padmanrajan Goto Github PK

adppres

ADP seed-grant presentation

all_conv_net_is2018

# All Conv-Net for Bird Activity Detection Significance of Learned Pooling Bird activity detection (BAD) deals with the task of predicting the presence or absence of bird vocalizations in a given audio recording. In this work, we propose an all-convolutional neural network (all-conv net) for bird activity detection. All the layers of this network including pooling and dense layers are implemented using convolution operations. The pooling operation implemented by convolution is termed as learned pooling. This learned pooling takes into account the inter featuremap correlations which are ignored in traditional max-pooling. This helps in learning a pooling function which aggregates the complementary information in various feature maps, leading to better bird activity detection. Experimental observations confirm this hypothesis. The performance of the proposed all-conv net is evaluated on the BAD Challenge 2017 dataset. The proposed all-conv net achieves state-of-art performance with a simple architecture and does not employ any data pre-processing or data augmentation techniques. This work is accepted for publication in INTERSPEECH 2018. feature_extract.py is used to extract melspectrogram features. all_convnet_BAD.py is the all convolutional model architecture all_convnet_BAD_maxpool_variant.py is the maxpool variant of the all_conv_net model get_activation_map.py is used to analyse any layer activations in the model To know more about BAD 2017 challenge and to download data, follow this link http://machine-listening.eecs.qmul.ac.uk/bird-audio-detection-challenge/

all_conv_pytorch

# Pytorch: All Conv-Net for Bird Activity Detection Significance of Learned Pooling Bird activity detection (BAD)

attentiondrivenprojections

bad_psk

Rapid Bird Activity Detection Using Probabilistic Sequence Kernels

bird_seg_icspcs_2016

Model-based unsupervised segmentation of birdcalls from field recordings

birdsidentifier

This is an Android App developed by Piyush Goyal

ccse_icassp

COMPRESSED CONVEX SPECTRAL EMBEDDING FOR BIRD SPECIES CLASSIFICATION

cla_mlsp2018

Convex likelihood alignments

daa_imk

dataadder

This is an application developed in LAP 2019 by one of the student teams.

deep-convex-representation_is2018

DCR_deep_archetypal_analysis

demorepocs308_sept20

Demo repo for CS308 in Sept 2020

enesmeble_soundnet

feature-switching

Paper for feature-switching

ic152_2021_grading

Pandas code for grading IC152 for BTech 2021.

jarvis

Personal Assistant for Linux and macOS

learningtoseparate

Learning to Separate: Soundscape Classification using Foreground and Background

mlsp2017_bird_seg

mtechshortlisting

Code for MTech shortlisting form files generated by OAS.

multiviewembeddingsforsoundscapeclassification

ncc2016

paper for ncc 2016

pruning-1d-cnn

seedgrant

Seed grant

segment_is2016