2 research outputs found
ARMAS: Active Reconstruction of Missing Audio Segments
Digital audio signal reconstruction of a lost or corrupt segment using deep
learning algorithms has been explored intensively in recent years.
Nevertheless, prior traditional methods with linear interpolation, phase coding
and tone insertion techniques are still in vogue. However, we found no research
work on reconstructing audio signals with the fusion of dithering,
steganography, and machine learning regressors. Therefore, this paper proposes
the combination of steganography, halftoning (dithering), and state-of-the-art
shallow (RF- Random Forest regression) and deep learning (LSTM- Long Short-Term
Memory) methods. The results (including comparing the SPAIN, Autoregressive,
deep learning-based, graph-based, and other methods) are evaluated with three
different metrics. The observations from the results show that the proposed
solution is effective and can enhance the reconstruction of audio signals
performed by the side information (e.g., Latent representation and learning for
audio inpainting) steganography provides. Moreover, this paper proposes a novel
framework for reconstruction from heavily compressed embedded audio data using
halftoning (i.e., dithering) and machine learning, which we termed the HCR
(halftone-based compression and reconstruction). This work may trigger interest
in optimising this approach and/or transferring it to different domains (i.e.,
image reconstruction). Compared to existing methods, we show improvement in the
inpainting performance in terms of signal-to-noise (SNR), the objective
difference grade (ODG) and the Hansen's audio quality metric.Comment: 9 pages, 2 Tables, 8 Figure
Algebraic Neural Architecture Representation, Evolutionary Neural Architecture Search, and Novelty Search in Deep Reinforcement Learning
Evolutionary algorithms have recently re-emerged as powerful tools for machine learning and artificial intelligence, especially when combined with advances in deep learning developed over the last decade. In contrast to the use of fixed architectures and rigid learning algorithms, we leveraged the open-endedness of evolutionary algorithms to make both theoretical and methodological contributions to deep reinforcement learning. This thesis explores and develops two major areas at the intersection of evolutionary algorithms and deep reinforcement learning: generative network architectures and behaviour-based optimization. Over three distinct contributions, both theoretical and experimental methods were applied to deliver a novel mathematical framework and experimental method for generative, modular neural network architecture search for reinforcement learning, and a generalized formulation of a behaviour- based optimization framework for reinforcement learning called novelty search. Experimental results indicate that both alternative, behaviour-based optimization and neural architecture search can each be used to improve learning in the popular Atari 2600 benchmark compared to DQN — a popular gradient-based method. These results are in-line with related work demonstrating that strictly gradient-free methods are competitive with gradient-based reinforcement learning. These contributions, together with other successful combinations of evolutionary algorithms and deep learning, demonstrate that alternative architectures and learning algorithms to those conventionally used in deep learning should be seriously investigated in an effort to drive progress in artificial intelligence