2014:Symbolic Melodic Similarity Results

Introduction

These are the results for the 2014 running of the Symbolic Melodic Similarity task set. For background information about this task set please refer to the 2014:Symbolic Melodic Similarity page.

Each system was given a query and returned the 10 most melodically similar songs from those taken from the Essen Collection (5274 pieces in the MIDI format; see ESAC Data Homepage for more information). For each query, we made four classes of error-mutations, thus the set comprises the following query classes:

• 0. No errors
• 1. One note deleted
• 2. One note inserted
• 3. One interval enlarged
• 4. One interval compressed

For each query (and its 4 mutations), the returned results (candidates) from all systems were then grouped together (query set) for evaluation by the human graders. The graders were provide with only heard perfect version against which to evaluate the candidates and did not know whether the candidates came from a perfect or mutated query. Each query/candidate set was evaluated by 1 individual grader. Using the Evalutron 6000 system, the graders gave each query/candidate pair two types of scores. Graders were asked to provide 1 categorical score with 3 categories: NS,SS,VS as explained below, and one fine score (in the range from 0 to 100).

Evalutron 6000 Summary Data

Number of evaluators = 6
Number of evaluations per query/candidate pair = 1
Number of queries per grader = 1
Total number of unique query/candidate pairs graded = 388
Average number of query/candidate pairs evaluated per grader: 67
Number of queries = 6 (perfect) with each perfect query error-mutated 4 different ways = 30

General Legend

Sub code	Submission name	Abstract	Contributors
JU1	ShapeH	PDF	Julián Urbano
JU2	ShapeTime	PDF	Julián Urbano
JU3	Time	PDF	Julián Urbano
RTBB1	ATIC_SMS_2014	PDF	Carles Roig, Lorenzo J. Tardón,Ana María Barbancho, Isabel Barbancho
YHKH1	sys-IRM	PDF	Sakurako Yazawa, Yuhei Hasegawa ,Kouhei Kanamori, Masatoshi Hamanaka

Broad Categories

NS = Not Similar
SS = Somewhat Similar
VS = Very Similar

Table Headings

ADR = Average Dynamic Recall
NRGB = Normalize Recall at Group Boundaries
AP = Average Precision (non-interpolated)
PND = Precision at N Documents

Calculating Summary Measures

Fine⁽¹⁾ = Sum of fine-grained human similarity decisions (0-100).
PSum⁽¹⁾ = Sum of human broad similarity decisions: NS=0, SS=1, VS=2.
WCsum⁽¹⁾ = 'World Cup' scoring: NS=0, SS=1, VS=3 (rewards Very Similar).
SDsum⁽¹⁾ = 'Stephen Downie' scoring: NS=0, SS=1, VS=4 (strongly rewards Very Similar).
Greater0⁽¹⁾ = NS=0, SS=1, VS=1 (binary relevance judgment).
Greater1⁽¹⁾ = NS=0, SS=0, VS=1 (binary relevance judgment using only Very Similar).

⁽¹⁾Normalized to the range 0 to 1.

Summary Results

Overall Scores (Includes Perfect and Error Candidates)

SCORE	JU1	JU2	JU3	YO1
ADR	0.7089	0.7962	0.7997	0.6912
NRGB	0.6786	0.7493	0.7602	0.6378
AP	0.7344	0.7534	0.7992	0.5535
PND	0.7361	0.7444	0.7611	0.5611
Fine	53.7767	54.5967	51.1933	36.9633
PSum	1.1167	1.13	1.1267	0.69
WCSum	1.5033	1.5133	1.5433	0.93667
SDSum	1.89	1.8967	1.96	1.1833
Greater0	0.73	0.74667	0.71	0.44333
Greater1	0.38667	0.38333	0.41667	0.24667

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Scores by Query Error Types

No Errors

SCORE	JU1	JU2	JU3	YO1
ADR	0.7089	0.7962	0.7997	0.6912
NRGB	0.6786	0.7493	0.7602	0.6378
AP	0.7344	0.7534	0.7992	0.5535
PND	0.7361	0.7444	0.7611	0.5611
Fine	58.2333	59.0667	53.3	38.3
PSum	1.2167	1.2333	1.1833	0.71667
WCSum	1.6167	1.6333	1.6167	0.96667
SDSum	2.0167	2.0333	2.05	1.2167
Greater0	0.81667	0.83333	0.75	0.46667
Greater1	0.4	0.4	0.43333	0.25

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Note Deletions

SCORE	JU1	JU2	JU3	YO1
ADR	0.6419	0.8038	0.7728	0.5673
NRGB	0.6268	0.7623	0.7349	0.5436
AP	0.6510	0.7414	0.7364	0.4675
PND	0.6125	0.7139	0.6889	0.4694
Fine	57.1	58.2667	53.65	34.6167
PSum	1.2167	1.2333	1.1833	0.63333
WCSum	1.65	1.6667	1.6333	0.86667
SDSum	2.0833	2.1	2.0833	1.1
Greater0	0.78333	0.8	0.73333	0.4
Greater1	0.43333	0.43333	0.45	0.23333

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Note Insertions

SCORE	JU1	JU2	JU3	YO1
ADR	0.6550	0.7943	0.8162	0.5889
NRGB	0.6326	0.7343	0.8015	0.5854
AP	0.6940	0.7282	0.7750	0.5167
PND	0.7361	0.6611	0.7361	0.5444
Fine	55.85	57.4667	53.05	37.35
PSum	1.15	1.1833	1.15	0.68333
WCSum	1.5333	1.5667	1.5833	0.93333
SDSum	1.9167	1.95	2.0167	1.1833
Greater0	0.76667	0.8	0.71667	0.43333
Greater1	0.38333	0.38333	0.43333	0.25

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Enlarged Intervals

SCORE	JU1	JU2	JU3	YO1
ADR	0.6807	0.7895	0.7727	0.6957
NRGB	0.6378	0.7304	0.7135	0.6360
AP	0.6694	0.7299	0.7296	0.5757
PND	0.6278	0.7389	0.7000	0.5917
Fine	45.4667	45.4167	47.0167	33.9833
PSum	0.91667	0.91667	1.0167	0.61667
WCSum	1.25	1.25	1.3667	0.83333
SDSum	1.5833	1.5833	1.7167	1.05
Greater0	0.58333	0.58333	0.66667	0.4
Greater1	0.33333	0.33333	0.35	0.21667

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Compressed Intervals

SCORE	JU1	JU2	JU3	YO1
ADR	0.6125	0.7076	0.7076	0.5701
NRGB	0.5579	0.6655	0.6655	0.5486
AP	0.6407	0.6711	0.6247	0.4728
PND	0.5833	0.6250	0.6250	0.4306
Fine	52.2333	52.7667	48.95	40.5667
PSum	1.0833	1.0833	1.1	0.8
WCSum	1.4667	1.45	1.5167	1.0833
SDSum	1.85	1.8167	1.9333	1.3667
Greater0	0.7	0.71667	0.68333	0.51667
Greater1	0.38333	0.36667	0.41667	0.28333

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Friedman Test with Multiple Comparisons Results (p=0.05)

The Friedman test was run in MATLAB against the Fine summary data over the 30 queries.
Command: [c,m,h,gnames] = multcompare(stats, 'ctype', 'tukey-kramer','estimate', 'friedman', 'alpha', 0.05);

Row Labels	JU1	JU2	JU3	YO1
q01	63.6	63.6	53.7	37.3
q01_1	63.6	63.6	53.7	27.2
q01_2	47.6	49.5	47.1	29.1
q01_3	25.2	24.9	32.8	30.5
q01_4	37.7	38.4	23.2	29.1
q02	72.5	72.5	72	56.5
q02_1	73.5	72.5	74	56
q02_2	70.5	70.5	70.5	60.5
q02_3	70	70	68.5	56.5
q02_4	72.5	70	72.5	56
q03	49	49	31.5	31.8
q03_1	45	45	31.8	31.8
q03_2	49	49	38.5	35.8
q03_3	49	49	34.5	31.8
q03_4	49	49	31.5	32
q04	67.7	67.7	64.8	29.2
q04_1	62.9	62.9	69.1	29.2
q04_2	70.9	73.7	68.9	33.2
q04_3	64.5	64.5	60	21.6
q04_4	62.6	62.6	69.7	47.3
q05	63	68	63	43.5
q05_1	64	72	58.5	32
q05_2	63	68	58.5	34
q05_3	30.5	30.5	50	34
q05_4	63	68	63	47.5
q06	33.6	33.6	34.8	31.5
q06_1	33.6	33.6	34.8	31.5
q06_2	34.1	34.1	34.8	31.5
q06_3	33.6	33.6	36.3	29.5
q06_4	28.6	28.6	33.8	31.5

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TeamID	TeamID	Lowerbound	Mean	Upperbound	Significance
JU2	JU1	-0.6669	0.1500	0.9669	FALSE
JU2	JU3	-0.4002	0.4167	1.2336	FALSE
JU2	YO1	0.9498	1.7667	2.5836	TRUE
JU1	JU3	-0.5502	0.2667	1.0836	FALSE
JU1	YO1	0.7998	1.6167	2.4336	TRUE
JU3	YO1	0.5331	1.3500	2.1669	TRUE

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