Blog(2)

• TFPSNet Time-Frequency Domain Path Scanning Network for Speech Separation

  Deep learning techniques have accomplished a big step forward on speech separation task. The current leading methods are based on the time-domain audio separation network (TasNet) [1]. TasNet uses a learnable encoder and decoder to replace the fixed T-F domain transformation. It takes waveform inputs and directly reconstructs sources, and computes time-domain loss with utterance-level permutation invariant training (uPIT). Several approaches are proposed based on TasNet framework, such as the Conv-TasNet [2] , the dual-path recurrent neural network (DPRNN) [3], the dual-path Transformer network (DPTNet) [4], RNN-free transformer-based neural network (SepFormer) [5] , a self-attentive network with a novel sandglass-shape, namely Sandglasset [6].

• FedMargin - Federated Learning via Attentive Margin of Semantic Feature Representations

  Let’s play a simple game. Open the photo gallery on your phone and briefly scroll your images, do you see some patterns and recognize the objects you like on the images?

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Publications(352)

• Tail Call Optimization Tailored for Native Stack Utilization in JavaScript Runtimes

  AuthorHyukwoo Park, Seonghyun Kim

  PublishedIEEE Access

  Date2024-08-12

• EDgE: Enhancing Predictability in Decentralized Network Slices through Federated Learning – A Multi layered Ecosystem

  AuthorGanesh Chandrasekaran; Abhinay Kumar; Karthikeyan Subramaniam; N Sunil; Rajesh Challa

  PublishedIEEE Access

  Date2024-07-17

• Contamination by Idle RIS in Cellular Systems - Impacts and Solutions

  AuthorMohammed Saquib Noorulhuda Khan, Ashok Kumar Reddy Chavva

  PublishedIEEE Wireless Communications and Networking Conference (WCNC)

  Date2024-07-03

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News(42)

• Driving Technological Excellence: SRI-B Innovator Wins IEEE's Top Technologist Award

  Samsung Research & Development Institute, Bangalore (SRI-B), continues to set benchmarks in innovation and engineering as Dr. Madhan Raj, a distinguished technologist, has been named the ‘Technologist of the Year’ by the prestigious Institute of Electrical and Electronics Engineers (IEEE) India Council.

• Five Samsung Electronics Researchers Named IEEE Fellows  

  Samsung Electronics today announced that five of its researchers from around the world have been selected as 2025 Fellows of the Institute of Electrical and Electronics Engineers1 (IEEE), one of the world’s most prestigious engineering organizations.

• Five Samsung Electronics Researchers Named IEEE Fellows

  Samsung Electronics today announced that five of its researchers from around the world have been selected as 2025 Fellows of the Institute of Electrical and Electronics Engineers1 (IEEE), one of the world’s most prestigious engineering organizations.

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