Learning Features of Music From Scratch
This paper introduces a new large-scale music dataset, MusicNet, to serve as a source of supervision and evaluation of machine learning methods for music research. MusicNet consists of hundreds of freely-licensed classical music recordings by 10 composers, written for 11 instruments, together with instrument/note annotations resulting in over 1 million temporal labels on 34 hours of chamber music performances under various studio and microphone conditions. The paper defines a multi-label classification task to predict notes in musical recordings, along with an evaluation protocol, and benchmarks several machine learning architectures for this task: i) learning from spectrogram features; ii) end-to-end learning with a neural net; iii) end-to-end learning with a convolutional neural net. These experiments show that end-to-end models trained for note prediction learn frequency selective filters as a low-level representation of audio.
This paper describes the creation of a corpus of freely-licensed classical music recordings along with corresponding MIDI-scores aligned to the audio. It also describes experiments in polyphonic transcription using various deep learning approaches, which show promising results. The paper is a little disorganised and somewhat contradictory in parts. For example, I find the first sentence in section 2 (MusicNet) would better be pushed one paragraph below so that the section be allowed to begin with a survey of the tools available to researchers in music. Also, the description for Table 3 should probably appear somewhere in the Methods section. Last example: the abstract/intro says the purpose is note prediction; later (4th paragraph of intro) there*s a claim that the focus is *learning low-level features of music....* I find this slightly disorienting. Although others (Uehara et al., 2016, for example) have discussed collection platforms and corpora, this work is interesting because of its size and the approach for generating features. I*m interested in what the authors will to do expand the offerings in the corpus, both in terms of volume and diversity.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning Features of Music From Scratch
This paper introduces a new large-scale music dataset, MusicNet, to serve as a source of supervision and evaluation of machine learning methods for music research. MusicNet consists of hundreds of freely-licensed classical music recordings by 10 composers, written for 11 instruments, together with instrument/note annotations resulting in over 1 million temporal labels on 34 hours of chamber music performances under various studio and microphone conditions. The paper defines a multi-label classification task to predict notes in musical recordings, along with an evaluation protocol, and benchmarks several machine learning architectures for this task: i) learning from spectrogram features; ii) end-to-end learning with a neural net; iii) end-to-end learning with a convolutional neural net. These experiments show that end-to-end models trained for note prediction learn frequency selective filters as a low-level representation of audio.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper describes the creation of a corpus of freely-licensed classical music recordings along with corresponding MIDI-scores aligned to the audio. It also describes experiments in polyphonic transcription using various deep learning approaches, which show promising results. The paper is a little disorganised and somewhat contradictory in parts. For example, I find the first sentence in section 2 (MusicNet) would better be pushed one paragraph below so that the section be allowed to begin with a survey of the tools available to researchers in music. Also, the description for Table 3 should probably appear somewhere in the Methods section. Last example: the abstract/intro says the purpose is note prediction; later (4th paragraph of intro) there*s a claim that the focus is *learning low-level features of music....* I find this slightly disorienting. Although others (Uehara et al., 2016, for example) have discussed collection platforms and corpora, this work is interesting because of its size and the approach for generating features. I*m interested in what the authors will to do expand the offerings in the corpus, both in terms of volume and diversity.
assistant
This paper describes the creation of a corpus of freely-licensed classical music recordings along with corresponding MIDI-scores aligned to the audio. It also describes experiments in polyphonic transcription using various deep learning approaches, which show promising results. The paper is a little disorganised and somewhat contradictory in parts. For example, I find the first sentence in section 2 (MusicNet) would better be pushed one paragraph below so that the section be allowed to begin with a survey of the tools available to researchers in music. Also, the description for Table 3 should probably appear somewhere in the Methods section. Last example: the abstract/intro says the purpose is note prediction; later (4th paragraph of intro) there*s a claim that the focus is *learning low-level features of music....* I find this slightly disorienting. Although others (Uehara et al., 2016, for example) have discussed collection platforms and corpora, this work is interesting because of its size and the approach for generating features. I*m interested in what the authors will to do expand the offerings in the corpus, both in terms of volume and diversity.