15 research outputs found
ChoreoNet: Towards Music to Dance Synthesis with Choreographic Action Unit
Dance and music are two highly correlated artistic forms. Synthesizing dance
motions has attracted much attention recently. Most previous works conduct
music-to-dance synthesis via directly music to human skeleton keypoints
mapping. Meanwhile, human choreographers design dance motions from music in a
two-stage manner: they firstly devise multiple choreographic dance units
(CAUs), each with a series of dance motions, and then arrange the CAU sequence
according to the rhythm, melody and emotion of the music. Inspired by these, we
systematically study such two-stage choreography approach and construct a
dataset to incorporate such choreography knowledge. Based on the constructed
dataset, we design a two-stage music-to-dance synthesis framework ChoreoNet to
imitate human choreography procedure. Our framework firstly devises a CAU
prediction model to learn the mapping relationship between music and CAU
sequences. Afterwards, we devise a spatial-temporal inpainting model to convert
the CAU sequence into continuous dance motions. Experimental results
demonstrate that the proposed ChoreoNet outperforms baseline methods (0.622 in
terms of CAU BLEU score and 1.59 in terms of user study score).Comment: 10 pages, 5 figures, Accepted by ACM MM 202
A Comparative Study on Transformer vs RNN in Speech Applications
Sequence-to-sequence models have been widely used in end-to-end speech
processing, for example, automatic speech recognition (ASR), speech translation
(ST), and text-to-speech (TTS). This paper focuses on an emergent
sequence-to-sequence model called Transformer, which achieves state-of-the-art
performance in neural machine translation and other natural language processing
applications. We undertook intensive studies in which we experimentally
compared and analyzed Transformer and conventional recurrent neural networks
(RNN) in a total of 15 ASR, one multilingual ASR, one ST, and two TTS
benchmarks. Our experiments revealed various training tips and significant
performance benefits obtained with Transformer for each task including the
surprising superiority of Transformer in 13/15 ASR benchmarks in comparison
with RNN. We are preparing to release Kaldi-style reproducible recipes using
open source and publicly available datasets for all the ASR, ST, and TTS tasks
for the community to succeed our exciting outcomes.Comment: Accepted at ASRU 201
Gasto público ambiental: los casos del Perú y El Salvador
En este documento se desarrolla un estudio sobre tres aspectos fundamentales en torno a los mecanismos que propone El Salvador como respuesta a los inminentes cambios que trae consigo el cambio climático. Por un lado, presenta la política de cambio climático a través de la revisión de la Estrategia de Cambio Climático desarrollada por el Ministerio de Ambiente y Recursos Naturales (MARN) y una síntesis de investigaciones asociadas a los costos en los que incurriría la sociedad salvadoreña ante al cambio climático y la variabilidad de los fenómenos climatológicos.Por el otro, desarrolla una metodología sencilla de clasificación del gasto de las entidades en materia ambiental, tratando de cuantificar el gasto público relacionada al CC en materia de mitigación, adaptación, reparación o compensación sobre la base del Presupuesto General del Estado correspondiente al año fiscal 2012.Introducción .-- I. Marco de referencia .-- II. El caso del Perú .-- III. El caso de El Salvador .-- IV. Conclusiones
Asignación de recursos: un ejemplo de focalización en el Perú
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