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

A large, crowdsourced evaluation of gesture generation systems on common data : the GENEA Challenge 2020

Co-speech gestures, gestures that accompany speech, play an important role in human communication. Automatic co-speech gesture generation is thus a key enabling technology for embodied conversational agents (ECAs), since humans expect ECAs to be capable of multi-modal communication. Research into gesture generation is rapidly gravitating towards data-driven methods. Unfortunately, individual research efforts in the field are difficult to compare: there are no established benchmarks, and each study tends to use its own dataset, motion visualisation, and evaluation methodology. To address this situation, we launched the GENEA Challenge, a gesture-generation challenge wherein participating teams built automatic gesture-generation systems on a common dataset, and the resulting systems were evaluated in parallel in a large, crowdsourced user study using the same motion-rendering pipeline. Since differences in evaluation outcomes between systems now are solely attributable to differences between the motion-generation methods, this enables benchmarking recent approaches against one another in order to get a better impression of the state of the art in the field. This paper reports on the purpose, design, results, and implications of our challenge.Part of Proceedings: ISBN 978-145038017-1QC 20210607</p