Instruments

Assessment of musical representations using a music information retrieval technique

November 1, 2021 by Valentina Hernández-López, Néstor Darío Duque-Méndez, and Mauricio Orozco-Alzate

While learning to play a musical instrument, a student does not always have the support of a specialized teacher to guide and assess his or her daily practice. The lack of guidance and untimely feedback can be demotivating for successful study. Lerch et al. (2019), in a recent study, suggest that performance assessment is an important aspect in music pedagogy: “Students rely on … [Read more...] about Assessment of musical representations using a music information retrieval technique

Alexa-based voice assistant for smart home applications

July 1, 2021 by Clara Jiménez, Edgar Saavedra, Guillermo del Campo, and Asunción Santamaría

Today, the Internet of Things (IoT) is becoming an essential player in creating a new smart era. The communication through the Internet to IoT devices enables new applications in multiple environments, including smart buildings and cities. The IoT market is projected to grow to 75.4 billion connected devices by 2020. Within the IoT ecosystem, smart home technology is also … [Read more...] about Alexa-based voice assistant for smart home applications

Privacy-preserving distributed learning: Techniques, applications, and future challenges

The rapid increase in data generated by connected devices has created a pressing need for privacy-preserving techniques in distributed learning. This article examines methods that enable collaborative machine learning (ML) while maintaining data security and user privacy. Key approaches such as federated learning (FL), differential privacy (DP), secure multiparty computation (SMPC), and homomorphic encryption (HE) are analyzed for their unique capabilities and various applications. FL facilitates model training across decentralized data sources, ensuring data remain local, while DP mitigates privacy risks by adding controlled noise. SMPC and HE support secure computations on encrypted data, maintaining confidentiality during processing. Despite their effectiveness, these techniques face challenges related to computational complexity, scalability, and regulatory compliance. The article reviews current advancements, practical implementations, and future directions, emphasizing the need for optimized, accessible solutions to enhance data security in distributed systems.

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