554,835 research outputs found
DrumGAN: Synthesis of Drum Sounds With Timbral Feature Conditioning Using Generative Adversarial Networks
Synthetic creation of drum sounds (e.g., in drum machines) is commonly
performed using analog or digital synthesis, allowing a musician to sculpt the
desired timbre modifying various parameters. Typically, such parameters control
low-level features of the sound and often have no musical meaning or perceptual
correspondence. With the rise of Deep Learning, data-driven processing of audio
emerges as an alternative to traditional signal processing. This new paradigm
allows controlling the synthesis process through learned high-level features or
by conditioning a model on musically relevant information. In this paper, we
apply a Generative Adversarial Network to the task of audio synthesis of drum
sounds. By conditioning the model on perceptual features computed with a
publicly available feature-extractor, intuitive control is gained over the
generation process. The experiments are carried out on a large collection of
kick, snare, and cymbal sounds. We show that, compared to a specific prior work
based on a U-Net architecture, our approach considerably improves the quality
of the generated drum samples, and that the conditional input indeed shapes the
perceptual characteristics of the sounds. Also, we provide audio examples and
release the code used in our experiments.Comment: 8 pages, 1 figure, 3 tables, accepted in Proc. of the 21st
International Society for Music Information Retrieval (ISMIR2020
CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis
We propose and evaluate a new technique for learning hybrid automata
automatically by observing the runtime behavior of a dynamical system. Working
from a sequence of continuous state values and predicates about the
environment, CHARDA recovers the distinct dynamic modes, learns a model for
each mode from a given set of templates, and postulates causal guard conditions
which trigger transitions between modes. Our main contribution is the use of
information-theoretic measures (1)~as a cost function for data segmentation and
model selection to penalize over-fitting and (2)~to determine the likely causes
of each transition. CHARDA is easily extended with different classes of model
templates, fitting methods, or predicates. In our experiments on a complex
videogame character, CHARDA successfully discovers a reasonable
over-approximation of the character's true behaviors. Our results also compare
favorably against recent work in automatically learning probabilistic timed
automata in an aircraft domain: CHARDA exactly learns the modes of these
simpler automata.Comment: 7 pages, 2 figures. Accepted for IJCAI 201
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Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme
The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as water delivery requirement, environmental constraints, dry/wet conditions, etc. In this paper, a robust reservoir outflow simulation model is presented, which incorporates one of the well-developed data-mining models (Classification and Regression Tree) to predict the complicated human-controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross-validation approach is further implemented to improve CART's predictive performance. An application study of nine major reservoirs in California is carried out. Results produced by the enhanced CART, original CART, and random forest are compared with observation. The statistical measurements show that the enhanced CART and random forest overperform the CART control run in general, and the enhanced CART algorithm gives a better predictive performance over random forest in simulating the peak flows. The results also show that the proposed model is able to consistently and reasonably predict the expert release decisions. Experiments indicate that the release operation in the Oroville Lake is significantly dominated by SWP allocation amount and reservoirs with low elevation are more sensitive to inflow amount than others
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