Zobrazeno 1 - 10
of 79
pro vyhledávání: '"Pikrakis, Aggelos"'
Most contemporary music tagging systems rely on large volumes of annotated data. As an alternative, we investigate the extent to which synthetically generated music excerpts can improve tagging systems when only small annotated collections are availa
Externí odkaz:
http://arxiv.org/abs/2407.02156
Autor:
Koufopoulou, Amalia Artemis, Papadimitriou, Athanasios, Pikrakis, Aggelos, Psarakis, Mihalis, Hely, David
High-level synthesis (HLS) tools have provided significant productivity enhancements to the design flow of digital systems in recent years, resulting in highly-optimized circuits, in terms of area and latency. Given the evolution of hardware attacks,
Externí odkaz:
http://arxiv.org/abs/2312.07594
We are investigating the broader concept of using AI-based generative music systems to generate training data for Music Information Retrieval (MIR) tasks. To kick off this line of work, we ran an initial experiment in which we trained a genre classif
Externí odkaz:
http://arxiv.org/abs/2311.09094
We apply post-processing to the class probability distribution outputs of audio event classification models and employ reinforcement learning to jointly discover the optimal parameters for various stages of a post-processing stack, such as the classi
Externí odkaz:
http://arxiv.org/abs/2208.09201
Publikováno v:
In Engineering Applications of Artificial Intelligence July 2024 133 Part E
In this work we apply deep reinforcement learning to the problems of navigating a three-dimensional environment and inferring the locations of human speaker audio sources within, in the case where the only available information is the raw sound from
Externí odkaz:
http://arxiv.org/abs/2110.12778
In this work we use deep reinforcement learning to create an autonomous agent that can navigate in a two-dimensional space using only raw auditory sensory information from the environment, a problem that has received very little attention in the rein
Externí odkaz:
http://arxiv.org/abs/2105.04488
Predictive analytics over mobility data are of great importance since they can assist an analyst to predict events, such as collisions, encounters, traffic jams, etc. A typical example of such analytics is future location prediction, where the goal i
Externí odkaz:
http://arxiv.org/abs/2102.08870
Autor:
Kroher, Nadine, Pikrakis, Aggelos
We present computational tools that we developed for the analysis of a large corpus of flamenco music recordings, along with the related exploratory findings. The proposed computational backend is based on a set of Convolutional Neural Networks that
Externí odkaz:
http://arxiv.org/abs/1807.00069
Autor:
Karamanolakis, Giannis, Iosif, Elias, Zlatintsi, Athanasia, Pikrakis, Aggelos, Potamianos, Alexandros
The recent development of Audio-based Distributional Semantic Models (ADSMs) enables the computation of audio and lexical vector representations in a joint acoustic-semantic space. In this work, these joint representations are applied to the problem
Externí odkaz:
http://arxiv.org/abs/1612.08391