Multi-channel approaches for musical audio content analysis

The goal of this research project is to undertake a critical evaluation of signal representations for musical audio content analysis. In particular it will contrast three different means for undertaking the analysis of micro-rhythmic content in Afro-Latin American music, namely through the use of: i) stereo or mono mixed recordings; ii) separated sources obtained via state of the art musical audio source separation techniques; and iii) the use of perfectly separated multi-track stems. In total the project comprises the following four objectives: i) To compile a dataset of mixed and multi-channel recordings of the Brazilian Maracatu musicians; ii) To conceive methods for rhythmical micro-variations analysis and pattern recognition; iii) To explore diverse music source separation approaches that preserve micro-rhythmic content; iv) To evaluate the performance of several automatic onset estimation approaches; and v) To compare the rhythmic analysis obtained from the original multi-channel sources versus the separated ones to evaluate separation quality regarding microtiming identification

Computational Tonality Estimation: Signal Processing and Hidden Markov Models

PhDThis thesis investigates computational musical tonality estimation from an audio signal. We present a hidden Markov model (HMM) in which relationships between chords and keys are expressed as probabilities of emitting observable chords from a hidden key sequence. The model is tested first using symbolic chord annotations as observations, and gives excellent global key recognition rates on a set of Beatles songs. The initial model is extended for audio input by using an existing chord recognition algorithm, which allows it to be tested on a much larger database. We show that a simple model of the upper partials in the signal improves percentage scores. We also present a variant of the HMM which has a continuous observation probability density, but show that the discrete version gives better performance. Then follows a detailed analysis of the effects on key estimation and computation time of changing the low level signal processing parameters. We find that much of the high frequency information can be omitted without loss of accuracy, and significant computational savings can be made by applying a threshold to the transform kernels. Results show that there is no single ideal set of parameters for all music, but that tuning the parameters can make a difference to accuracy. We discuss methods of evaluating more complex tonal changes than a single global key, and compare a metric that measures similarity to a ground truth to metrics that are rooted in music retrieval. We show that the two measures give different results, and so recommend that the choice of evaluation metric is determined by the intended application. Finally we draw together our conclusions and use them to suggest areas for continuation of this research, in the areas of tonality model development, feature extraction, evaluation methodology, and applications of computational tonality estimation.Engineering and Physical Sciences Research Council (EPSRC)

Soundscape Generation Using Web Audio Archives

Os grandes e crescentes acervos de áudio na web têm transformado a prática do design de som. Neste contexto, sampling -- uma ferramenta essencial do design de som -- mudou de gravações mecânicas para os domínios da cópia e reprodução no computador. A navegação eficaz nos grandes acervos e a recuperação de conteúdo tornaram-se um problema bem identificado em Music Information Retrieval, nomeadamente através da adoção de metodologias baseadas no conteúdo do áudio.Apesar da sua robustez e eficácia, as soluções tecnológicas atuais assentam principalmente em métodos (estatísticos) de processamento de sinal, cuja terminologia atinge um nível de adequação centrada no utilizador.Esta dissertação avança uma nova estratégia orientada semanticamente para navegação e recuperação de conteúdo de áudio, em particular, sons ambientais, a partir de grandes acervos de áudio na web. Por fim, pretendemos simplificar a extração de pedidos definidos pelo utilizador para promover uma geração fluida de paisagens sonoras. No nosso trabalho, os pedidos aos acervos de áudio na web são feitos por dimensões afetivas que se relacionam com estados emocionais (exemplo: baixa ativação e baixa valência) e descrições semânticas das fontes de áudio (exemplo: chuva). Para tal, mapeamos as anotações humanas das dimensões afetivas para descrições espectrais de áudio extraídas do conteúdo do sinal. A extração de novos sons dos acervos da web é feita estipulando um pedido que combina um ponto num plano afetivo bidimensional e tags semânticas. A aplicação protótipo, MScaper, implementa o método no ambiente Ableton Live. A avaliação da nossa pesquisa avaliou a confiabilidade perceptual dos descritores espectrais de áudio na captura de dimensões afetivas e a usabilidade da MScaper. Os resultados mostram que as características espectrais do áudio capturam significativamente as dimensões afetivas e que o MScaper foi entendido pelos os utilizadores experientes como tendo excelente usabilidade.The large and growing archives of audio content on the web have been transforming the sound design practice. In this context, sampling -- a fundamental sound design tool -- has shifted from mechanical recording to the realms of the copying and cutting on the computer. To effectively browse these large archives and retrieve content became a well-identified problem in Music Information Retrieval, namely through the adoption of audio content-based methodologies. Despite its robustness and effectiveness, current technological solutions rely mostly on (statistical) signal processing methods, whose terminology do attain a level of user-centered explanatory adequacy.This dissertation advances a novel semantically-oriented strategy for browsing and retrieving audio content, in particular, environmental sounds, from large web audio archives. Ultimately, we aim to streamline the retrieval of user-defined queries to foster a fluid generation of soundscapes. In our work, querying web audio archives is done by affective dimensions that relate to emotional states (e.g., low arousal and low valence) and semantic audio source descriptions (e.g., rain). To this end, we map human annotations of affective dimensions to spectral audio-content descriptions extracted from the signal content. Retrieving new sounds from web archives is then made by specifying a query which combines a point in a 2-dimensional affective plane and semantic tags. A prototype application, MScaper, implements the method in the Ableton Live environment. An evaluation of our research assesses the perceptual soundness of the spectral audio-content descriptors in capturing affective dimensions and the usability of MScaper. The results show that spectral audio features significantly capture affective dimensions and that MScaper has been perceived by expert-users as having excellent usability

Augmenting Music Sheets with Harmonic Fingerprints

Conventional Music Notation (CMN) is the well-established foundation for the written communication of musical information, such as rhythm, harmony, or timbre. However, CMN suffers from the complexity of its visual encoding and the need for extensive training to acquire proficiency and legibility. While alternative notations using additional visual variables (such as color to improve pitch identification) have been proposed, the music community does not readily accept notation systems that vary widely from the CMN. Therefore, to support student musicians in understanding the harmonic relationship of notes, instead of replacing the CMN, we present a visualization technique that augments a digital music sheet with a harmonic fingerprint glyph. Our design exploits the circle of fifths - a fundamental concept in music theory, as a visual metaphor. By attaching these visual glyphs to each bar of a selected composition we provide additional information about the salient harmonic features available in a musical piece. We conducted a user study to analyze the performance of experts and non-experts in an identification and comparison task of recurring patterns. The evaluation shows that the harmonic fingerprint supports these tasks without the need for close-reading, as when compared to a not-annotated music sheet.Comment: (9+1) pages; 5 figures; User Stud