The effect of using pitch and duration for symbolic music retrieval

Quite reasonable retrieval effectiveness is achieved for retrieving polyphonic (multiple notes at once) music that is symbolically encoded via melody queries, using relatively simple pattern matching techniques based on pitch sequences. Earlier work showed that adding duration information was not particularly helpful for improving retrieval effectiveness. In this paper we demonstrate that defining the duration information as the time interval between consecutive notes does lead to more effective retrieval when combined with pitch-based pattern matching in our collection of over 14 000 MIDI files

Rhythm extraction from polyphonic symbolic music

International audienceWe focus on the rhythmic component of symbolic music similarity, proposing several ways to extract a monophonic rhythmic signature from a symbolic poly- phonic score. To go beyond the simple extraction of all time intervals between onsets (noteson extraction), we select notes according to their length (short and long extractions) or their intensities (intensity+/− extractions). Once the rhythm is extracted, we use dynamic programming to compare several sequences. We report results of analysis on the size of rhythm patterns that are specific to a unique piece, as well as experiments on similarity queries (ragtime music and Bach chorale variations). These results show that long and intensity+ extractions are often good choices for rhythm extraction. Our conclusions are that, even from polyphonic symbolic music, rhythm alone can be enough to identify a piece or to perform pertinent music similarity queries, especially when using wise rhythm extractions

Towards the automated analysis of simple polyphonic music : a knowledge-based approach

PhDMusic understanding is a process closely related to the knowledge and experience of the listener. The amount of knowledge required is relative to the complexity of the task in hand. This dissertation is concerned with the problem of automatically decomposing musical signals into a score-like representation. It proposes that, as with humans, an automatic system requires knowledge about the signal and its expected behaviour to correctly analyse music. The proposed system uses the blackboard architecture to combine the use of knowledge with data provided by the bottom-up processing of the signal's information. Methods are proposed for the estimation of pitches, onset times and durations of notes in simple polyphonic music. A method for onset detection is presented. It provides an alternative to conventional energy-based algorithms by using phase information. Statistical analysis is used to create a detection function that evaluates the expected behaviour of the signal regarding onsets. Two methods for multi-pitch estimation are introduced. The first concentrates on the grouping of harmonic information in the frequency-domain. Its performance and limitations emphasise the case for the use of high-level knowledge. This knowledge, in the form of the individual waveforms of a single instrument, is used in the second proposed approach. The method is based on a time-domain linear additive model and it presents an alternative to common frequency-domain approaches. Results are presented and discussed for all methods, showing that, if reliably generated, the use of knowledge can significantly improve the quality of the analysis.Joint Information Systems Committee (JISC) in the UK National Science Foundation (N.S.F.) in the United states. Fundacion Gran Mariscal Ayacucho in Venezuela

Streaming Audio Using MPEG–7 Audio Spectrum Envelope to Enable Self-similarity within Polyphonic Audio

One method overlooked to date, which can work alongside existing audio compression schemes, is that which takes account of the semantics and natural repetition of music through meta-data tagging. Similarity detection within polyphonic audio has presented problematic challenges within the field of Music Information Retrieval.  This paper presents a method (SoFI) for improving the quality of stored audio being broadcast over any wireless medium through meta-data which has a number of market applications all with market value. Our system works at the content level thus rendering it applicable in existing streaming services. Using the MPEG-7 Audio Spectrum Envelope (ASE) gives features for extraction and combined with k-means clustering enables self-similarity to be performed within polyphonic audio. SoFI uses string matching to identify similarity between large sections of clustered audio. Objective evaluations of SoFI give positive results which show that SoFI is shown to detect high levels of similarity on varying lengths of time within an audio file. In a scale between 0 and 1 with 0 the best, a clear correlation between similarly identified sections of 0.2491 shows successful identification

Content-based music retrieval by acoustic query

Ph.DDOCTOR OF PHILOSOPH

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

Graph based representation of the music symbolic level. A music information retrieval application

In this work, a new music symbolic level representation system is described. It has been tested in two information retrieval tasks concerning similarity between segments of music and genre detection of a given segment. It could include both harmonic and contrapuntal informations. Moreover, a new large dataset consisting of more than 5000 leadsheets is presented, with meta informations taken from different web databases, including author information, year of first performance, lyrics, genre, etc.ope