Reviews and Comments on Paper 519
Paper information
| Paper #519: Edgar Chavez and Antonio Camarena-Ibarrola. On Musical Performances Identification, Entropy and String Matching |
| Abstract: In this paper we address the problem of matching musical
renditions of the same piece of music also known as
performances. We use an entropy based Audio-Fingerprint
delivering a framed, small footprint AFP which reduces the problem
to a string matching problem. The Entropy AFP has very low
resolution (750 ms per symbol), making it suitable for flexible
string matching.
We show experimental results using dynamic time warping (DTW),
Levenshtein or edit distance and the Longest Common
Subsequence (LCS) distance. We are able to correctly (100\%)
identify different renditions of masterpieces as well as pop music
in less than a second per comparison.
The three approaches are 100\% effective, but LCS and Levenshtein
can be computed online, making them suitable for monitoring
applications (unlike DTW), and since they are distances a metric
index could be use to speed up the recognition process. (file) |
Summary of received reviews and comments
Reviews superseded by other reviews are shown in the grey color in the table.
| confidence | score | ||||
| Review 1 | 1 | 2 | |||
| Review 2 | 3 | 3 | |||
| Review 3 | 3 | 2 | |||
Reviews and Comments
Review 1
| PC member: | |
| Overall rating: | 2 (accept: I will argue for this paper) |
| Confidence: | 1 |
| Relevance: Is this paper relevant for this conference? | 2 (accept (I will argue for this paper)) |
| Soundness: Is this paper technically sound and complete? | 2 (accept (I will argue for this paper)) |
| Are the claims sufficiently supported by experimental/theoretical results? | 1 (weak accept (vote accept but don't mind rejecting)) |
| Significance: Are the results/ideas interesting for other AI researchers? | 1 (weak accept (vote accept but don't mind rejecting)) |
| Originality: Are the results or ideas novel and previously unknown? | 1 (weak accept (vote accept but don't mind rejecting)) |
| Readability: Is the paper well-organized and easy to understand? | 1 (weak accept (vote accept but don't mind rejecting)) |
| Language: Is the paper written in correct English and style? | 1 (weak accept (vote accept but don't mind rejecting)) |
| Format: Is the paper correctly and consistently formatted? | 2 (accept (I will argue for this paper)) |
| Review: | CONTRIBUTION OF THE PAPER: POSITIVE ASPECTS: NEGATIVE ASPECTS: CHANGES TO IMPROVE THE PAPER: FURTHER COMMENTS: ITEMS BELOW ARE JUSTIFICATION OF THE SCORES IF NEGATIVE: (1) IS THIS PAPER RELEVANT FOR THIS CONFERENCE? (2) IS THIS PAPER TECHNICALLY SOUND AND COMPLETE? (3) ARE THE CLAIMS SUFFICIENTLY SUPPORTED BY EXPERIMENTAL OR THEORETICAL RESULTS? (4) ARE THE RESULTS/IDEAS INTERESTING FOR OTHER AI RESEARCHERS? (5) ARE THE RESULTS OR IDEAS NOVEL AND PREVIOUSLY UNKNOWN? (6) IS THE PAPER WELL-ORGANIZED AND EASY TO UNDERSTAND? (7) IS THE PAPER WRITTEN IN CORRECT ENGLISH AND STYLE? (8) IS THE PAPER CORRECTLY AND CONSISTENTLY FORMATTED? |
| PC only: | |
| Time: | Jul 9, 13:21 |
Review 2
| PC member: | |
| Overall rating: | 3 (strong accept) |
| Confidence: | 3 |
| Relevance: Is this paper relevant for this conference? | 2 (accept (I will argue for this paper)) |
| Soundness: Is this paper technically sound and complete? | 3 (strong accept) |
| Are the claims sufficiently supported by experimental/theoretical results? | 2 (accept (I will argue for this paper)) |
| Significance: Are the results/ideas interesting for other AI researchers? | 3 (strong accept) |
| Originality: Are the results or ideas novel and previously unknown? | 3 (strong accept) |
| Readability: Is the paper well-organized and easy to understand? | 2 (accept (I will argue for this paper)) |
| Language: Is the paper written in correct English and style? | 2 (accept (I will argue for this paper)) |
| Format: Is the paper correctly and consistently formatted? | 2 (accept (I will argue for this paper)) |
| Review: | CONTRIBUTION OF THE PAPER: A method for comparing different musical performances POSITIVE ASPECTS: An interesting problem. The solution is well explained and it has good theoretical aspects. NEGATIVE ASPECTS: None. CHANGES TO IMPROVE THE PAPER: Review English further. FURTHER COMMENTS: Paper was not send as "blind" (author's info were displayed). Why are you not using HMM? You talk of a Hardware limitation. What kind of HW are you thinking about? Check references (#10) |
| PC only: | |
| Time: | Jul 16, 05:39 |
Review 3
| PC member: | |
| Reviewer: | |
| Overall rating: | 2 (accept: I will argue for this paper) |
| Confidence: | 3 |
| Relevance: Is this paper relevant for this conference? | 2 (accept (I will argue for this paper)) |
| Soundness: Is this paper technically sound and complete? | 1 (weak accept (vote accept but don't mind rejecting)) |
| Are the claims sufficiently supported by experimental/theoretical results? | 2 (accept (I will argue for this paper)) |
| Significance: Are the results/ideas interesting for other AI researchers? | 2 (accept (I will argue for this paper)) |
| Originality: Are the results or ideas novel and previously unknown? | 2 (accept (I will argue for this paper)) |
| Readability: Is the paper well-organized and easy to understand? | 2 (accept (I will argue for this paper)) |
| Language: Is the paper written in correct English and style? | 2 (accept (I will argue for this paper)) |
| Format: Is the paper correctly and consistently formatted? | 2 (accept (I will argue for this paper)) |
| Review: | CONTRIBUTION OF THE PAPER: Interesting implementation of very well known techniques, DTW, LCS and Levenshtein distances, to align features for the same problem, matching musical performances. POSITIVE ASPECTS: The testing is done in 20 pairs of different musical pieces, which are enough to demonstrate the performance of the spectral entropy based string AFP method. NEGATIVE ASPECTS: CHANGES TO IMPROVE THE PAPER: Explain why even the performance duration difference for a given musical piece does not affect the results of your proposed method. (e.g. Mozart’s Serenade Number 13 Allegro performance 1 is roughly 18% shorter than performance 2) FURTHER COMMENTS: ITEMS BELOW ARE JUSTIFICATION OF THE SCORES IF NEGATIVE: (1) IS THIS PAPER RELEVANT FOR THIS CONFERENCE? (2) IS THIS PAPER TECHNICALLY SOUND AND COMPLETE? (3) ARE THE CLAIMS SUFFICIENTLY SUPPORTED BY EXPERIMENTAL OR THEORETICAL RESULTS? (4) ARE THE RESULTS/IDEAS INTERESTING FOR OTHER AI RESEARCHERS? (5) ARE THE RESULTS OR IDEAS NOVEL AND PREVIOUSLY UNKNOWN? (6) IS THE PAPER WELL-ORGANIZED AND EASY TO UNDERSTAND? (7) IS THE PAPER WRITTEN IN CORRECT ENGLISH AND STYLE? (8) IS THE PAPER CORRECTLY AND CONSISTENTLY FORMATTED? |
| PC only: | |
| Time: | Jul 18, 22:40 |