Publications (15)
Very Deep Convolutional Neural Networks for Raw Waveforms
Wei Dai, Chia Dai, Shuhui Qu +2
Learning acoustic models directly from the raw waveform data with minimal processing is challenging. Current waveform-based models have generally used very few (~2) convolutional l…
A Light-Weight Multimodal Framework for Improved Environmental Audio Tagging
Juncheng Li, Yun Wang, Joseph Szurley +2
The lack of strong labels has severely limited the state-of-the-art fully supervised audio tagging systems to be scaled to larger dataset. Meanwhile, audio-visual learning models b…
Eventness: Object Detection on Spectrograms for Temporal Localization of Audio Events
Phuong Pham, Juncheng Li, Joseph Szurley +1
In this paper, we introduce the concept of Eventness for audio event detection, which can, in part, be thought of as an analogue to Objectness from computer vision. The key observa…
Understanding Cross-sectional Dependence in Panel Data
Gopal K Basak, Samarjit Das
We provide various norm-based definitions of different types of cross-sectional dependence and the relations between them. These definitions facilitate to comprehend and to charact…
Learning Filter Banks Using Deep Learning For Acoustic Signals
Shuhui Qu, Juncheng Li, Wei Dai +1
Designing appropriate features for acoustic event recognition tasks is an active field of research. Expressive features should both improve the performance of the tasks and also be…
Linear Regression: Inference Based on Cluster Estimates
Subhodeep Dey, Gopal K. Basak, Samarjit Das
This article proposes a novel estimator for regression coefficients in clustered data that explicitly accounts for within-cluster dependence. We study the asymptotic properties of…
Identifying Actions for Sound Event Classification
Benjamin Elizalde, Radu Revutchi, Samarjit Das +3
In Psychology, actions are paramount for humans to identify sound events. In Machine Learning (ML), action recognition achieves high accuracy; however, it has not been asked whethe…
Statistical inference using debiased group graphical lasso for multiple sparse precision matrices
Sayan Ranjan Bhowal, Debashis Paul, Gopal K Basak +1
Debiasing group graphical lasso estimates enables statistical inference when multiple Gaussian graphical models share a common sparsity pattern. We analyze the estimation propertie…
Multiple Instance Deep Learning for Weakly Supervised Small-Footprint Audio Event Detection
Shao-Yen Tseng, Juncheng Li, Yun Wang +3
State-of-the-art audio event detection (AED) systems rely on supervised learning using strongly labeled data. However, this dependence severely limits scalability to large-scale da…
Understanding Audio Pattern Using Convolutional Neural Network From Raw Waveforms
Shuhui Qu, Juncheng Li, Wei Dai +1
One key step in audio signal processing is to transform the raw signal into representations that are efficient for encoding the original information. Traditionally, people transfor…
A Comparison of deep learning methods for environmental sound
Juncheng Li, Wei Dai, Florian Metze +2
Environmental sound detection is a challenging application of machine learning because of the noisy nature of the signal, and the small amount of (labeled) data that is typically a…
Estimation of multiple precision matrices under shared support with heterogeneous edge strengths
Sayan Ranjan Bhowal, Debashis Paul, Gopal K Basak +1
Estimating multiple precision matrices in high-dimension presents significant challenges, particularly when distinct datasets share a common conditional dependency structure but ex…
Cross Sectional Regression with Cluster Dependence: Inference based on Averaging
Subhodeep Dey, Gopal K. Basak, Samarjit Das
We re-investigate the asymptotic properties of the traditional OLS (pooled) estimator, , in the context of cluster dependence. The present study considers various scenar…
Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection
Bingqing Chen, Luca Bondi, Samarjit Das
Anomaly detection has many important applications, such as monitoring industrial equipment. Despite recent advances in anomaly detection with deep-learning methods, it is unclear h…
Relative Efficiency of Higher Normed Estimators Over the Least Squares Estimator
Gopal K Basak, Samarjit Das, Arijit De +1
In this article, we study the performance of the estimator that minimizes order loss function (for against the estimators which minimizes the orde…