Robust Fundamental Frequency Estimation in Coloured Noise

Most parametric fundamental frequency estimators make the implicit assumption that any corrupting noise is additive, white Gaus-sian. Under this assumption, the maximum likelihood (ML) and the least squares estimators are the same, and statistically efficient. However, in the coloured noise case, the estimators differ, and the spectral shape of the corrupting noise should be taken into account. To allow for this, we here propose two schemes that refine the noise statistics and parameter estimates in an iterative manner, one of them based on an approximate ML solution and the other one based on removing the periodic signal obtained from a linearly constrained minimum variance (LCMV) filter. Evaluations on real speech data indicate that the iteration steps improve the estimation accuracy, therefore offering improvement over traditional non-parametric fundamental frequency methods in most of the evaluated scenarios

Un algoritmo para la detección automática de falsetas de guitarra flamenca

La terminología acuñada en el ámbito de la guitarra flamenca es pobre y en ocasiones algo ambigua, lo que genera gran controversia al estudiarla o interactuar con otros músicos. Así, términos como falseta, melodía o variación melódica, se aplican de forma indiscriminada. Con el fin de arrojar información adicional y facilitar un estudio más explícito y objetivo, se plantea un algoritmo de extracción automática de aquellas secciones de audio en que sólo intervenga la guitarra ejecutando una falseta. Así, este proyecto tiene como objetivo fundamental la identificación automática de falsetas flamencas, entendiendo éstas como determinados fragmentos musicales interpretados por la guitarra flamenca que poseen entidad musical propia y se ejecuta entre dos interpretaciones vocales. La investigación ha sido realizada a partir de la colección de una serie de grabaciones en las que intervienen la guitarra, la voz del cantaor e incluso, en algunos casos, la percusión.The terminology associated to the field of the flamenco guitar is ocassionally poor and ambiguous, so it generates controversy by studying it or by playing and interacting with other musicians. Thereby, concepts like falseta, melody or melodic variation, are applied with no discrimination. With the final purpose of giving aditional information and making easier a more objective and explicit study, it is proposed an algorithm for an automatic extraction of all the audio sections where exclusively the guitar takes part. This Project has as main objective the automatic identification of flamenco falsetas, understanding them as the musical fragments played by the flamenco guitar, with their own musical entities, and executed between two vocal interpretations. The research has been done starting from a collection of audios containing the guitar, the voice and, ocassionally, some percussion.Universidad de Sevilla. Grado en Ingeniería de las Tecnologías de Telecomunicació

Automatic transcription of flamenco singing from polyphonic music recordings

Automatic note-level transcription is considered one of the most challenging tasks in music information retrieval. The specific case of flamenco singing transcription poses a particular challenge due to its complex melodic progressions, intonation inaccuracies, the use of a high degree of ornamentation, and the presence of guitar accompaniment. In this study, we explore the limitations of existing state of the art transcription systems for the case of flamenco singing and propose a specific solution for this genre: We first extract the predominant melody and apply a novel contour filtering process to eliminate segments of the pitch contour which originate from the guitar accompaniment. We formulate a set of onset detection functions based on volume and pitch characteristics to segment the resulting vocal pitch contour into discrete note events. A quantised pitch label is assigned to each note event by combining global pitch class probabilities with local pitch contour statistics. The proposed system outperforms state of the art singing transcription systems with respect to voicing accuracy, onset detection, and overall performance when evaluated on flamenco singing datasets.This work was supported in part by the Ph.D. Fellowship of the Department of Information and Communication Technologies, Universitat Pompeu Fabra and in part by the projects SIGMUS (TIN2012-36650) and COFLA II (P12-TIC-1362)

Automatic transcription of flamenco singing from polyphonic music recordings

Automatic note-level transcription is considered one of the most challenging tasks in music information retrieval. The specific case of flamenco singing transcription poses a particular challenge due to its complex melodic progressions, intonation inaccuracies, the use of a high degree of ornamentation, and the presence of guitar accompaniment. In this study, we explore the limitations of existing state of the art transcription systems for the case of flamenco singing and propose a specific solution for this genre: We first extract the predominant melody and apply a novel contour filtering process to eliminate segments of the pitch contour which originate from the guitar accompaniment. We formulate a set of onset detection functions based on volume and pitch characteristics to segment the resulting vocal pitch contour into discrete note events. A quantised pitch label is assigned to each note event by combining global pitch class probabilities with local pitch contour statistics. The proposed system outperforms state of the art singing transcription systems with respect to voicing accuracy, onset detection, and overall performance when evaluated on flamenco singing datasets.This work was supported in part by the Ph.D. Fellowship of the Department of Information and Communication Technologies, Universitat Pompeu Fabra and in part by the projects SIGMUS (TIN2012-36650) and COFLA II (P12-TIC-1362)

Un estudio de identificación por tarareo para cante flamenco

El flamenco como entidad musical tiene su base en la voz cantada, llamada “cante” en el argot flamenco, donde predomina una ornamentación barroca que suele ser improvisada. Esto provoca una serie de retos tecnológicos a la hora de automatizar las tareas de estudio. En el presente trabajo se aborda el diseño e implementación de un sistema de identificación de melodías mediante consultas realizadas sobre grabaciones de cante flamenco obtenidas en un trabajo de campo realizado con cantaores en Sevilla y Jerez. Se propone una estrategia que usa un algoritmo de similitud melódica basado en el método de Needleman-Wunsch. Los resultados obtenidos muestran la competencia del método que resulta ser el primero que aborda la tarea de query by humming para cante flamenco.Flamenco as a musical entity is based on the singing voice, called “cante” in the flamenco argot, where a baroque ornamentation predominates and it is usually the improvised part of the performance. This musical aesthetics causes a series of technological challenges. This paper deals with the design and implementation of a query by humming method for flamenco singing using a data base with recordings obtained in a field work carried out with singers in Seville and Jerez. A strategy using a new melodic similarity algorithm based on the Needleman-Wunsch method is proposed. We discuss the results obtained with our approach, the first one that addresses the query by humming problem for flamenco singing.Universidad de Sevilla. Grado en Ingeniería de Tecnologías Industriale

Application of singing synthesis techniquest to bertsolaritza

237 p

El flamenco como vehículo de la religiosidad popular

Se aborda en este trabajo de investigación un análisis etnomusicológico de varios escenarios en los que se usa el flamenco como herramienta de comunicación entre una comunidad y una Imagen religiosa. Concretamente, describimos y analizamos tres eventos u ocasiones en los que se ejecutan estilos flamencos distintos en varios municipios de la geografía andaluza: el cante por alboreá en la Semana Santa (Utrera), el fandango de Huelva en el Traslado de la Virgen del Rocío (Almonte) y la toná religiosa del Santo Dios (Mairena del Alcor). Como complemento metodológico al análisis musical y la teoría del performance, proponemos hacer uso de la tecnología para identificar de forma automática los descriptores musicales que elige el cantaor en estos contextos. Como estudio de caso, diseñamos un algoritmo que detecta y clasifica la ornamentación flamenca en un proceso evolutivo en el escenario de ejecución. Con ello, podemos inducir los rasgos de identidad y mecanismos de evolución del flamenco en general. De esta forma, el objeto fundamental que se persigue en este estudio multidisciplinar es analizar los efectos de la ejecución del cante flamenco como práctica articuladora de aspectos como religiosidad, cultura y música.We address in this thesis an ethnomusicological analysis of several events where the flamenco singing takes the rol of communication tool between a comunity and a religious Image. Three andalusian scenarios are described under the umbrela of the performance theory: the use of the alboreá singing in the Easter (Utrera, Seville); the religious toná Santo Dios in spring (Mairena del Alcor, Seville) and the performance of the fandango de Huelva in the roccesion of the Virgen del Rocío (Almonte, Huelva). In this multidisciplinary study we propose the use of the musical technology to automatically identify the musical descriptors chosen by the singer in these contexts. As a case study, we design an algorithm for extraction and classification of ornamentation in flamenco singing based on the performance evolution. In this way, the main goal of our research is to analyze the effects of flamenco singing as an articulating practice of aspects such as religiosity, culture and music

Modelling Professional Singers: A Bayesian Machine Learning Approach with Enhanced Real-time Pitch Contour Extraction and Onset Processing from an Extended Dataset.

Singing signals are one of the input data that computer systems need to analyse, and singing is part of all the cultures in the world. However, although there have been several studies on audio signal processing during the last three decades, it is still an active research area because most of the available algorithms in the literature require improvement due to the complexity of audio/music signals. More efforts are needed for analysing sounds/music in a real-time environment since the algorithms should work only on the past data, while in an offline system, all the required data are available. In addition, the complexity of the data will be increased if the audio signals come from singing due to the unique features of singing signals (such as vocal system, vibration, pitch drift, and tuning approach) that make the signals different and more complicated than those from an instrument. This thesis is mainly focused on analysing singing signals and better understanding how trained- professional singers sing the pitch frequency and duration of the notes according to their position in a piece of music and the singing technique applied. To do this, it is discovered that by incorporating singing features, such as gender and BPM, a real-time pitch detection algorithm can be found to estimate fundamental frequencies with fewer errors. In addition, two novel algorithms were proposed, one for smoothing pitch contours and another for estimating onset, offset, and the transition between notes. These two algorithms showed better results as compared to several other state-of-the-art algorithms. Moreover, a new vocal dataset that included several annotations for 2688 singing files was published. Finally, this thesis presents two models for calculating pitches and the duration of notes according to their positions in a piece of music. In conclusion, optimizing results for pitch-oriented Music Information Retrieval (MIR) algorithms necessitates adapting/selecting them based on the unique characteristics of the signals. Achieving a universal algorithm that performs exceptionally well on all data types remains a formidable challenge given the current state of technology