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Rakshith Sharma Srinivasa

11 accepted papers

2025

Better Exploiting Spatial Separability in Multichannel Speech Enhancement with an Align-and-Filter Network

Ching Hua Lee, Chouchang Yang, Yashas Malur Saidutta, Rakshith Sharma Srinivasa, Yilin Shen, Hongxia Jin
ICASSP 2025accepted

Multichannel speech enhancement (SE) techniques combine multiple microphone signals to extract clean speech from noisy mixtures based on spatial filtering. As the target speech may come from arbitrary, unknown directions, current deep learning-based SE systems could suffer from performance bottlenec…

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2025

MIB: Mixed Information Bottleneck for Out-of-Distribution Keyword Spotting

Yashas Malur Saidutta, Rakshith Sharma Srinivasa, Jaejin Cho, Ching Hua Lee, Chouchang Yang, Yilin Shen +1
ICASSP 2025accepted

Deep Keyword Spotting (KWS) systems continuously process audio streams to detect keywords. However, performance of deep neural networks degrade when the input data diverges from the training data; referred to as Out-of-Distribution (OOD) data problem. In this paper, we show performance degradation o…

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2025

RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior

Ching-Hua Lee, Chouchang Yang, Jaejin Cho, Yashas Malur Saidutta, Rakshith Sharma Srinivasa, Yilin Shen +1
ICML 2025poster

Denoising diffusion probabilistic models (DDPMs) can be utilized to recover a clean signal from its degraded observation(s) by conditioning the model on the degraded signal. The degraded signals are themselves contaminated versions of the clean signals; due to this correlation, they may encompass ce…

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2024

CIFD: Controlled Information Flow to Enhance Knowledge Distillation

Yashas Malur Saidutta, Rakshith Sharma Srinivasa, Jaejin Cho, Ching-Hua Lee, Chouchang Yang, Yilin Shen +1
NeurIPS 2024poster

Knowledge Distillation is the mechanism by which the insights gained from a larger teacher model are transferred to a smaller student model. However, the transfer suffers when the teacher model is significantly larger than the student. To overcome this, prior works have proposed training intermediat…

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2024

End-To-End Personalized Cuff-Less Blood Pressure Monitoring Using ECG and PPG Signals

Suhas BN, Rakshith Sharma Srinivasa, Yashas Malur Saidutta, Jaejin Cho, Ching Hua Lee, Chouchang Yang +2
ICASSP 2024accepted

Cuffless blood pressure (BP) monitoring offers the potential for continuous, non-invasive healthcare but has been limited in adoption by existing models relying on handcrafted features from ECG and PPG signals. To overcome this, researchers have looked to deep learning. Along these lines, in this pa…

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2024

Leveraging Self-Supervised Speech Representations for Domain Adaptation in Speech Enhancement

Ching Hua Lee, Chouchang Yang, Rakshith Sharma Srinivasa, Yashas Malur Saidutta, Jaejin Cho, Yilin Shen +1
ICASSP 2024accepted

Deep learning based speech enhancement (SE) approaches could suffer from performance degradation due to mismatch between training and testing environments. A realistic situation is that an SE model trained on parallel noisy-clean utterances from one environment, the source domain, may fail to perfor…

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2024

Zero-Shot Intent Classification Using a Semantic Similarity Aware Contrastive Loss and Large Language Model

Jaejin Cho, Rakshith Sharma Srinivasa, Ching Hua Lee, Yashas Malur Saidutta, Chouchang Yang, Yilin Shen +1
ICASSP 2024accepted

Zero-shot systems can reduce the cost of collecting data and training in a new domain since they can work directly with the test data without further training. In this paper, we build zero-shot systems for intent classification, based on Semantic Similarity-aware Contrastive Loss (SSCL) that address…

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2023

CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss

Rakshith Sharma Srinivasa, Jaejin Cho, Chouchang Yang, Yashas Malur Saidutta, Ching-Hua Lee, Yilin Shen +1
NeurIPS 2023poster

This paper considers contrastive training for cross-modal 0-shot transfer wherein a pre-trained model in one modality is used for representation learning in another domain using pairwise data. The learnt models in the latter domain can then be used for a diverse set of tasks in a 0-shot way, similar…

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2023

To Wake-Up or Not to Wake-Up: Reducing Keyword False Alarm by Successive Refinement

Yashas Malur Saidutta, Rakshith Sharma Srinivasa, Ching Hua Lee, Chouchang Yang, Yilin Shen, Hongxia Jin
ICASSP 2023accepted

Keyword spotting systems continuously process audio streams to detect keywords. One of the most challenging tasks in designing such systems is to reduce False Alarm (FA) which happens when the system falsely registers a keyword despite the keyword not being uttered. In this paper, we propose a simpl…

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2019

Decentralized sketching of low rank matrices

Rakshith Sharma Srinivasa, Kiryung Lee, Marius Junge, Justin Romberg
NeurIPS 2019poster

We address a low-rank matrix recovery problem where each column of a rank-r matrix X of size (d1,d2) is compressed beyond the point of recovery to size L with L << d1. Leveraging the joint structure between the columns, we propose a method to recover the matrix to within an epsilon relative error in…

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