深層学習を用いたロボット聴覚フレームワーク

早大学位記番号:新8470早稲田大

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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