Networked Music Performance (NMP) is a mediated interactional modality with a tremendous potential impact on professional and amateur musicians, as it enables real-time interaction from remote locations. One of the known limiting factors of distributed networked performances is the impact of the unavoidable packet delay and jitter introduced by IP networks, which make it difficult to keep a stable tempo during the performance. This paper investigates the tolerance of remotely interacting musicians towards adverse network conditions. We do so for various musical instruments and music genres, as a function of rhythmic complexity and tempo. In order to conduct this analysis, we implemented a testbed for psycho-acoustic analysis emulating the behavior of a real IP network in terms of variable transmission delay and jitter, and we quantitatively evaluated the impact of such parameters on the trend of the tempo maintained during the performance and on the perceptual quality of the musical interaction.
The widespread diffusion of portable devices capable of capturing high-quality multimedia data, together with the rapid proliferation of media sharing platforms, has determined an incredible growth of user-generated content available online. Since it is hard to strictly regulate this trend, illegal diffusion of copyrighted material is often likely to occur. This is the case of audio bootlegs, i.e., concerts illegally recorded and redistributed by fans. In this paper, we propose a bootleg detector, with the aim of disambiguating between: i) bootlegs unofficially recorded; ii) live concerts officially published; iii) studio recordings from officially released albums. The proposed method is based on audio feature analysis and machine learning techniques. We exploit a deep learning paradigm to extract highly characterizing features from audio excerpts, and a supervised classifier for detection. The method is validated against a dataset of nearly 500 songs, and results are compared to a state-of-the-art detector. The conducted experiments confirm the capability of deep learning techniques to outperform classic feature extraction approaches
Cooperative music making in networked environments has been subject of extensive research, scientific and artistic. Networked music performance (NMP) is attracting renewed interest thanks to the growing availability of effective technology and tools for computer-based communications, especially in the area of distance and blended learning applications. We propose a conceptual framework for NMP research and design in the context of classical chamber music practice and learning: presence-related constructs and objective quality metrics are used to problematize and systematize the many factors affecting the experience of studying and practicing music in a networked environment. To this end, a preliminary NMP experiment on the effect of latency on chamber music duos experience and quality of the performance is introduced. The degree of involvement, perceived coherence, and immersion of the NMP environment are here combined with measures on the networked performance, including tempo trends and misalignments from the shared score. Early results on the impact of temporal factors on NMP musical interaction are outlined, and their methodological implications for the design of pedagogical applications are discussed.
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