On The Impact of Normalization Strategies in Unsupervised Adversarial Domain Adaptation for Acoustic Scene Classification

Olvera, Michel; Gasso, Gilles

doi:10.1109/icassp43922.2022.9747540

Cited by 4 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In these cases, a classifier may mistakenly learn to use non-species-specific features for classification during training. Inclusion of additional strategies, such as domain adaptation [ 52 ], in the pipeline may be needed to improve the robustness of these methods across locations and sensor types. Because of the inherent heterogeneity of the beaked whale species’ occurrence, the datasets will likely show a long-tailed distribution, with some classes having many more samples than others.…”

Section: Discussionmentioning

confidence: 99%

Machine learning with taxonomic family delimitation aids in the classification of ephemeral beaked whale events in passive acoustic monitoring

Solsona-Berga,

DeAngelis,

Cholewiak

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Passive acoustic monitoring is an essential tool for studying beaked whale populations. This approach can monitor elusive and pelagic species, but the volume of data it generates has overwhelmed researchers’ ability to quantify species occurrence for effective conservation and management efforts. Automation of data processing is crucial, and machine learning algorithms can rapidly identify species using their sounds. Beaked whale acoustic events, often infrequent and ephemeral, can be missed when co-occurring with signals of more abundant, and acoustically active species that dominate acoustic recordings. Prior efforts on large-scale classification of beaked whale signals with deep neural networks (DNNs) have approached the class as one of many classes, including other odontocete species and anthropogenic signals. That approach tends to miss ephemeral events in favor of more common and dominant classes. Here, we describe a DNN method for improved classification of beaked whale species using an extensive dataset from the western North Atlantic. We demonstrate that by training a DNN to focus on the taxonomic family of beaked whales, ephemeral events were correctly and efficiently identified to species, even with few echolocation clicks. By retrieving ephemeral events, this method can support improved estimation of beaked whale occurrence in regions of high odontocete acoustic activity.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine learning with taxonomic family delimitation aids in the classification of ephemeral beaked whale events in passive acoustic monitoring

Solsona-Berga,

DeAngelis,

Cholewiak

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…To mitigate the performance drop caused by domain mismatch, various domain adaptation techniques have been explored, particularly in transfer learning settings [15]. In the selfsupervised context, adversarial approaches have been applied during the unsupervised pretraining and tested on speech recognition [16,17], emotion recognition [18] and speaker recognition [19].…”

Section: Introductionmentioning

confidence: 99%

Automatic Data Augmentation Selection and Parametrization in Contrastive Self-Supervised Speech Representation Learning

Zaiem¹,

Parcollet²,

Essid³

2022

Interspeech 2022

View full text Add to dashboard Cite

Self-Supervised Learning (SSL) has allowed leveraging large amounts of unlabeled speech data to improve the performance of speech recognition models even with small annotated datasets. Despite this, speech SSL representations may fail while facing an acoustic mismatch between the pretraining and target datasets. To address this issue, we propose a novel supervised domain adaptation method, designed for cases exhibiting such a mismatch in acoustic domains. It consists in applying properly calibrated data augmentations on a large clean dataset, bringing it closer to the target domain, and using it as part of an initial fine-tuning stage. Augmentations are automatically selected through the minimization of a conditional-dependence estimator, based on the target dataset. The approach is validated during an oracle experiment with controlled distortions and on two amateur-collected low-resource domains, reaching better performances compared to the baselines in both cases.

show abstract

A Supervised Domain Adaptive Method for Multi Device Acoustic Scene Classification

Liu,

Wang,

Luo

et al. 2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

On The Impact of Normalization Strategies in Unsupervised Adversarial Domain Adaptation for Acoustic Scene Classification

Cited by 4 publications

References 19 publications

Machine learning with taxonomic family delimitation aids in the classification of ephemeral beaked whale events in passive acoustic monitoring

Machine learning with taxonomic family delimitation aids in the classification of ephemeral beaked whale events in passive acoustic monitoring

Automatic Data Augmentation Selection and Parametrization in Contrastive Self-Supervised Speech Representation Learning

A Supervised Domain Adaptive Method for Multi Device Acoustic Scene Classification

Contact Info

Product

Resources

About