50 citations · 50 across the 5 of their papers we have counts for
5 papers
Device Invariance using Domain Adaptation on Acoustic Scene Classification
Abhishek dileep, Shubham Sharma, Padmanabhan Rajan
This paper explores the effectiveness of domain adaptation techniques when using convolutional neural network (CNN)-based and transformer-based feature representations for acoustic…
Health Monitoring of Industrial machines using Scene-Aware Threshold Selection
Arshdeep Singh, Raju Arvind, Padmanabhan Rajan
This paper presents an autoencoder based unsupervised approach to identify anomaly in an industrial machine using sounds produced by the machine. The proposed framework is trained…
Multiscale CNN based Deep Metric Learning for Bioacoustic Classification: Overcoming Training Data Scarcity Using Dynamic Triplet Loss
Anshul Thakur, Daksh Thapar, Padmanabhan Rajan +1
This paper proposes multiscale convolutional neural network (CNN)-based deep metric learning for bioacoustic classification, under low training data conditions. The proposed CNN is…
Directional Embedding Based Semi-supervised Framework For Bird Vocalization Segmentation
Anshul Thakur, Padmanabhan Rajan
This paper proposes a data-efficient, semi-supervised, two-pass framework for segmenting bird vocalizations. The framework utilizes a binary classification model to categorize fram…
Conv-codes: Audio Hashing For Bird Species Classification
Anshul Thakur, Pulkit Sharma, Vinayak Abrol +1
In this work, we propose a supervised, convex representation based audio hashing framework for bird species classification. The proposed framework utilizes archetypal analysis, a m…