Compositemap: A Novel Framework for Music Similarity Measure

10.1145/1571941.1572011Proceedings - 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009403-41

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

Scalable cover song identification based on melody indexing

In this work, we describe an efficient method for cover song identification focusing our target on pop and rock music genres. The procedure proposed is based on the fact that every pop/rock song has usually a main melody or a easily recognizing theme inside. Usually this theme or this melody, even if the cover song is really different from the original, is present in every version of the original song. This means that if we can identify the melody in each song we can identify also the original songope

Music information retrieval: conceptuel framework, annotation and user behaviour

Understanding music is a process both based on and influenced by the knowledge and experience of the listener. Although content-based music retrieval has been given increasing attention in recent years, much of the research still focuses on bottom-up retrieval techniques. In order to make a music information retrieval system appealing and useful to the user, more effort should be spent on constructing systems that both operate directly on the encoding of the physical energy of music and are flexible with respect to users’ experiences. This thesis is based on a user-centred approach, taking into account the mutual relationship between music as an acoustic phenomenon and as an expressive phenomenon. The issues it addresses are: the lack of a conceptual framework, the shortage of annotated musical audio databases, the lack of understanding of the behaviour of system users and shortage of user-dependent knowledge with respect to high-level features of music. In the theoretical part of this thesis, a conceptual framework for content-based music information retrieval is defined. The proposed conceptual framework - the first of its kind - is conceived as a coordinating structure between the automatic description of low-level music content, and the description of high-level content by the system users. A general framework for the manual annotation of musical audio is outlined as well. A new methodology for the manual annotation of musical audio is introduced and tested in case studies. The results from these studies show that manually annotated music files can be of great help in the development of accurate analysis tools for music information retrieval. Empirical investigation is the foundation on which the aforementioned theoretical framework is built. Two elaborate studies involving different experimental issues are presented. In the first study, elements of signification related to spontaneous user behaviour are clarified. In the second study, a global profile of music information retrieval system users is given and their description of high-level content is discussed. This study has uncovered relationships between the users’ demographical background and their perception of expressive and structural features of music. Such a multi-level approach is exceptional as it included a large sample of the population of real users of interactive music systems. Tests have shown that the findings of this study are representative of the targeted population. Finally, the multi-purpose material provided by the theoretical background and the results from empirical investigations are put into practice in three music information retrieval applications: a prototype of a user interface based on a taxonomy, an annotated database of experimental findings and a prototype semantic user recommender system. Results are presented and discussed for all methods used. They show that, if reliably generated, the use of knowledge on users can significantly improve the quality of music content analysis. This thesis demonstrates that an informed knowledge of human approaches to music information retrieval provides valuable insights, which may be of particular assistance in the development of user-friendly, content-based access to digital music collections

An approach to melodic segmentation and classification based on filtering with the Haar wavelet

We present a novel method of classification and segmentation of melodies in symbolic representation. The method is based on filtering pitch as a signal over time with the Haar-wavelet, and we evaluate it on two tasks. The filtered signal corresponds to a single-scale signal ws from the continuous Haar wavelet transform. The melodies are first segmented using local maxima or zero-crossings of ws. The segments of ws are then classified using the k–nearest neighbour algorithm with Euclidian and city-block distances. The method proves more effective than using unfiltered pitch signals and Gestalt-based segmentation when used to recognize the parent works of segments from Bach’s Two-Part Inventions (BWV 772–786). When used to classify 360 Dutch folk tunes into 26 tune families, the performance of the method is comparable to the use of pitch signals, but not as good as that of string-matching methods based on multiple features

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

Meet the man who turns vission into mission.

More and more universities are committed to taking the bilateral relationship with the industries to higher and more comprehensive level. Universiti Putra Malaysia (UPM) too has taken initiative to join the league to boost its professionalism, networking and educational opportunities. Over the years. UPM has undergone phases of extensive development starting from the transformation of Serdang School of Agriculture in 1931 to Universiti Pertanian Malaysia in 1969 as well as to Universiti Putra Malaysia (1997). Along the way, UPM has pinned its vision to become a world-class university. This implies that UPM needs to increase its effort to rise to a much higher level; its core business on teaching and learning, research and innovation and professional services. Hence, greater linkages between UPM and industries will be a crucial enabler to this end. In line with this, the creation of the new portfolio of the Deputy Vice Chancellor (Industry and Community Relations) by the Ministry of Higher Education Malaysia (MoHE) in UPM in late 2007 can be viewed as a remarkable effort of MoHE to strengthen the university-industry linkages

Flamenco music information retrieval.

El flamenco, un género musical centrado en la improvisación y la espontaneidad, tiene su origen en el sur de España y atrae a una creciente comunidad de aficionados de países de todo el mundo. El aumento constante y la accesibilidad a colecciones digitales de flamenco, en archivos de música y plataformas online, exige el desarrollo de métodos de análisis y descripción computacionales con el fin de indexar y analizar el contenido musical de manera automática. Music Information Retrieval (MIR) es un área de investigación multidisciplinaria dedicada a la extracción automática de información musical desde grabaciones de audio y partituras. Sin embargo, la gran mayoría de las herramientas existentes se dirigen a la música clásica y la música popular occidental y, a menudo, no se generalizan bien a las tradiciones musicales no occidentales, particularmente cuando las suposiciones relacionadas con la teoría musical no son válidas para estos géneros. Por otro lado, las características y los conceptos musicales específicos de una tradición musical pueden implicar nuevos desafíos computacionales, para los cuales no existen métodos adecuados. Esta tesis enfoca estas limitaciones existentes en el área abordando varios desafíos computacionales que surgen en el contexto de la música flamenca. Con este fin, se realizan una serie de contribuciones en forma de algoritmos novedosos, evaluaciones comparativas y estudios basados en datos, dirigidos a varias dimensiones musicales y que abarcan varias subáreas de ingeniería, matemática computacional, estadística, optimización y musicología computacional. Una particularidad del género, que influye enormemente en el trabajo presentado en esta tesis, es la ausencia de partituras para el cante flamenco. En consecuencia, los métodos computacionales deben basarse únicamente en el análisis de grabaciones, o de transcripciones extraídas automáticamente, lo que genera una colección de nuevos problemas computacionales. Un aspecto clave del flamenco es la presencia de patrones melódicos recurrentes, que esán sujetos a variación y ornamentación durante su interpretación. Desde la perspectiva computacional, identificamos tres tareas relacionadas a esta característica que se abordan en esta tesis: la clasificación por melodía, la búsqueda de secuencias melódicas y la extracción de patrones melódicos. Además, nos acercamos a la tarea de la detección no supervisada de frases melódicas repetidas y exploramos el uso de métodos de deep learning para la identificación de cantaores en grabaciones de video y la segmentación estructural de grabaciones de audio. Finalmente, demostramos en un estudio de minería de datos, cómo una exploración de anotaciones extraídas de manera automática de un corpus amplio de grabaciones nos ayuda a descubrir correlaciones interesantes y asimilar conocimientos sobre este género mayormente indocumentado.Flamenco is a rich performance-oriented art music genre from Southern Spain, which attracts a growing community of aficionados around the globe. The constantly increasing number of digitally available flamenco recordings in music archives, video sharing platforms and online music services calls for the development of genre-specific description and analysis methods, capable of automatically indexing and examining these collections in a content-driven manner. Music Information Retrieval is a multi-disciplinary research area dedicated to the automatic extraction of musical information from audio recordings and scores. Most existing approaches were however developed in the context of popular or classical music and do often not generalise well to non-Western music traditions, in particular when the underlying music theoretical assumptions do not hold for these genres. The specific characteristics and concepts of a music tradition can furthermore imply newcomputational challenges, for which no suitable methods exist. This thesis addresses these current shortcomings of Music Information Retrieval by tackling several computational challenge which arise in the context of flamenco music. To this end, a number of contributions to the field are made in form of novel algorithms, comparative evaluations and data-driven studies, directed at various musical dimensions and encompassing several sub-areas of computer science, computational mathematics, statistics, optimisation and computational musicology. A particularity of flamenco, which immensely shapes the work presented in this thesis, is the absence of written scores. Consequently, computational approaches can solely rely on the direct analysis of raw audio recordings or automatically extracted transcriptions, and this restriction generates set of new computational challenges. A key aspect of flamenco is the presence of reoccurring melodic templates, which are subject to heavy variation during performance. From a computational perspective, we identify three tasks related to this characteristic - melody classification, melody retrieval and melodic template extraction - which are addressed in this thesis. We furthermore approach the task of detecting repeated sung phrases in an unsupervised manner and explore the use of deep learning methods for image-based singer identification in flamenco videos and structural segmentation of flamenco recordings. Finally, we demonstrate in a data-driven corpus study, how automatic annotations can be mined to discover interesting correlations and gain insights into a largely undocumented genre

Singing Voice Recognition for Music Information Retrieval

This thesis proposes signal processing methods for analysis of singing voice audio signals, with the objectives of obtaining information about the identity and lyrics content of the singing. Two main topics are presented, singer identification in monophonic and polyphonic music, and lyrics transcription and alignment. The information automatically extracted from the singing voice is meant to be used for applications such as music classification, sorting and organizing music databases, music information retrieval, etc. For singer identification, the thesis introduces methods from general audio classification and specific methods for dealing with the presence of accompaniment. The emphasis is on singer identification in polyphonic audio, where the singing voice is present along with musical accompaniment. The presence of instruments is detrimental to voice identification performance, and eliminating the effect of instrumental accompaniment is an important aspect of the problem. The study of singer identification is centered around the degradation of classification performance in presence of instruments, and separation of the vocal line for improving performance. For the study, monophonic singing was mixed with instrumental accompaniment at different signal-to-noise (singing-to-accompaniment) ratios and the classification process was performed on the polyphonic mixture and on the vocal line separated from the polyphonic mixture. The method for classification including the step for separating the vocals is improving significantly the performance compared to classification of the polyphonic mixtures, but not close to the performance in classifying the monophonic singing itself. Nevertheless, the results show that classification of singing voices can be done robustly in polyphonic music when using source separation. In the problem of lyrics transcription, the thesis introduces the general speech recognition framework and various adjustments that can be done before applying the methods on singing voice. The variability of phonation in singing poses a significant challenge to the speech recognition approach. The thesis proposes using phoneme models trained on speech data and adapted to singing voice characteristics for the recognition of phonemes and words from a singing voice signal. Language models and adaptation techniques are an important aspect of the recognition process. There are two different ways of recognizing the phonemes in the audio: one is alignment, when the true transcription is known and the phonemes have to be located, other one is recognition, when both transcription and location of phonemes have to be found. The alignment is, obviously, a simplified form of the recognition task. Alignment of textual lyrics to music audio is performed by aligning the phonetic transcription of the lyrics with the vocal line separated from the polyphonic mixture, using a collection of commercial songs. The word recognition is tested for transcription of lyrics from monophonic singing. The performance of the proposed system for automatic alignment of lyrics and audio is sufficient for facilitating applications such as automatic karaoke annotation or song browsing. The word recognition accuracy of the lyrics transcription from singing is quite low, but it is shown to be useful in a query-by-singing application, for performing a textual search based on the words recognized from the query. When some key words in the query are recognized, the song can be reliably identified