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pro vyhledávání: '"Comanducci, A"'
Text-To-Music (TTM) models have recently revolutionized the automatic music generation research field. Specifically, by reaching superior performances to all previous state-of-the-art models and by lowering the technical proficiency needed to use the
Externí odkaz:
http://arxiv.org/abs/2409.10684
Recent advancements in deep learning have led to widespread use of techniques for audio content generation, notably employing Denoising Diffusion Probabilistic Models (DDPM) across various tasks. Among these, Foley Sound Synthesis is of particular in
Externí odkaz:
http://arxiv.org/abs/2409.09162
In recent years, text-to-music models have been the biggest breakthrough in automatic music generation. While they are unquestionably a showcase of technological progress, it is not clear yet how they can be realistically integrated into the artistic
Externí odkaz:
http://arxiv.org/abs/2407.04333
Deep learning models are widely applied in the signal processing community, yet their inner working procedure is often treated as a black box. In this paper, we investigate the use of eXplainable Artificial Intelligence (XAI) techniques to learning-b
Externí odkaz:
http://arxiv.org/abs/2404.03436
In recent years, text-to-audio models have revolutionized the field of automatic audio generation. This paper investigates their application in generating synthetic datasets for training data-driven models. Specifically, this study analyzes the perfo
Externí odkaz:
http://arxiv.org/abs/2403.17864
Autor:
Miotello, Federico, Ostan, Paolo, Pezzoli, Mirco, Comanducci, Luca, Bernardini, Alberto, Antonacci, Fabio, Sarti, Augusto
In this paper, we present HOMULA-RIR, a dataset of room impulse responses (RIRs) acquired using both higher-order microphones (HOMs) and a uniform linear array (ULA), in order to model a remote attendance teleconferencing scenario. Specifically, meas
Externí odkaz:
http://arxiv.org/abs/2402.13896
Reconstructing the room transfer functions needed to calculate the complex sound field in a room has several important real-world applications. However, an unpractical number of microphones is often required. Recently, in addition to classical signal
Externí odkaz:
http://arxiv.org/abs/2402.04866
Autor:
Miotello, Federico, Comanducci, Luca, Pezzoli, Mirco, Bernardini, Alberto, Antonacci, Fabio, Sarti, Augusto
Reconstructing the sound field in a room is an important task for several applications, such as sound control and augmented (AR) or virtual reality (VR). In this paper, we propose a data-driven generative model for reconstructing the magnitude of aco
Externí odkaz:
http://arxiv.org/abs/2312.08821
Timbre transfer techniques aim at converting the sound of a musical piece generated by one instrument into the same one as if it was played by another instrument, while maintaining as much as possible the content in terms of musical characteristics s
Externí odkaz:
http://arxiv.org/abs/2307.04586
Synthesis of soundfields through irregular loudspeaker arrays based on convolutional neural networks
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2024, Iss 1, Pp 1-20 (2024)
Abstract Most soundfield synthesis approaches deal with extensive and regular loudspeaker arrays, which are often not suitable for home audio systems, due to physical space constraints. In this article, we propose a technique for soundfield synthesis
Externí odkaz:
https://doaj.org/article/b9fefc84b1d74e49954f20c73af6870a
