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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

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Online Continual Learning in Acoustic Scene Classification: An Empirical Study.

Donghee Ha^1,2, Mooseop Kim^1,2, Chi Yoon Jeong^1,2

¹Artificial Intelligence Research Laboratory, Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Daejeon 34129, Republic of Korea.

Sensors (Basel, Switzerland)

|August 12, 2023

Summary

This study evaluates ten continual learning (CL) methods for acoustic scene classification (ASC). SCR and GDumb performed best in different scenarios, offering guidance for future CL research in sound analysis.

Keywords:

acoustic scene classification catastrophic forgetting continual learning intransigence online learning

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Area of Science:

Artificial Intelligence
Machine Learning
Signal Processing

Background:

Deep learning advances acoustic scene classification (ASC) accuracy for sound events.
Continual learning (CL) methods for adapting models to changing tasks in ASC are under-explored.
Systematic performance analysis of existing CL methods is needed to guide future research.

Purpose of the Study:

To systematically analyze and compare the performance of ten recent CL methods for ASC.
To provide guidelines for selecting appropriate CL methods based on different learning scenarios and memory buffer sizes.
To evaluate CL methods in realistic online class-incremental (OCI) and online domain-incremental (ODI) settings.

Main Methods:

Evaluated ten CL methods: two regularization-based and eight replay-based.
Defined and utilized OCI and ODI scenarios across three public sound datasets.
Assessed performance based on average accuracy, average forgetting, and training time.

Main Results:

In OCI scenarios, iCaRL and SCR excelled with small buffers; GDumb was best with large buffers.
In ODI scenarios, SCR with supervised contrastive learning consistently outperformed others, irrespective of buffer size.
Replay-based methods showed consistent training times and performance gains with larger memory buffers.

Conclusions:

GDumb and SCR are recommended as primary considerations for continual learning in ASC.
Method selection should depend on specific OCI or ODI scenarios and available memory buffer size.
Further research can build upon these findings for more robust and adaptive ASC systems.