2017:Drum Transcription Results
Contents
Introduction
The drum transcription task was reintroduced this year after it's first edition in 2005. Two out of the three datasets used in 2005 were available and have been used for evaluation also this year. For those datasets the results from 2005 may be compared to this years results.
As in 2005 only the three main drum instruments (kick drum, snare drum, and hi-hat) are considered. Additionally to the two datasets from 2005, three new datasets were used in the evaluation. For training the algorithms, the public training set from 2005 plus additional training data taken from the new datasets was provided to the participants.
Submissions
Abstract | Contributors | |
---|---|---|
CS1-CS3 | Carl Southall | |
CW1-CW3 | Chih-Wei Wu | |
RV1-RV4 | Richard Vogl |
Results
The overall results represent the mean values over all datasets.
Overall
Algorithm | mean fm | mean pr | mean rc | BD mean fm | SD mean fm | HH mean fm |
---|---|---|---|---|---|---|
CW1 | 0.51 | 0.46 | 0.68 | 0.68 | 0.48 | 0.38 |
CW3 | 0.53 | 0.50 | 0.65 | 0.67 | 0.46 | 0.42 |
CW2 | 0.55 | 0.52 | 0.66 | 0.70 | 0.55 | 0.40 |
RV3 | 0.68 | 0.74 | 0.70 | 0.81 | 0.64 | 0.51 |
RV2 | 0.67 | 0.69 | 0.73 | 0.78 | 0.67 | 0.51 |
RV1 | 0.71 | 0.75 | 0.74 | 0.82 | 0.70 | 0.53 |
RV4 | 0.70 | 0.74 | 0.73 | 0.81 | 0.70 | 0.52 |
CS1 | 0.61 | 0.56 | 0.73 | 0.79 | 0.55 | 0.46 |
CS3 | 0.63 | 0.59 | 0.75 | 0.78 | 0.58 | 0.49 |
CS2 | 0.63 | 0.61 | 0.71 | 0.78 | 0.57 | 0.49 |
2005 baseline: 0.670 (YGO)
The best overall result from 2005 is only provided to put the current results into perspective. Since the overall result form 2005 was calculated on different datasets it is problematic to compare them directly.
IDMT subset
Algorithm | mean fm | mean pr | mean rc | BD mean fm | SD mean fm | HH mean fm |
---|---|---|---|---|---|---|
CW1 | 0.37 | 0.30 | 0.66 | 0.55 | 0.45 | 0.26 |
CW3 | 0.42 | 0.37 | 0.62 | 0.53 | 0.44 | 0.32 |
CW2 | 0.41 | 0.35 | 0.67 | 0.56 | 0.54 | 0.28 |
RV3 | 0.62 | 0.73 | 0.68 | 0.73 | 0.70 | 0.45 |
RV2 | 0.66 | 0.69 | 0.75 | 0.74 | 0.71 | 0.54 |
RV1 | 0.66 | 0.74 | 0.73 | 0.75 | 0.72 | 0.53 |
RV4 | 0.66 | 0.74 | 0.72 | 0.74 | 0.73 | 0.51 |
CS1 | 0.51 | 0.49 | 0.64 | 0.62 | 0.48 | 0.42 |
CS3 | 0.51 | 0.51 | 0.63 | 0.60 | 0.48 | 0.43 |
CS2 | 0.52 | 0.54 | 0.63 | 0.60 | 0.51 | 0.43 |
2005 baseline: 0.753 (CD)
KT subset
Algorithm | mean fm | mean pr | mean rc | BD mean fm | SD mean fm | HH mean fm |
---|---|---|---|---|---|---|
CW1 | 0.48 | 0.40 | 0.67 | 0.59 | 0.44 | 0.40 |
CW3 | 0.48 | 0.43 | 0.62 | 0.58 | 0.42 | 0.41 |
CW2 | 0.52 | 0.48 | 0.64 | 0.60 | 0.53 | 0.41 |
RV3 | 0.62 | 0.73 | 0.60 | 0.77 | 0.63 | 0.46 |
RV2 | 0.63 | 0.66 | 0.65 | 0.76 | 0.68 | 0.45 |
RV1 | 0.65 | 0.73 | 0.64 | 0.80 | 0.68 | 0.47 |
RV4 | 0.65 | 0.72 | 0.63 | 0.79 | 0.68 | 0.44 |
CS1 | 0.53 | 0.48 | 0.63 | 0.71 | 0.50 | 0.38 |
CS3 | 0.56 | 0.52 | 0.65 | 0.71 | 0.53 | 0.40 |
CS2 | 0.55 | 0.52 | 0.61 | 0.70 | 0.52 | 0.39 |
2005 baseline: 0.617 (YGO)
RBMA subset
Algorithm | mean fm | mean pr | mean rc | BD mean fm | SD mean fm | HH mean fm |
---|---|---|---|---|---|---|
CW1 | 0.50 | 0.46 | 0.61 | 0.71 | 0.35 | 0.39 |
CW3 | 0.54 | 0.51 | 0.63 | 0.75 | 0.30 | 0.47 |
CW2 | 0.54 | 0.52 | 0.61 | 0.73 | 0.37 | 0.46 |
RV3 | 0.69 | 0.71 | 0.73 | 0.89 | 0.49 | 0.55 |
RV2 | 0.70 | 0.68 | 0.78 | 0.91 | 0.60 | 0.55 |
RV1 | 0.72 | 0.74 | 0.75 | 0.91 | 0.62 | 0.56 |
RV4 | 0.72 | 0.73 | 0.76 | 0.92 | 0.64 | 0.57 |
CS1 | 0.66 | 0.60 | 0.79 | 0.88 | 0.45 | 0.57 |
CS3 | 0.66 | 0.60 | 0.81 | 0.87 | 0.49 | 0.58 |
CS2 | 0.64 | 0.60 | 0.72 | 0.87 | 0.43 | 0.54 |
MEDLEY subset
Algorithm | mean fm | mean pr | mean rc | BD mean fm | SD mean fm | HH mean fm |
---|---|---|---|---|---|---|
CW1 | 0.62 | 0.61 | 0.68 | 0.76 | 0.52 | 0.53 |
CW3 | 0.59 | 0.56 | 0.66 | 0.71 | 0.49 | 0.52 |
CW2 | 0.62 | 0.62 | 0.65 | 0.75 | 0.55 | 0.52 |
RV3 | 0.69 | 0.79 | 0.66 | 0.74 | 0.62 | 0.57 |
RV2 | 0.66 | 0.77 | 0.63 | 0.60 | 0.62 | 0.64 |
RV1 | 0.73 | 0.78 | 0.73 | 0.75 | 0.70 | 0.60 |
RV4 | 0.70 | 0.79 | 0.68 | 0.70 | 0.68 | 0.61 |
CS1 | 0.68 | 0.65 | 0.76 | 0.83 | 0.60 | 0.57 |
CS3 | 0.74 | 0.70 | 0.82 | 0.84 | 0.67 | 0.65 |
CS2 | 0.72 | 0.71 | 0.76 | 0.82 | 0.62 | 0.65 |
GEN subset
Algorithm | mean fm | mean pr | mean rc | BD mean fm | SD mean fm | HH mean fm |
---|---|---|---|---|---|---|
CW1 | 0.60 | 0.53 | 0.76 | 0.80 | 0.64 | 0.33 |
CW3 | 0.63 | 0.60 | 0.72 | 0.79 | 0.66 | 0.36 |
CW2 | 0.65 | 0.65 | 0.72 | 0.84 | 0.74 | 0.33 |
RV3 | 0.76 | 0.73 | 0.83 | 0.90 | 0.77 | 0.50 |
RV2 | 0.70 | 0.63 | 0.83 | 0.89 | 0.75 | 0.38 |
RV1 | 0.78 | 0.74 | 0.86 | 0.91 | 0.80 | 0.50 |
RV4 | 0.76 | 0.72 | 0.84 | 0.90 | 0.79 | 0.48 |
CS1 | 0.68 | 0.59 | 0.84 | 0.90 | 0.73 | 0.37 |
CS3 | 0.69 | 0.61 | 0.85 | 0.86 | 0.75 | 0.39 |
CS2 | 0.72 | 0.65 | 0.84 | 0.89 | 0.74 | 0.42 |