by addressing symphonic western classical music recordings, focusing on pitch estimation for melody extraction. This material is characterised by a high number of overlapping sources, and by the fact that the melody may be played by different instrumental sections, often alternating within an excerpt. We evaluate the performance of eleven state-of-the-art pitch salience functions, multipitch estimation and melody extraction algorithms when determining the sequence of pitches corresponding to the main melody in a varied set of pieces. An important contribution of the present study is the proposed evaluation framework, including the annotation methodology, generated dataset and evaluation metrics. The results show that the assumptions made by certain methods hold better than others when dealing with this type of music signals, leading to a better performance. Additionally, we propose a simple method for combining the output of several algorithms, with promising results

Self-Supervised Representation Learning for Vocal Music Context

In music and speech, meaning is derived at multiple levels of context. Affect, for example, can be inferred both by a short sound token and by sonic patterns over a longer temporal window such as an entire recording. In this paper we focus on inferring meaning from this dichotomy of contexts. We show how contextual representations of short sung vocal lines can be implicitly learned from fundamental frequency ($F_0$) and thus be used as a meaningful feature space for downstream Music Information Retrieval (MIR) tasks. We propose three self-supervised deep learning paradigms which leverage pseudotask learning of these two levels of context to produce latent representation spaces. We evaluate the usefulness of these representations by embedding unseen vocal contours into each space and conducting downstream classification tasks. Our results show that contextual representation can enhance downstream classification by as much as 15 % as compared to using traditional statistical contour features.Comment: Working on more updated versio

Automatic music transcription: challenges and future directions

Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects

Singing information processing: techniques and applications

Por otro lado, se presenta un método para el cambio realista de intensidad de voz cantada. Esta transformación se basa en un modelo paramétrico de la envolvente espectral, y mejora sustancialmente la percepción de realismo al compararlo con software comerciales como Melodyne o Vocaloid. El inconveniente del enfoque propuesto es que requiere intervención manual, pero los resultados conseguidos arrojan importantes conclusiones hacia la modificación automática de intensidad con resultados realistas. Por último, se propone un método para la corrección de disonancias en acordes aislados. Se basa en un análisis de múltiples F0, y un desplazamiento de la frecuencia de su componente sinusoidal. La evaluación la ha realizado un grupo de músicos entrenados, y muestra un claro incremento de la consonancia percibida después de la transformación propuesta.La voz cantada es una componente esencial de la música en todas las culturas del mundo, ya que se trata de una forma increíblemente natural de expresión musical. En consecuencia, el procesado automático de voz cantada tiene un gran impacto desde la perspectiva de la industria, la cultura y la ciencia. En este contexto, esta Tesis contribuye con un conjunto variado de técnicas y aplicaciones relacionadas con el procesado de voz cantada, así como con un repaso del estado del arte asociado en cada caso. En primer lugar, se han comparado varios de los mejores estimadores de tono conocidos para el caso de uso de recuperación por tarareo. Los resultados demuestran que \cite{Boersma1993} (con un ajuste no obvio de parámetros) y \cite{Mauch2014}, tienen un muy buen comportamiento en dicho caso de uso dada la suavidad de los contornos de tono extraídos. Además, se propone un novedoso sistema de transcripción de voz cantada basada en un proceso de histéresis definido en tiempo y frecuencia, así como una herramienta para evaluación de voz cantada en Matlab. El interés del método propuesto es que consigue tasas de error cercanas al estado del arte con un método muy sencillo. La herramienta de evaluación propuesta, por otro lado, es un recurso útil para definir mejor el problema, y para evaluar mejor las soluciones propuestas por futuros investigadores. En esta Tesis también se presenta un método para evaluación automática de la interpretación vocal. Usa alineamiento temporal dinámico para alinear la interpretación del usuario con una referencia, proporcionando de esta forma una puntuación de precisión de afinación y de ritmo. La evaluación del sistema muestra una alta correlación entre las puntuaciones dadas por el sistema, y las puntuaciones anotadas por un grupo de músicos expertos

調波音打楽器音分離による歌声のスペクトルゆらぎに基づく音楽信号処理の研究

学位の種別:課程博士University of Tokyo(東京大学

Automatic transcription of the melody from polyphonic music

This dissertation addresses the problem of melody detection in polyphonic musical audio. The proposed algorithm uses a bottom-up design, in which each module leads to a more abstract representation of the audio data, which allows a very efficient computation of the melody. Nonetheless, the dataflow is not strictly unidirectional: on several occasions, feedback from higher processing modules controls the processing of low-level modules. The spectral analysis is based on a technique for the efficient computation of short-time Fourier spectra in different time-frequency resolutions. The pitch determination algorithm (PDA) is based on the pair-wise analysis of spectral peaks. Although melody detection implies a strong focus on the predominant voice, the proposed tone processing module aims at extracting multiple fundamental frequencies (F0). In order to identify the melody, the best succession of tones has to be chosen. This thesis describes an efficient computational method for auditory stream segregation that processes a variable number of simultaneous voices. The presented melody extraction algorithm has been evaluated during the MIREX audio melody extraction task. The MIREX results show that the proposed algorithm belongs to the state-of-the-art-algorithms, reaching the best overall accuracy in MIREX 2014.Diese Dissertation befasst sich mit dem Problem der Melodiextraktion aus polyphonem musikalischen Audio. Der vorgestellte Algorithmus umfasst ein „bottom-up“-Design, in dem jedes dieser Module eine abstraktere Darstellung der Audiodaten liefert, was eine effiziente Extraktion der Melodie erlaubt. Allerdings ist der Datenstrom nicht unidirektional -- bei verschiedenen Gelegenheiten steuert Feedback von höheren Verarbeitungsmodulen die Verarbeitung von vorangestellten Modulen. Die Spektralanalyse basiert auf einer Technik zur effizienten Berechnung von Kurzzeit-Fourier-Spektren in verschiedenen Zeit-Frequenz-Auflösungen. Der Pitchbestimmungsalgorithmus basiert auf der paarweisen Analyse von spektralen Maxima. Obwohl die Melodieextraktion einen starken Fokus auf die vorherrschende Stimme voraussetzt, zielt das Tonverabeitungsmodul auf eine Extraktion von allen auftretenden Grundfrequenzen (F0) ab. Um die Melodiestimme zu identifizieren, muss die beste Abfolge von Tönen ausgewählt werden. Diese Dissertation beschreibt eine effiziente Methode für die automatische Segregation von sogenannten auditiven Klangströmen. Dabei wird eine variable Anzahl von gleichzeitigen Stimmen verarbeitet. Der vorgestellte Melodieextraktionsalgorithmus wurde im MIREX „audio melody extraction task“ evaluiert. Die Resultate zeigen, dass der Algorithmus zum Stand der Technik gehört – es wurde die beste Gesamtgenauigkeit der im Jahr 2014 ausgewerteten Algorithmen erreicht

A computational framework for sound segregation in music signals

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200