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

Statistical Approaches for Signal Processing with Application to Automatic Singer Identification

In the music world, the oldest instrument is known as the singing voice that plays an important role in musical recordings. The singer\u27s identity serves as a primary aid for people to organize, browse, and retrieve music recordings. In this thesis, we focus on the problem of singer identification based on the acoustic features of singing voice. An automatic singer identification system is constructed and has achieved a very high identification accuracy. This system consists of three crucial parts: singing voice detection, background music removal and pattern recognition. These parts are introduced and explored in great details in this thesis. To be specific, in terms of the singing voice detection, we firstly study a traditional method, double GMM. Then an improved method, namely single GMM, is proposed. The experimental result shows that the detection accuracy of single GMM can be achieved as high as 96.42%. In terms of the background music removal, Non-negative Matrix Factorization (NMF) and Robust Principal Component Analysis (RPCA) are demonstrated. The evaluation result shows that RPCA outperforms NMF. In terms of pattern recognition, we explore the algorithms of Support Vector Machine (SVM) and Gaussian Mixture Model (GMM). Based on the experimental results, it turns out that the prediction accuracy of GMM classifier is about 16% higher than SVM

Concatenative Synthesis for Novel Timbral Creation

Modern day musicians rely on a variety of instruments for musical expression. Tones produced from electronic instruments have become almost as commonplace as those produced by traditional ones as evidenced by the plethora of artists who can be found composing and performing with nothing more than a personal computer. This desire to embrace technical innovation as a means to augment performance art has created a budding field in computer science that explores the creation and manipulation of sound for artistic purposes. One facet of this new frontier concerns timbral creation, or the development of new sounds with unique characteristics that can be wielded by the musician as a virtual instrument. This thesis presents Timcat, a software system that can be used to create novel timbres from prerecorded audio. Various techniques for timbral feature extraction from short audio clips, or grains, are evaluated for use in timbral feature spaces. Clustering is performed on feature vectors in these spaces and groupings are recombined using concatenative synthesis techniques in order to form new instrument patches. The results reveal that interesting timbres can be created using features extracted by both newly developed and existing signal analysis techniques, many common in other fields though not often applied to music audio signals. Several of the features employed also show high accuracy for instrument separation in randomly mixed tracks. Survey results demonstrate positive feedback concerning the timbres created by Timcat from electronic music composers, musicians, and music lovers alike

Application of automatic speech recognition technologies to singing

The research field of Music Information Retrieval is concerned with the automatic analysis of musical characteristics. One aspect that has not received much attention so far is the automatic analysis of sung lyrics. On the other hand, the field of Automatic Speech Recognition has produced many methods for the automatic analysis of speech, but those have rarely been employed for singing. This thesis analyzes the feasibility of applying various speech recognition methods to singing, and suggests adaptations. In addition, the routes to practical applications for these systems are described. Five tasks are considered: Phoneme recognition, language identification, keyword spotting, lyrics-to-audio alignment, and retrieval of lyrics from sung queries. The main bottleneck in almost all of these tasks lies in the recognition of phonemes from sung audio. Conventional models trained on speech do not perform well when applied to singing. Training models on singing is difficult due to a lack of annotated data. This thesis offers two approaches for generating such data sets. For the first one, speech recordings are made more “song-like”. In the second approach, textual lyrics are automatically aligned to an existing singing data set. In both cases, these new data sets are then used for training new acoustic models, offering considerable improvements over models trained on speech. Building on these improved acoustic models, speech recognition algorithms for the individual tasks were adapted to singing by either improving their robustness to the differing characteristics of singing, or by exploiting the specific features of singing performances. Examples of improving robustness include the use of keyword-filler HMMs for keyword spotting, an i-vector approach for language identification, and a method for alignment and lyrics retrieval that allows highly varying durations. Features of singing are utilized in various ways: In an approach for language identification that is well-suited for long recordings; in a method for keyword spotting based on phoneme durations in singing; and in an algorithm for alignment and retrieval that exploits known phoneme confusions in singing.Das Gebiet des Music Information Retrieval befasst sich mit der automatischen Analyse von musikalischen Charakteristika. Ein Aspekt, der bisher kaum erforscht wurde, ist dabei der gesungene Text. Auf der anderen Seite werden in der automatischen Spracherkennung viele Methoden für die automatische Analyse von Sprache entwickelt, jedoch selten für Gesang. Die vorliegende Arbeit untersucht die Anwendung von Methoden aus der Spracherkennung auf Gesang und beschreibt mögliche Anpassungen. Zudem werden Wege zur praktischen Anwendung dieser Ansätze aufgezeigt. Fünf Themen werden dabei betrachtet: Phonemerkennung, Sprachenidentifikation, Schlagwortsuche, Text-zu-Gesangs-Alignment und Suche von Texten anhand von gesungenen Anfragen. Das größte Hindernis bei fast allen dieser Themen ist die Erkennung von Phonemen aus Gesangsaufnahmen. Herkömmliche, auf Sprache trainierte Modelle, bieten keine guten Ergebnisse für Gesang. Das Trainieren von Modellen auf Gesang ist schwierig, da kaum annotierte Daten verfügbar sind. Diese Arbeit zeigt zwei Ansätze auf, um solche Daten zu generieren. Für den ersten wurden Sprachaufnahmen künstlich gesangsähnlicher gemacht. Für den zweiten wurden Texte automatisch zu einem vorhandenen Gesangsdatensatz zugeordnet. Die neuen Datensätze wurden zum Trainieren neuer Modelle genutzt, welche deutliche Verbesserungen gegenüber sprachbasierten Modellen bieten. Auf diesen verbesserten akustischen Modellen aufbauend wurden Algorithmen aus der Spracherkennung für die verschiedenen Aufgaben angepasst, entweder durch das Verbessern der Robustheit gegenüber Gesangscharakteristika oder durch das Ausnutzen von hilfreichen Besonderheiten von Gesang. Beispiele für die verbesserte Robustheit sind der Einsatz von Keyword-Filler-HMMs für die Schlagwortsuche, ein i-Vector-Ansatz für die Sprachenidentifikation sowie eine Methode für das Alignment und die Textsuche, die stark schwankende Phonemdauern nicht bestraft. Die Besonderheiten von Gesang werden auf verschiedene Weisen genutzt: So z.B. in einem Ansatz für die Sprachenidentifikation, der lange Aufnahmen benötigt; in einer Methode für die Schlagwortsuche, die bekannte Phonemdauern in Gesang mit einbezieht; und in einem Algorithmus für das Alignment und die Textsuche, der bekannte Phonemkonfusionen verwertet

Deep learning for i-vector speaker and language recognition

Over the last few years, i-vectors have been the state-of-the-art technique in speaker and language recognition. Recent advances in Deep Learning (DL) technology have improved the quality of i-vectors but the DL techniques in use are computationally expensive and need speaker or/and phonetic labels for the background data, which are not easily accessible in practice. On the other hand, the lack of speaker-labeled background data makes a big performance gap, in speaker recognition, between two well-known cosine and Probabilistic Linear Discriminant Analysis (PLDA) i-vector scoring techniques. It has recently been a challenge how to fill this gap without speaker labels, which are expensive in practice. Although some unsupervised clustering techniques are proposed to estimate the speaker labels, they cannot accurately estimate the labels. This thesis tries to solve the problems above by using the DL technology in different ways, without any need of speaker or phonetic labels. In order to fill the performance gap between cosine and PLDA scoring given unlabeled background data, we have proposed an impostor selection algorithm and a universal model adaptation process in a hybrid system based on Deep Belief Networks (DBNs) and Deep Neural Networks (DNNs) to discriminatively model each target speaker. In order to have more insight into the behavior of DL techniques in both single and multi-session speaker enrollment tasks, some experiments have been carried out in both scenarios. Experiments on the National Institute of Standard and Technology (NIST) 2014 i-vector challenge show that 46% of this performance gap, in terms of minDCF, is filled by the proposed DL-based system. Furthermore, the score combination of the proposed DL-based system and PLDA with estimated labels covers 79% of this gap. In the second line of the research, we have developed an efficient alternative vector representation of speech by keeping the computational cost as low as possible and avoiding phonetic labels, which are not always accessible. The proposed vectors will be based on both Gaussian Mixture Models (GMMs) and Restricted Boltzmann Machines (RBMs) and will be referred to as GMM-RBM vectors. The role of RBM is to learn the total speaker and session variability among background GMM supervectors. This RBM, which will be referred to as Universal RBM (URBM), will then be used to transform unseen supervectors to the proposed low dimensional vectors. The use of different activation functions for training the URBM and different transformation functions for extracting the proposed vectors are investigated. At the end, a variant of Rectified Linear Unit (ReLU) which is referred to as Variable ReLU (VReLU) is proposed. Experiments on the core test condition 5 of the NIST Speaker Recognition Evaluation (SRE) 2010 show that comparable results with conventional i-vectors are achieved with a clearly lower computational load in the vector extraction process. Finally, for the Language Identification (LID) application, we have proposed a DNN architecture to model effectively the i-vector space of four languages, English, Spanish, German, and Finnish, in the car environment. Both raw i-vectors and session variability compensated i-vectors are evaluated as input vectors to DNN. The performance of the proposed DNN architecture is compared with both conventional GMM-UBM and i-vector/Linear Discriminant Analysis (LDA) systems considering the effect of duration of signals. It is shown that the signals with duration between 2 and 3 sec meet the accuracy and speed requirements of this application, in which the proposed DNN architecture outperforms GMM-UBM and i-vector/LDA systems by 37% and 28%, respectively.En los últimos años, los i-vectores han sido la técnica de referencia en el reconocimiento de hablantes y de idioma. Los últimos avances en la tecnología de Aprendizaje Profundo (Deep Learning. DL) han mejorado la calidad de los i-vectores, pero las técnicas DL en uso son computacionalmente costosas y necesitan datos etiquetados para cada hablante y/o unidad fon ética, los cuales no son fácilmente accesibles en la práctica. La falta de datos etiquetados provoca una gran diferencia de los resultados en el reconocimiento de hablante con i-vectors entre las dos técnicas de evaluación más utilizados: distancia coseno y Análisis Lineal Discriminante Probabilístico (PLDA). Por el momento, sigue siendo un reto cómo reducir esta brecha sin disponer de las etiquetas de los hablantes, que son costosas de obtener. Aunque se han propuesto algunas técnicas de agrupamiento sin supervisión para estimar las etiquetas de los hablantes, no pueden estimar las etiquetas con precisión. Esta tesis trata de resolver los problemas mencionados usando la tecnología DL de diferentes maneras, sin necesidad de etiquetas de hablante o fon éticas. Con el fin de reducir la diferencia de resultados entre distancia coseno y PLDA a partir de datos no etiquetados, hemos propuesto un algoritmo selección de impostores y la adaptación a un modelo universal en un sistema hibrido basado en Deep Belief Networks (DBN) y Deep Neural Networks (DNN) para modelar a cada hablante objetivo de forma discriminativa. Con el fin de tener más información sobre el comportamiento de las técnicas DL en las tareas de identificación de hablante en una única sesión y en varias sesiones, se han llevado a cabo algunos experimentos en ambos escenarios. Los experimentos utilizando los datos del National Institute of Standard and Technology (NIST) 2014 i-vector Challenge muestran que el 46% de esta diferencia de resultados, en términos de minDCF, se reduce con el sistema propuesto basado en DL. Además, la combinación de evaluaciones del sistema propuesto basado en DL y PLDA con etiquetas estimadas reduce el 79% de esta diferencia. En la segunda línea de la investigación, hemos desarrollado una representación vectorial alternativa eficiente de la voz manteniendo el coste computacional lo más bajo posible y evitando las etiquetas fon éticas, Los vectores propuestos se basan tanto en el Modelo de Mezcla de Gaussianas (GMM) y en las Maquinas Boltzmann Restringidas (RBM), a los que se hacer referencia como vectores GMM-RBM. El papel de la RBM es aprender la variabilidad total del hablante y de la sesión entre los supervectores del GMM gen érico. Este RBM, al que se hará referencia como RBM Universal (URBM), se utilizará para transformar supervectores ocultos en los vectores propuestos, de menor dimensión. Además, se estudia el uso de diferentes funciones de activación para el entrenamiento de la URBM y diferentes funciones de transformación para extraer los vectores propuestos. Finalmente, se propone una variante de la Unidad Lineal Rectificada (ReLU) a la que se hace referencia como Variable ReLU (VReLU). Los experimentos sobre los datos de la condición 5 del test de la NIST Speaker Recognition Evaluation (SRE) 2010 muestran que se han conseguidos resultados comparables con los i-vectores convencionales, con una carga computacional claramente inferior en el proceso de extracción de vectores. Por último, para la aplicación de Identificación de Idioma (LID), hemos propuesto una arquitectura DNN para modelar eficazmente en el entorno del coche el espacio i-vector de cuatro idiomas: inglés, español, alemán y finlandés. Tanto los i-vectores originales como los i-vectores propuestos son evaluados como vectores de entrada a DNN. El rendimiento de la arquitectura DNN propuesta se compara con los sistemas convencionales GMM-UBM y i-vector/Análisis Discriminante Lineal (LDA) considerando el efecto de la duración de las señales. Se muestra que en caso de señales con una duración entre 2 y 3 se obtienen resultados satisfactorios en cuanto a precisión y resultados, superando a los sistemas GMM-UBM y i-vector/LDA en un 37% y 28%, respectivament

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Voice Modeling Methods for Automatic Speaker Recognition

Building a voice model means to capture the characteristics of a speaker´s voice in a data structure. This data structure is then used by a computer for further processing, such as comparison with other voices. Voice modeling is a vital step in the process of automatic speaker recognition that itself is the foundation of several applied technologies: (a) biometric authentication, (b) speech recognition and (c) multimedia indexing. Several challenges arise in the context of automatic speaker recognition. First, there is the problem of data shortage, i.e., the unavailability of sufficiently long utterances for speaker recognition. It stems from the fact that the speech signal conveys different aspects of the sound in a single, one-dimensional time series: linguistic (what is said?), prosodic (how is it said?), individual (who said it?), locational (where is the speaker?) and emotional features of the speech sound itself (to name a few) are contained in the speech signal, as well as acoustic background information. To analyze a specific aspect of the sound regardless of the other aspects, analysis methods have to be applied to a specific time scale (length) of the signal in which this aspect stands out of the rest. For example, linguistic information (i.e., which phone or syllable has been uttered?) is found in very short time spans of only milliseconds of length. On the contrary, speakerspecific information emerges the better the longer the analyzed sound is. Long utterances, however, are not always available for analysis. Second, the speech signal is easily corrupted by background sound sources (noise, such as music or sound effects). Their characteristics tend to dominate a voice model, if present, such that model comparison might then be mainly due to background features instead of speaker characteristics. Current automatic speaker recognition works well under relatively constrained circumstances, such as studio recordings, or when prior knowledge on the number and identity of occurring speakers is available. Under more adverse conditions, such as in feature films or amateur material on the web, the achieved speaker recognition scores drop below a rate that is acceptable for an end user or for further processing. For example, the typical speaker turn duration of only one second and the sound effect background in cinematic movies render most current automatic analysis techniques useless. In this thesis, methods for voice modeling that are robust with respect to short utterances and background noise are presented. The aim is to facilitate movie analysis with respect to occurring speakers. Therefore, algorithmic improvements are suggested that (a) improve the modeling of very short utterances, (b) facilitate voice model building even in the case of severe background noise and (c) allow for efficient voice model comparison to support the indexing of large multimedia archives. The proposed methods improve the state of the art in terms of recognition rate and computational efficiency. Going beyond selective algorithmic improvements, subsequent chapters also investigate the question of what is lacking in principle in current voice modeling methods. By reporting on a study with human probands, it is shown that the exclusion of time coherence information from a voice model induces an artificial upper bound on the recognition accuracy of automatic analysis methods. A proof-of-concept implementation confirms the usefulness of exploiting this kind of information by halving the error rate. This result questions the general speaker modeling paradigm of the last two decades and presents a promising new way. The approach taken to arrive at the previous results is based on a novel methodology of algorithm design and development called “eidetic design". It uses a human-in-the-loop technique that analyses existing algorithms in terms of their abstract intermediate results. The aim is to detect flaws or failures in them intuitively and to suggest solutions. The intermediate results often consist of large matrices of numbers whose meaning is not clear to a human observer. Therefore, the core of the approach is to transform them to a suitable domain of perception (such as, e.g., the auditory domain of speech sounds in case of speech feature vectors) where their content, meaning and flaws are intuitively clear to the human designer. This methodology is formalized, and the corresponding workflow is explicated by several use cases. Finally, the use of the proposed methods in video analysis and retrieval are presented. This shows the applicability of the developed methods and the companying software library sclib by means of improved results using a multimodal analysis approach. The sclib´s source code is available to the public upon request to the author. A summary of the contributions together with an outlook to short- and long-term future work concludes this thesis

Content-based music structure analysis

Ph.DDOCTOR OF PHILOSOPH

Acoustic Approaches to Gender and Accent Identification

There has been considerable research on the problems of speaker and language recognition from samples of speech. A less researched problem is that of accent recognition. Although this is a similar problem to language identification, di�erent accents of a language exhibit more fine-grained di�erences between classes than languages. This presents a tougher problem for traditional classification techniques. In this thesis, we propose and evaluate a number of techniques for gender and accent classification. These techniques are novel modifications and extensions to state of the art algorithms, and they result in enhanced performance on gender and accent recognition. The first part of the thesis focuses on the problem of gender identification, and presents a technique that gives improved performance in situations where training and test conditions are mismatched. The bulk of this thesis is concerned with the application of the i-Vector technique to accent identification, which is the most successful approach to acoustic classification to have emerged in recent years. We show that it is possible to achieve high accuracy accent identification without reliance on transcriptions and without utilising phoneme recognition algorithms. The thesis describes various stages in the development of i-Vector based accent classification that improve the standard approaches usually applied for speaker or language identification, which are insu�cient. We demonstrate that very good accent identification performance is possible with acoustic methods by considering di�erent i-Vector projections, frontend parameters, i-Vector configuration parameters, and an optimised fusion of the resulting i-Vector classifiers we can obtain from the same data. We claim to have achieved the best accent identification performance on the test corpus for acoustic methods, with up to 90% identification rate. This performance is even better than previously reported acoustic-phonotactic based systems on the same corpus, and is very close to performance obtained via transcription based accent identification. Finally, we demonstrate that the utilization of our techniques for speech recognition purposes leads to considerably lower word error rates. Keywords: Accent Identification, Gender Identification, Speaker Identification, Gaussian Mixture Model, Support Vector Machine, i-Vector, Factor Analysis, Feature Extraction, British English, Prosody, Speech Recognition

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