Robot computing for music visualization

© Springer Nature Switzerland AG 2019. This paper presents an algorithm design of Music Visualization on Robot (MVR) which could automatically link the flashlight, color, and emotion through music. We call this algorithm as MVR algorithm that composed by two analyses. First, we focus on Music Signal Analysis. Second, we focus on Music Sentiment Analysis. We integrate two analysis results and implement the MVR algorithm on a robot called Zenbo which is released from ASUS Company. We perform the Zenbo Robot in luminous environments. The MVR system not only could be used in Zenbo robot but also could extend to other fields of Artificial Intelligent (AI) equipment in the future

Composing Dynamical Systems to Realize Dynamic Robotic Dancing

This paper presents a methodology for the composition of complex dynamic behaviors in legged robots, and illustrates these concepts to experimentally achieve robotic dancing . Inspired by principles from dynamic locomotion, we begin by constructing controllers that drive a collection of virtual constraints to zero; this creates a low-dimensional representation of the bipedal robot. Given any two poses of the robot, we utilize this low-dimensional representation to connect these poses through a dynamic transition. The end result is a meta-dynamical system that describes a series of poses (indexed by the vertices of a graph) together with dynamic transitions (indexed by the edges) connecting these poses. These formalisms are illustrated in the case of dynamic dancing; a collection of ten poses are connected through dynamic transitions obtained via virtual constraints, and transitions through the graph are synchronized with music tempo. The resulting meta-dynamical system is realized experimentally on the bipedal robot AMBER 2 yielding dynamic robotic dancing

Acoustic environment as an indicator of social and physical context

Acoustic environments provide many valuable cues for context-aware computing applications. From the acoustic environment we can infer the types of activity, communication modes and other actors involved in the activity. Environmental or background noise can be classified with a high degree of accuracy using recordings from microphones commonly found in PDAs and other consumer devices. We describe an acoustic environment recognition system incorporating an adaptive learning mechanism and its use in a noise tracker. We show how this information is exploited in a mobile context framework. To illustrate our approach we describe a context-aware multimodal weather forecasting service, which accepts spoken or written queries and presents forecast information in several forms, including email, voice and sign languages

Acoustic Environment as an Indicator of Social and Physical Context

Acoustic environments provide many valuable cues for context-aware computing applications. From the acoustic environment we can infer the types of activity, communication modes and other actors involved in the activity. Environmental or background noise can be classified with a high degree of accuracy using recordings from microphones commonly found in PDAs and other consumer devices. We describe an acoustic environment recognition system incorporating an adaptive learning mechanism and its use in a noise tracker. We show how this information is exploited in a mobile context framework. To illustrate our approach we describe a context-aware multimodal weather forecasting service, which accepts spoken or written queries and presents forecast information in several forms, including email, voice and sign languag