Motif Mining and Unsupervised Representation Learning for BirdCLEF 2022

06/08/2022
by   Anthony Miyaguchi, et al.
0

We build a classification model for the BirdCLEF 2022 challenge using unsupervised methods. We implement an unsupervised representation of the training dataset using a triplet loss on spectrogram representation of audio motifs. Our best model performs with a score of 0.48 on the public leaderboard.

READ FULL TEXT
research
01/12/2021

Learning Efficient Representations for Keyword Spotting with Triplet Loss

In the past few years, triplet loss-based metric embeddings have become ...
research
05/02/2018

OMG Emotion Challenge - ExCouple Team

The proposed model is only for the audio module. All videos in the OMG E...
research
09/12/2023

Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language Model

Sentence Representation Learning (SRL) is a fundamental task in Natural ...
research
12/03/2019

Large scale representation learning from triplet comparisons

In this paper, we discuss the fundamental problem of representation lear...
research
02/14/2022

On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective"

Model identifiability is a desirable property in the context of unsuperv...
research
06/07/2021

Unsupervised Representation Disentanglement of Text: An Evaluation on Synthetic Datasets

To highlight the challenges of achieving representation disentanglement ...
research
01/03/2020

Deep Unsupervised Common Representation Learning for LiDAR and Camera Data using Double Siamese Networks

Domain gaps of sensor modalities pose a challenge for the design of auto...

Please sign up or login with your details

Forgot password? Click here to reset