SearcharxivSearch

arXiv · 1006.0866

A Study on the Interactive "HOPSCOTCH" Game for the Children Using Computer Music Techniques

Abstract

"Hopscotch" is a world-wide game for children to play since the times in the ancient Roman Empire and China. Here we present a study mainly focused on the research and discussion of the application on the children's well-know edutainment via the physical interactive design to provide the sensing of the times for the conventional hopscotch, which is a new type of experiment for the technology aided edutainment. The innovated hopscotch music game involves the sound samples of various animals and the characters of cartoon, and the algorithmic composition via the development of the music technology based interactive game, to gradually make the children perceive the world of digits, sound, and music. It can guide the growing children's personality and character from disorder into clarity. Furthermore, the traditional teaching materials can be improved via the implementation of the electrical sensing devices, electrical I/O module, and the computer music program Max/MSP, to integrate the interactive computer music with the interactive and immersive soundscapes composition, and the teaching tool with educational gaming is completely accomplished eventually.

Explore related subjects

Keep this discovery

BibTeXRIS

Shing-Kwei Tzeng, Chih-Fang Huang. 2010-06-04. A Study on the Interactive "HOPSCOTCH" Game for the Children Using Computer Music Techniques. https://doi.org/10.5121/ijma.2010.2203

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Xiaomi-CocktailASR-1 Technical Report

Recently, large language model (LLM) based ASR models have achieved significant progress, yet they generally lack support for multi-speaker scenarios, where the cocktail party problem remains a critical bottleneck for further advancing ASR. Existing TS-ASR methods, including end-to-end architectures with speaker embeddings and latest LLM-based explorations suffer from degraded single-speaker performance and the inability to reject when the target speaker is absent. In this paper, we propose Xiaomi-CocktailASR-1, an LLM-based end-to-end TS-ASR architecture. By utilizing reference speech as voiceprint prompts, it directly transcribes the target speaker's speech without requiring speech separation. Xiaomi-CocktailASR-1 maintains competitive performance in single-speaker scenarios, comparable to mainstream ASR models. It also features a negative sample rejection capability, outputting empty text when the target speaker is absent from the mixed speech. Additionally, Xiaomi-CocktailASR-1 supports a Chain-of-Thought (CoT) reasoning mode to provide explicit reasoning steps. Extensive experiments on various synthetic and real-world multispeaker benchmarks demonstrate that Xiaomi-CocktailASR-1 achieves state-of-the-art performance, effectively addressing the cocktail party problem through a unified architecture that balances multispeaker and single-speaker recognition accuracy, along with rejection capability.

cs.SD

EConv-TasNet: Efficient Conv-TasNet for Effective Speech Separation

Conv-TasNet has served as a strong baseline for time-domain speech separation, and many studies have extended it with advanced architectures such as dual-path networks, U-Nets, and attention mechanisms. However, these methods often introduce high computational cost and complexity, limiting their deployment in resource-constrained scenarios. To address this issue, we propose eConv-TasNet, an efficient variant of Conv-TasNet that improves both effectiveness and efficiency without relying on resource-intensive modules. The proposed model consists of a group-wise early-splitting (GES) module and a multi-group feature aggregation (MGFA) module. GES generates discriminative speaker embeddings at intermediate stages, while MGFA progressively aggregates these group-level representations for refined mask estimation. Experimental results show that eConv-TasNet reduces model size by 22.4%, accelerates inference by 18.9%, and improves SI-SNRi by 14.0%-28.0% across three public benchmarks. Moreover, it achieves competitive performance compared with state-of-the-art methods while requiring significantly fewer parameters and lower inference cost. These results demonstrate a favorable efficiency-effectiveness trade-off for edge deployment.

cs.SD