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 present an application of an hybrid deep learning model to the task of audio artist identification.

Novelty:
+ The novelty of the paper comes from using an hybrid unsupervised learning approach by stacking Denoising Auto-Encoders (DA) and Restricted Boltzman Machines (RBM). 
= Another minor novelty is the application of deep learning to artist identification. However, deep learning has already been applied to similar tasks before such as genre recognition and automatic tag annotation.
- Unfortunately, I found that the major contributions of the paper are not exposed clearly enough in the introduction.

Quality of presentation:
- The quality of the presentation leaves to be desired. A more careful proofreading would have been required. There are several sentences with gramatical errors. Several verbs or adjectives are wrong. The writing style is also sometimes inadequate for a scientific paper (ex. 'we will review some fantastic work', 'we can build many outstanding networks'). The quality of the english is, in general, inadequate.
- The abstract does not present in a relevant and concise manner the essential points of the paper.
- Also, there is a bit of confusion in between the introduction and related work sections, as most of the introduction is also about related work.

Reference to previous work:
+ Previous related work coverage is good. Previous work in deep learning and its applications in MIR, as well as work in audio artist identification are well covered.
- In the beginning of section 5: 'It's known that Bach, Beethoven and Brahms, known as the three Bs, shared some style when they wrote their composition.' I find this claim, without any reference,  hard to understand.  Bach, Beethoven and Brahms are from 3 different musical eras. How are these 3 composers more related than the others?

Quality of the research.
- Although the idea of using a hybrid deep learning system might be new, no justification as to why such a system should work better is presented in the paper.
- In the experiments, the authors compare the hybrid model to pure models. However, the pure models all have less layers than the hybrid model. Why didn't the authors compare same-depth models? I feel it would have made a much stronger point.
- Although the authors describe in details the theory behind SDAs and DBNs, there is little to no detail about the hyper-parameters used in the actual model (number of hidden units, number of unsupervised epochs, regularization, etc.). How was the data corrupted for the DA? White Noise, or random flipped bits? How many steps in the CD? These details would be important to reproduce the results.
- In the beginning of section 3 and 6, the authors mention that they think their model will project the data into a semantic space which is very sparse. How is your model learning a sparse representation? Have you used sparseness constraints in your training? If so, there is no mention of it in the paper.