Audio Artist Identification by Deep Neural Network
Authors: 胡振, Kun Fu, Changshui Zhang
ICLR 2013

Abstract: Since officially began in 2005, the annual Music Information Retrieval Evaluation eXchange (MIREX) has made great contributions to the Music Information Retrieval (MIR) research. By defining some important tasks and providing a meaningful comparison system, the International Music Information Retrieval Systems Evaluation Laboratory (IMIRSEL), organizer of the MIREX, drives researchers in the MIR field to develop more advanced system to fulfill the tasks. One of the important tasks is the Audio Artist Identification task, or the AAI task. We implemented a Deep Belief Network (DBN) to identify the artist by audio signal. As a matter of copyright, IMIRSEL didn't publish there data set and we had to construct our own. In our data set we got an accuracy of 69.87% without carefully choosing parameters while the best result reported on MIREX is 69.70%. We think our method is promising and we want to discuss with others.


Peer review:
[Review]:
This paper describes work to collect a new dataset with music from 11 classical composers for the task of audio composer identification (although the title, abstract, and introduction use the phrase 'audio artist identification' which is a different task).  It describes experiments training a few different deep neural networks to perform this classification task.

The paper is not very novel.  It describes existing deep architectures applied to a new version of an existing dataset for an existing task.

The quality of the paper is not very high.  The comparisons of the models were not systematic and because it is a new dataset, they cannot be compared directly to results on other datasets of existing models.  There are very few specifics given about the models used (layer sizes, cost functions, input feature types, specific input features).

The use of mel frequency spectrum seems dubious for this task.  What distinguishes classical works from different composers is generally harmonic and melodic content, which mel frequency spectrum ignores almost entirely.

Few details are given about the make-up of the new dataset.  Are these orchestral pieces, chamber pieces, concertos, piano pieces, etc?  How many clips came from each piece?  How many clips came from each movement?  The use of clips from different movements of the same piece in the training and test sets might account for the increase in accuracy scores relative to previous MIREX results.  Movements from the same piece generally share many characteristics like recording conditions, production, instrumentation, and timbre, which are the main characteristics captured by mel frequency spectrum.  They also generally share harmonic and melodic content.

And finally, the 'Three B's' that the authors refer to, Bach, Beethoven, and Brahms, are very different composers from different musical eras.  Their works should not be easily confused with each other, and so the fact that the proposed algorithm does confuse them is concerning.  Potentially it indicates the weakness of the mel spectrum for performing this task.

Pros:
- Literary presentation of the paper is high (although there are a number of strange word substitutions)
- Decent summary of existing work
- New dataset might be useful, if it is made public, although it is pretty small

Cons:
- Little novelty
- Un-systematic comparisons of systems
- Features don't make much sense
- Few details on actual systems compared and on the dataset
- Few generalizable conclusions