A biped robot that keeps steps in time with musical beats while listening to music with its own ears

2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012)

Batula

Schmidt

et al. 2012

Abstract-The recognition of emotions and the generation of appropriate responses is a key component for facilitating more natural human-robot interaction. Music, often called the "language of emotions," is a particularly useful medium for investigating questions involving the expression of emotion. Likewise, movements and gestures, such as dance, can also communicate specific emotions to human observers. We apply an efficient, causal technique for estimating the emotions (mood) from music audio to enable a humanoid to perform gestures reflecting the musical mood. We implement this system using Hubo, an adult-sized humanoid that has been used in several applications of musical robotics. Our preliminary experiments indicate that the system is able to produce dancelike gestures that are judged by human observers to match the perceived emotion of the music.

Section: Related Workmentioning

confidence: 99%

Affective gesturing with music mood recognition

2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012)

Batula

Schmidt

et al. 2012

“…The HRP-2 has been enabled to reproduce traditional folk dances [6]. A few such robots already incorporate beat information, such as Honda's ASIMO, which can step in place with music based upon the beats that it "hears" [7]. Our group has enabled a Robonova to perform motions in synchrony to beats in music [8], [9].…”

Section: Related Workmentioning

confidence: 99%

Enabling humanoid musical interaction and performance

Kim

2011 International Conference on Collaboration Technologies and Systems (CTS)

Batula

et al. 2011

“…dgrunberg@drexel.edu 2 Youngmoo E. Kim is with the Faculty of the Department of Electrical and Computer Engineering, Drexel University, 3141 Market Street, Philadelphia, Pennsylvania, USA. ykim@drexel.edu beat locations have been developed, few allow robots to learn the spectral characteristics that determine what a beat sounds like [1], [2]. The ability to learn how beats sound, though, could help robots in understanding higher-level rhythmic structure, just as it can help humans.…”

Section: Introductionmentioning

confidence: 99%

Rapidly learning musical beats in the presence of environmental and robot ego noise

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

Kim

2014

Abstract-Humans can often learn high-level features of a piece of music, such as beats, from only a few seconds of audio. If robots could obtain this information just as rapidly, they would be more capable of musical interaction without needing long lead times to learn the music. The presence of robot ego noise, however, makes accurately analyzing music more difficult. In this paper, we focus on the task of learning musical beats, which are often identifiable to humans even in noisy environments such as bars. Learning beats would not only help robots to synchronize their responses to music, but could lead to learning other aspects of musical audio, such as other repeated events, timbrel aspects, and more. We introduce a novel algorithm utilizing stacked spectrograms, in which each column contains frequency bins from multiple instances in time, as well as Probabilistic Latent Component Analysis (PLCA) to learn beats in noisy audio. The stacked spectrograms are exploited to find time-varying spectral characteristics of acoustic components, and PLCA is used to learn and separate the components and find those containing beats. We demonstrate that this system can learn musical beats even when only provided with a few seconds of noisy audio.