Performance Following: Real-Time Prediction of Musical Sequences Without a Score

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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

Singing voice resynthesis using concatenative-based techniques

Tese de Doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201

AI Methods in Algorithmic Composition: A Comprehensive Survey

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.This study was partially supported by a grant for the MELOMICS project (IPT-300000-2010-010) from the Spanish Ministerio de Ciencia e Innovación, and a grant for the CAUCE project (TSI-090302-2011-8) from the Spanish Ministerio de Industria, Turismo y Comercio. The first author was supported by a grant for the GENEX project (P09-TIC- 5123) from the Consejería de Innovación y Ciencia de Andalucía

Singing voice resynthesis using concatenative-based techniques

Dissertação submetida à Faculdade de Engenharia da Universidade do Porto para satisfação parcial dos requisitos do grau de doutor em Engenharia Informática.Singing has an important role in our life, and although synthesizers have been trying to replicate every musical instrument for decades, is was only during the last nine years that commercial singing synthesizers started to appear, allowing the ability to merge music and text, i.e., singing. These solutions may present realistic results on some situations, but they require time consuming processes and experienced users. The goal of this research work is to develop, create or adapt techniques that allow the resynthesis of the singing voice, i.e., allow the user to directly control a singing voice synthesizer using his/her own voice. The synthesizer should be able to replicate, as close as possible, the same melody, same phonetic sequence, and the same musical performance. Initially, some work was developed trying to resynthesize piano recordings with evolutionary approaches, using Genetic Algorithms, where a population of individuals (candidate solutions) representing a sequence of music notes evolved over time, tries to match an original audio stream. Later, the focus would return to the singing voice, exploring techniques as Hidden Markov Models, Neural Network Self Organized Maps, among others. Finally, a Concatenative Unit Selection approach was chosen as the core of a singing voice resynthesis system. By extracting energy, pitch and phonetic information (MFCC, LPC), and using it within a phonetic similarity Viterbi-based Unit Selection System, a sequence of internal sound library frames is chosen to replicate the original audio performance. Although audio artifacts still exist, preventing its use on professional applications, the concept of a new audio tool was created, that presents high potential for future work, not only in singing voice, but in other musical or speech domains.This dissertation had the kind support of FCT (Portuguese Foundation for Science and Technology, an agency of the Portuguese Ministry for Science, Technology and Higher Education) under grant SFRH / BD / 30300 / 2006, and has been articulated with research project PTDC/SAU-BEB/104995/2008 (Assistive Real-Time Technology in Singing) whose objectives include the development of interactive technologies helping the teaching and learning of singing

The development of corpus-based computer assisted composition program and its application for instrumental music composition

In the last 20 years, we have seen the nourishing environment for the development of music software using a corpus of audio data expanding significantly, namely that synthesis techniques producing electronic sounds, and supportive tools for creative activities are the driving forces to the growth. Some software produces a sequence of sounds by means of synthesizing a chunk of source audio data retrieved from an audio database according to a rule. Since the matching of sources is processed according to their descriptive features extracted by FFT analysis, the quality of the result is significantly influenced by the outcomes of the Audio Analysis, Segmentation, and Decomposition. Also, the synthesis process often requires a considerable amount of sample data and this can become an obstacle to establish easy, inexpensive, and user-friendly applications on various kinds of devices. Therefore, it is crucial to consider how to treat the data and construct an efficient database for the synthesis. We aim to apply corpusbased synthesis techniques to develop a Computer Assisted Composition program, and to investigate the actual application of the program on ensemble pieces. The goal of this research is to apply the program to the instrumental music composition, refine its function, and search new avenues for innovative compositional method

Musicians and Machines: Bridging the Semantic Gap In Live Performance

PhDThis thesis explores the automatic extraction of musical information from live performances – with the intention of using that information to create novel, responsive and adaptive performance tools for musicians. We focus specifically on two forms of musical analysis – harmonic analysis and beat tracking. We present two harmonic analysis algorithms – specifically we present a novel chroma vector analysis technique which we later use as the input for a chord recognition algorithm. We also present a real-time beat tracker, based upon an extension of state of the art non-causal models, that is computationally efficient and capable of strong performance compared to other models. Furthermore, through a modular study of several beat tracking algorithms we attempt to establish methods to improve beat tracking and apply these lessons to our model. Building upon this work, we show that these analyses can be combined to create a beat-synchronous musical representation, with harmonic information segmented at the level of the beat. We present a number of ways of calculating these representations and discuss their relative merits. We proceed by introducing a technique, which we call Performance Following, for recognising repeated patterns in live musical performances. Through examining the real-time beat-synchronous musical representation, this technique makes predictions of future harmonic content in musical performances with no prior knowledge in the form of a score. Finally, we present a number of potential applications for live performances that incorporate the real-time musical analysis techniques outlined previously. The applications presented include audio effects informed by beat tracking, a technique for synchronising video to a live performance, the use of harmonic information to control visual displays and an automatic accompaniment system based upon our performance following technique.EPSR

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

A computational framework for sound segregation in music signals

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