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]:
A brief summary of the paper’s contributions. In the context of prior work:
This paper builds a hybrid model based on Deep Belief Network (DBN) and Stacked Denoising Autoencoder (SDA) and applies it to Audio Artist Identification (AAI) task. Specifically, the proposed model is constructed with a two-layer SDA in the lower layers, a two-layer DBN in the middle, and a logistic regression classification layer on the top. The proposed model seems to achieve good classification performance.


An assessment of novelty and quality:
The paper proposes a hybrid deep network by stacking denoising autoencoders and RBMs.
Although this may be a new way of building a deep network, it seems to be a minor modification of the standard methods. Therefore, the novelty seems to be limited.

More importantly, motivation or justification about hybrid architecture is not clearly presented. Without a clear motivation or justification, this method doesn’t seem to be technically interesting. To make a fair comparison to other baseline methods, the SDA2-DBN2 should be compared to DBN4 or SDA4, but there are no such comparisons.

Although the classification performance by the proposed method is good, the results are not directly comparable to other work in the literature. It will be helpful to apply some widely used methods in authors’ data set as additional control experiments;

The paper isn’t well polished. There are many awkward sentences and grammatical errors.


Other comments:
Figure 2 is anecdotal and is not convincing enough.

Authors use some non-standard terminology. For example, what does “MAP paradigm” mean?

In Table 3, rows corresponding to “#DA layers”, “#RBM layers”, “#logistic layers” are unnecessary.



A list of pros and cons (reasons to accept/reject)

pros:
+ Literature review seems fine.
+ good (but incomplete) empirical classification results

cons:
- lack of clear motivation or justification of the hybrid method; lack of proper control experiments
- the results are not comparable to other published work
- unpolished writing (lots of awkward sentences and grammatical errors).