Under-Explored Problems in Academia¶
Your choices as a researcher are affected by the circumstance such as
\ |
Has more freedom in |
Has Bias towards |
|---|---|---|
Academia |
Choosing the topics |
Publishable topics |
Industry |
Using resources (time, budget, work force) |
Profitable topics |
As a result, we’ll see each entity uses [A] to specialize in [B]. And that’s great! But, for the same reason, some topics are getting little attention in academia.
What makes a topic difficult to work on in academia?¶
When it feels like it’s solved → You can’t write a paper about it anymore!
When it’s hard to create a dataset → In this data-driven era, it’s a deal-breaker.
When the problem is too new / there’s no dataset for it → No way for sure.
When it’s difficult to evaluate → Don’t feed Reviewer 2 a reason for rejection!
Let’s talk about research topics¶
Disclaimer - This section is meant to be subjective. Also, as the content is based on the diagnosis of the current research field, it will expire as time goes by.
Speech/music classification¶
Although
It seems easy
Many methods have achieved 100% accuracy in Gtzan speech/music dataset [Tza99],
It is an interesting problem because
The model is needed anyway and there’s no reliable public model since Gtzan speech/music dataset [Tza99] is pretty small
The problem can be defined further such as:
Clip-level decision → short segment-level decision (say, 1 second)
More than binary decision - {100% Music – many levels in between – 100% speech} + {something neither music or speech} (e.g., [MelendezCatalanMGomez19], [HWW+21])
Language classification¶
Although
We were not doing it (nearly at all) because there was no public dataset
It is an interesting problem because
Mood recognition¶
Although
It has lost popularity for these reasons:
Tagging tasks sort of overshadowed it
Hard to get large-scale data // while we have to write deep learning papers!
Hard to evaluate (fundamentally, completely subjective)
Maybe a lot about lyrics, which are also hard to get.
It is an interesting problem because
Year/decade/era¶
Although
No one does it explicitly
Metadata is supposed to cover this pretty well
MSD includes it and it works pretty okay [BMEWL11]
It is an interesting problem because
And yes, there is demand! Metadata is NOT always there or correct
Relevant to user’s musical preference
Audio codec quality (mp3, wav, etc)¶
Although
Music services are supposed to always have high-quality audio
It is an interesting problem because
There are many fake CD-quality/fake HD audio files
Indie music/Directly publishing + sample-based music producing = Increase of audio quality issue
Hierarchical Classification¶
Although
There are little datasets that have hierarchical taxonomies
It is an interesting problem because
We can do a better job by learning the knowledge in the hierarchy
The users of your model may want it! Even if they did not explicitly want a label hierarchy, it might make more sense to have one based on the labels in demand.