A proposal for file extension of back up files

There are several extensions used for backup files: .0, .1, .old, .bak, .backup, .bkp, .baka, etc. Based on Reddit votes, the most used is .bak; therefore, we should use the extension used by common sense.

Specification:

1. A backup file must be named with the extension .bak
2. If there are multiple backups, the next backup extension must be added with an integer, e.g., .bak1, .bak2, etc.
3. The above rules also apply for folder or directory, e.g., folderA.bak, dirB.bak1.

Finding all backup files

To find all backup files, simply use a shell command

$ ls *.bak*
a.bak  a.bak0  a.bak1  a.bak2

A paper was accepted at 2025 ICASSP Workshop!

Alhamdulillah, our paper has been accepted for a satellite workshop of ICASSP 2025. This paper discusses "Pathological Voice Detection From Sustained Vowels: Handcrafted vs. Self-supervised Learning". We proposed to examine pathological voice detection from sustained vowels (/a/, /i/, /u/) both using acoustic features and self-supervised learning (SSL) models. We also evaluated early fusion (feature concatenation) and decision-level ensemble learning for both types of features.

Our work is highly beneficial to society, as it will help to improve the performance of pathological voice detection. 

Several aspects were evaluated in this research project: evaluation of different vowels (which one leads to better results), evaluation of different acoustic and SSL features, and ensemble learning results.

Future work could tackle the limitations of the F1 score AUC by using more recent metrics like the Matthew correlation coefficient (MCC), which considers true and false positives and negatives.

Since the nature of the problem of detecting pathological voices can be classified as anomaly detection, future work can also be accomplished to observe the effectiveness of anomaly detection methods for pathological voice detection.

We extend our gratitude to AIST for their full support of our research, and to NEDO and JST for research funding.

Happy reading. We welcome your feedback. See you in Hyderabad!

URL for downloading the paper: (will be given after it is available or contact me to get the accepted version). 

Project repository: https://github.com/bagustris/svd-exploration

A paper was accepted at ICAIIC 2025!

Alhamdulillah, our paper has been accepted for the conference ICAIIC 2025. This paper discusses the importance of ensemble learning to improve speech classification accuracy. We proposed performance-weighting methods to evaluate with two variants: using weighted and unweighted accuracies.

Our work is highly beneficial to society, as it will help to improve the performance of speech classification. It may also be generalizable to other domains outside of the speech area.

Several aspects can still be developed further, such as incorporating other weighting methods, and implementation in other datasets as well as in other tasks. We invite readers to collaborate in addressing the unresolved questions above.

We extend our gratitude to AIST for their full support of our research, NEDO and JST for research funding.

Happy reading. We welcome your feedback. See you in Fukuoka!

URL for downloading the paper: (will be given after it is available or contact me to get the accepted version).