Evaluation in Audio Music Similarity

transcript

PhD dissertation

Julián Urbano

Leganés, October 3rd 2013 Picture by Javier García

Outline

• Introduction

• Validity

• Reliability

• Efficiency

• Conclusions and Future Work

Outline

• Introduction

– Scope

– The Cranfield Paradigm

• Validity

• Reliability

• Efficiency

Information Retrieval

• Automatic representation, storage and search of unstructured information

– Traditionally textual information

– Lately multimedia too: images, video, music

• A user has an information need and uses an IR system that retrieves the relevant or significant information from a collection of documents

Information Retrieval Evaluation

• IR systems are based on models to estimate relevance, implementing different techniques

• How good is my system? What system is better?

• Answered with IR Evaluation experiments

– “if you can’t measure it, you can’t improve it”

– But we need to be able to trust our measurements

• Research on IR Evaluation

– Improve our methods to evaluate systems

– Critical for the correct development of the field

History of IR Evaluation research

Cranfield 2 MEDLARS

1980 1970 1990 2000 2010

CLEF NTCIR

Cranfield 2 MEDLARS

1980 1970 1990 2000 2010

CLEF NTCIR

Cranfield 2 MEDLARS

MusiCLEF

1980 1970 1990 2000 2010

MSD Challenge

CLEF NTCIR

Cranfield 2 MEDLARS

MusiCLEF

1980 1970 1990 2000 2010

MSD Challenge

CLEF NTCIR

Cranfield 2 MEDLARS

MusiCLEF

1980 1970 1990 2000 2010

MSD Challenge

Audio Music Similarity

• Song as input to system, audio signal

• Retrieve songs musically similar to it, by content

• Resembles traditional Ad Hoc retrieval in Text IR

• (most?) Important task in Music IR

– Music recommendation

– Playlist generation

– Plagiarism detection

• Annual evaluation in MIREX

Outline

• Introduction

– Scope

• Validity

• Reliability

• Efficiency

Outline

• Introduction

– Scope

• Validity

• Reliability

• Efficiency

The two questions

• How good is my system?

– What does good mean?

– What is good enough?

• Is system A better than system B?

– What does better mean?

– How much better?

• Efficiency? Effectiveness? Ease?

Measure user experience

• We are interested in user-measures

– Time to complete task, idle time, success rate, failure rate, frustration, ease to learn, ease to use …

– Their distributions describe user experience, fully

• User satisfaction is the bigger picture

– How likely is it that an arbitrary user, with an arbitrary query (and with an arbitrary document collection) will be satisfied by the system?

• This is the ultimate goal: the good, the better

The Cranfield Paradigm

• Estimate user-measure distributions

– Sample documents, queries and users

– Monitor user experience and behavior

– Representativeness, cost, ethics, privacy …

• Fix samples to allow reproducibility

– But cannot fix users and their behavior

– Remove users, but include a static user component, fixed across experiments: ground truth judgments

– Still need to include the dynamics of the process: user models behind effectiveness measures and scales

Test collections

• Our goal is the users: user-measure = f(system)

• Cranfield measures systems: system-effectiveness = f(system, measure, scale)

• Estimators of the distributions of user-measures – Only source of variability is the systems themselves

– Reproducibility becomes easy

– Experiments are inexpensive (collections are not)

– Research becomes systematic

Validity, Reliability and Efficiency

• Validity: are we measuring what we want to?

– How well are effectiveness and satisfaction correlated?

– How good is good and how better is better?

• Reliability: how repeatable are the results?

– How large do samples have to be?

– What statistical methods should be used?

• Efficiency: how inexpensive is it to get valid and reliable results?

– Can we estimate results with fewer judgments?

Goal of this dissertation

Study and improve the validity, reliability and efficiency

of the methods used to evaluate AMS systems

Additionally, improve meta-evaluation methods

Outline

• Introduction

– Scope

• Validity

• Reliability

• Efficiency

Outline

• Introduction

• Validity

– System Effectiveness and User Satisfaction

– Modeling Distributions

• Reliability

• Efficiency

Assumption of Cranfield

• Systems with better effectiveness are perceived by users as more useful, more satisfactory

• But different effectiveness measures and relevance scales produce different distributions

– Which one is better to predict user satisfaction?

• Map system effectiveness onto user satisfaction, experimentally

– If P@10 = 0.2, how likely is it that an arbitrary user will find the results satisfactory?

– What if DCG@20 = 0.46? 18

Measures and scales

Measure Original Artificial Graded Artificial Binary

Broad Fine nℒ=3 nℒ=4 nℒ=5 ℓmin=20 ℓmin=40 ℓmin=60 ℓmin=80

P@5 X X X X

AP@5 X X X X

RR@5 X X X X

CGl@5 X X X X X P@5 P@5 P@5 P@5

CGe@5 X X X X P@5 P@5 P@5 P@5

DCGl@5 X X X X X X X X X

DCGe@5 X X X X DCGl@5 DCGl@5 DCGl@5 DCGl@5

EDCGl@5 X X X X X X X X X

EDCGe@5 X X X X EDCGl@5 EDCGl@5 EDCGl@5 EDCGl@5

Ql@5 X X X X X AP@5 AP@5 AP@5 AP@5

Qe@5 X X X X AP@5 AP@5 AP@5 AP@5

RBPl@5 X X X X X X X X X

RBPe@5 X X X X RBPl@5 RBPl@5 RBPl@5 RBPl@5

ERRl@5 X X X X X X X X X

ERRe@5 X X X X ERRl@5 ERRl@5 ERRl@5 ERRl@5

GAP@5 X X X X X AP@5 AP@5 AP@5 AP@5

ADR@5 X X X X X X X X

Measures and scales

P@5 X X X X

AP@5 X X X X

RR@5 X X X X

CGl@5 X X X X X P@5 P@5 P@5 P@5

CGe@5 X X X X P@5 P@5 P@5 P@5

Measures and scales

P@5 X X X X

AP@5 X X X X

RR@5 X X X X

CGl@5 X X X X X P@5 P@5 P@5 P@5

CGe@5 X X X X P@5 P@5 P@5 P@5

Measures and scales

P@5 X X X X

AP@5 X X X X

RR@5 X X X X

CGl@5 X X X X X P@5 P@5 P@5 P@5

CGe@5 X X X X P@5 P@5 P@5 P@5

Measures and scales

P@5 X X X X

AP@5 X X X X

RR@5 X X X X

CGl@5 X X X X X P@5 P@5 P@5 P@5

CGe@5 X X X X P@5 P@5 P@5 P@5

Measures and scales

P@5 X X X X

AP@5 X X X X

RR@5 X X X X

CGl@5 X X X X X P@5 P@5 P@5 P@5

CGe@5 X X X X P@5 P@5 P@5 P@5

Measures and scales

P@5 X X X X

AP@5 X X X X

RR@5 X X X X

CGl@5 X X X X X P@5 P@5 P@5 P@5

CGe@5 X X X X P@5 P@5 P@5 P@5

Measures and scales

P@5 X X X X

AP@5 X X X X

RR@5 X X X X

CGl@5 X X X X X P@5 P@5 P@5 P@5

CGe@5 X X X X P@5 P@5 P@5 P@5

Measures and scales

P@5 X X X X

AP@5 X X X X

RR@5 X X X X

CGl@5 X X X X X P@5 P@5 P@5 P@5

CGe@5 X X X X P@5 P@5 P@5 P@5

Measures and scales

P@5 X X X X

AP@5 X X X X

RR@5 X X X X

CGl@5 X X X X X P@5 P@5 P@5 P@5

CGe@5 X X X X P@5 P@5 P@5 P@5

Measures and scales

P@5 X X X X

AP@5 X X X X

RR@5 X X X X

CGl@5 X X X X X P@5 P@5 P@5 P@5

CGe@5 X X X X P@5 P@5 P@5 P@5

Measures and scales

P@5 X X X X

AP@5 X X X X

RR@5 X X X X

CGl@5 X X X X X P@5 P@5 P@5 P@5

CGe@5 X X X X P@5 P@5 P@5 P@5

Experimental design

What can we infer?

• Preference: difference noticed by user

– Positive: user agrees with evaluation

– Negative: user disagrees with evaluation

• Non-preference: difference not noticed by user

– Good: both systems are satisfactory

– Bad: both systems are not satisfactory

• Queries, documents and judgments from MIREX

• 4115 unique and artificial examples

• 432 unique queries, 5636 unique documents

• Answers collected via Crowdsourcing

– Quality control with trap questions

• 113 unique subjects

Single system: how good is it?

• For 2045 examples (49%) users could not decide which system was better

What do we expect?

• For 2045 examples (49%) users could not decide which system was better

• Large ℓmin thresholds underestimate satisfaction

• Users don’t pay attention to ranking?

• Exponential gain underestimates satisfaction

• Document utility independent of others

Two systems: which one is better?

• For 2090 examples (51%) users did prefer one system over the other one

What do we expect?

• For 2090 examples (51%) users did prefer one system over the other one

• Large differences needed for users to note them

• More relevance levels are better to discriminate

• Cascade and navigational user models are not appropriate

• Users do prefer the (supposedly) worse system

Summary

• Effectiveness and satisfaction are clearly correlated – But there is a bias of 20% because of user disagreement – Room for improvement through personalization

• Magnitude of differences does matter – Just looking at rankings is very naive

• Be careful with statistical significance

– Need Δλ≈0.4 for users to agree with effectiveness • Historically, only 20% of times in MIREX

• Differences among measures and scales – Linear gain slightly better than exponential gain – Informational and positional user models better than

navigational and cascade – The more relevance levels, the better

Measures and scales

P@5 X X X X

AP@5 X X X X

RR@5 X X X X

CGl@5 X X X X X P@5 P@5 P@5 P@5

CGe@5 X X X X P@5 P@5 P@5 P@5

Measures and scales

P@5 X X X X

AP@5 X X X X

RR@5 X X X X

CGl@5 X X X X X P@5 P@5 P@5 P@5

CGe@5 X X X X P@5 P@5 P@5 P@5

Outline

• Introduction

• Validity

• Reliability

• Efficiency

Outline

• Introduction

• Validity

• Reliability

• Efficiency

Evaluate in terms of user satisfaction

• So far, arbitrary users for a single query

– P Sat Ql@5 = 0.61 = 0.7

• Easily for n users and a single query

– P Sat15 = 10 Ql@5 = 0.61 = 0.21

• What about a sample of queries 𝒬?

– Map queries separately for the distribution of P(Sat)

– For easier mappings, P(Sat | λ) functions are interpolated with simple polynomials

Expected probability of satisfaction

• Now we can compute point and interval estimates of the expected probability of satisfaction

• Intuition fails when interpreting effectiveness

System success

• If P(Sat) ≥ threshold the system is successful

– Setting the threshold was rather arbitrary

– Now it is meaningful, in terms of user satisfaction

• Intuitively, we want the majority of users to find the system satisfactory

– P Succ = P P Sat > 0.5 = 1 − FP Sat (0.5)

• Improving queries for which we are bad is worthier than further improving those for which we are already good

Distribution of P(Sat)

• Need to estimate the cumulative distribution function of user satisfaction: FP(Sat)

• Not described by a typical distribution family

– ecdf converges, but what is a good sample size?

– Compare with Normal, Truncated Normal and Beta

• Compared on >2M random samples from MIREX collections, at different query set sizes

• Goodness of fit as to Cramér-von Mises ω2

Estimated distribution of P(Sat)

• More than ≈25 queries in the collection

– ecdf approximates better

• Less than ≈25 queries in the collection

– Normal for graded scales, ecdf for binary scales

• Beta is always the best with the Fine scale

• The more levels in the relevance scale, the better

• Linear gain better than exponential gain

Intuition fails, again

• Intuitive conclusions based on effectiveness alone contradict those based on user satisfaction

– E Δλ = −0.002

– E ΔP Sat = 0.001

– E ΔP Succ = 0.07

– E Δλ = −0.002

– E ΔP Sat = 0.001

– E ΔP Succ = 0.07

– E Δλ = −0.002

– E ΔP Sat = 0.001

– E ΔP Succ = 0.07

Historically, in MIREX

• Systems are not as satisfactory as we thought

• But they are more successful

– Good (or bad) for some kinds of queries

Measures and scales

Broad Fine nℒ=4 nℒ=5 ℓmin=20 ℓmin=40

P@5 X X

AP@5 X X

CGl@5 X X X X P@5 P@5

CGe@5 X X X P@5 P@5

DCGl@5 X X X X X X

DCGe@5 X X X DCGl@5 DCGl@5

Ql@5 X X X X AP@5 AP@5

Qe@5 X X X AP@5 AP@5

RBPl@5 X X X X X X

RBPe@5 X X X RBPl@5 RBPl@5

GAP@5 X X X X AP@5 AP@5

Measures and scales

Broad Fine nℒ=4 nℒ=5 ℓmin=20 ℓmin=40

P@5 X X

AP@5 X X

CGl@5 X X X X P@5 P@5

CGe@5 X X X P@5 P@5

DCGl@5 X X X X X X

DCGe@5 X X X DCGl@5 DCGl@5

Ql@5 X X X X AP@5 AP@5

Qe@5 X X X AP@5 AP@5

RBPl@5 X X X X X X

RBPe@5 X X X RBPl@5 RBPl@5

GAP@5 X X X X AP@5 AP@5

Outline

• Introduction

• Validity

• Reliability

• Efficiency

Outline

• Introduction

• Validity

• Reliability

– Optimality of Statistical Significance Tests

– Test Collection Size

• Efficiency

Random error

• Test collections are just samples from larger, possibly infinite, populations

• If we conclude system A is better than B, how confident can we be?

– Δλ𝒬 is just an estimate of the population mean μΔλ

• Usually employ some statistical significance test for differences in location

• If it is statistically significant, we have confidence that the true difference is at least that large

Statistical hypothesis testing

• Set two mutually exclusive hypotheses

– H0: μΔλ = 0

– H1: μΔλ ≠ 0

• Run test, obtain p-value= P μΔλ ≥ Δλ𝒬 H0

– p ≤ α: statistically significant, high confidence

– p > α: statistically non-significant, low confidence

• Possible errors in the binary decision

– Type I: incorrectly reject H0

– Type II: incorrectly accept H0

Statistical significance tests

• (Non-)parametric tests

– t-test, Wilcoxon test, Sign test

• Based on resampling

– Bootstrap test, permutation/randomization test

• They make certain assumptions about distributions and sampling methods

– Often violated in IR evaluation experiments

– Which test behaves better, in practice, knowing that assumptions are violated?

Optimality criteria

• Power

– Achieve significance as often as possible (low Type II)

– Usually increases Type I error rates

• Safety

– Minimize Type I error rates

– Usually decreases power

• Exactness

– Maintain Type I error rate at α level

– Permutation test is theoretically exact

Experimental design

• Randomly split query set in two

• Evaluate all systems with both subsets

– Simulating two different test collections

• Compare p-values with both subsets

– How well do statistical tests agree with themselves?

– At different α levels

• All systems and queries from MIREX 2007-2011

– >15M p-values

Power and success

• Bootstrap test is the most powerful

• Wilcoxon, bootstrap and permutation are the most successful, depending on α level

Conflicts

• Wilcoxon and t-test are the safest at low α levels

• Wilcoxon is the most exact at low α levels, but bootstrap is for usual levels

Optimal measure and scale

• Power: CGl@5, GAP@5, DCGl@5 and RBPl@5

• Success: CGl@5, GAP@5, DCGl@5 and RBPl@5

• Conflicts: very similar across measures

• Power: Fine, Broad and binary

• Success: Fine, Broad and binary

• Conflicts: very similar across scales

Outline

• Introduction

• Validity

• Reliability

• Efficiency

Outline

• Introduction

• Validity

• Reliability

• Efficiency

Acceptable sample size

• Reliability is higher with larger sample sizes

– But it is also more expensive

– What is an acceptable test collection size?

• Answer with Generalizability Theory

– G-Study: estimate variance components

– D-Study: estimate reliability of different sample sizes and experimental designs

G-study: variance components

• Fully crossed experimental design: s × q

λq,A = λ + λA + λq + εqA

σ2 = σs

2 + σq2 + σsq

σ2 = σs

2 + σq2 + σsq

σ2 = σs

2 + σq2 + σsq

σ2 = σs

2 + σq2 + σsq

σ2 = σs

2 + σq2 + σsq

σ2 = σs

2 + σq2 + σsq

σ2 = σs

2 + σq2 + σsq

σ2 = σs

2 + σq2 + σsq

σ2 = σs

2 + σq2 + σsq

σ2 = σs

2 + σq2 + σsq

σ2 = σs

2 + σq2 + σsq

σ2 = σs

2 + σq2 + σsq

• Estimated with Analysis of Variance

• If σs2 is small or σq

2 is large, we need more queries

D-study: variance ratios

• Stability of absolute scores

Φ nq =σs2

σs2 +

σq2 + σe

• Stability of relative scores

Eρ2 nq =σs2

σs2 +

• We can easily estimate how many queries are needed to reach some level of stability (reliability)

D-study: variance ratios

• Stability of absolute scores

Φ nq =σs2

σs2 +

σq2 + σe

• Stability of relative scores

Eρ2 nq =σs2

σs2 +

• We can easily estimate how many queries are needed to reach some level of stability (reliability)

Effect of query set size

• Average absolute stability Φ = 0.97 • ≈65 queries needed for Φ2 = 0.95, ≈100 in worst cases • Fine scale slightly better than Broad and binary scales • RBPl@5 and nDCGl@5 are the most stable

Effect of query set size

• Average relative stability Eρ 2 = 0.98

• ≈35 queries needed for Eρ2 = 0.95, ≈60 in worst cases

• Fine scale better than Broad and binary scales

• CGl@5 and RBPl@5 are the most stable

Effect of cutoff k

• What if we use a deeper cutoff, k=10?

– From 100 queries and k=5 to 50 queries and k=10

– Should still have stable scores

– Judging effort should decrease

– Rank-based measures should become more stable

• Tested in MIREX 2012

– Apparently in 2013 too

Effect of cutoff k

• Judging effort reduced to 72% of the usual

• Generally stable – From Φ = 0.81 to Φ = 0.83

– From Eρ 2 = 0.93 to Eρ 2 = 0.95

Effect of cutoff k

• Reliability given a fixed budged for judging?

– k=10 allows us to use fewer queries, about 70%

– Slightly reduced relative stability

Effect of assessor set size

• More assessors or simply more queries?

– Judging effort is multiplied

• Can be studied with MIREX 2006 data

– 3 different assessors per query

– Nested experimental design: s × h: q

Effect of assessor set size

• Broad scale: σ s2 ≈ σ h:q

• Fine scale: σ s2 ≫ σ h:q

• Always better to spend resources on queries

Summary

• MIREX collections generally larger than necessary

• For fixed budget

– More queries better than more assessors

– More queries slightly better than deeper cutoff

• Worth studying alternative user model?

• Employ G-Theory while building the collection

• Fine better than Broad, better than binary

• CGl@5 and DCGl@5 best for relative stability

• RBPl@5 and nDCGl@5 best for absolute stability

Outline

• Introduction

• Validity

• Reliability

• Efficiency

Outline

• Introduction

• Validity

• Reliability

• Efficiency

– Learning Relevance Distributions

– Low-cost Evaluation

Probabilistic evaluation

• The MIREX setting is still expensive

– Need to judge all top k documents from all systems

– Takes days, even weeks sometimes

• Model relevance probabilistically

• Relevance judgments are random variables over the space of possible assignments of relevance

• Effectiveness measures are also probabilistic

Probabilistic evaluation

• Accuracy increases as we make judgments

– E Rd ← rd

• Reliability increases too (confidence)

– Var Rd ← 0

• Iteratively estimate relevance and effectiveness

– If confidence is low, make judgments

– If confidence is high, stop

• Judge as few documents as possible

Learning distributions of relevance

• Uniform distribution is very uninformative

• Historical distribution in MIREX has high variance

• Estimate from a set of features: P Rd = ℓ θd

– For each document separately

– Ordinal Logistic Regression

• Three sets of features

– Output-based, can always be used

– Judgment-based, to exploit known judgments

– Audio-based, to exploit musical similarity

Learned models

• Mout : can be used even without judgments

– Similarity between systems’ outputs

– Genre and artist metadata

• Genre is highly correlated to similarity

– Decent fit, R2 ≈ 0.35

• Mjud : can be used when there are judgments

– Similarity between systems’ outputs

– Known relevance of same system and same artist

• Artist is extremely correlated to similarity

– Excellent fit, R2 ≈ 0.91

Estimation errors

• Actual vs. predicted by Mout

– 0.36 with Broad and 0.34 with Fine

• Actual vs. predicted by Mjud

• Among assessors in MIREX 2006

• Negligible under the current MIREX setting

Outline

• Introduction

• Validity

• Reliability

• Efficiency

Outline

• Introduction

• Validity

• Reliability

• Efficiency

Probabilistic effectiveness measures

• Effectiveness scores are also random variables

• Different approaches to compute estimates

– Deal with dependence of random variables

– Different definitions of confidence

• For measures based on ideal ranking (nDCGl@k and RBPl@k) we do not have a closed form

– Approximated with Delta method and Taylor series

Ranking without judgments

1. Estimate relevance with Mout

2. Estimate relative differences and rank systems

• Average confidence in the rankings is 94%

• Average accuracy of the ranking is 92%

Ranking without judgments

• Can we trust individual estimates?

– Ideally, we want X% accuracy when X% confidence

– Confidence slightly overestimated in [0.9, 0.99)

DCGl@5

Confidence Broad Fine

In bin Accuracy In bin Accuracy

[0.5, 0.6) 23 (6.5%) 0.826 22 (6.2%) 0.636

[0.6, 0.7) 14 (4%) 0.786 16 (4.5%) 0.812

[0.7, 0.8) 14 (4%) 0.571 11 (3.1%) 0.364

[0.8, 0.9) 22 (6.2%) 0.864 21 (6%) 0.762

[0.9, 0.95) 23 (6.5%) 0.87 19 (5.4%) 0.895

[0.95, 0.99) 24 (6.8%) 0.917 27 (7.7%) 0.926

[0.99, 1) 232 (65.9%) 0.996 236 (67%) 0.996

E[Accuracy] 0.938 0.921

Relative estimates with judgments

2. Estimate relative differences and rank systems

3. While confidence is low (<95%) 1. Select a document and judge it

2. Update relevance estimates with Mjud when possible

3. Update estimates of differences and rank systems

• What documents should we judge? – Those that are the most informative

– Measure-dependent

Relative estimates with judgments

• Judging effort dramatically reduced – 1.3% with CGl@5, 9.7% with RBPl@5

• Average accuracy still 92%, but improved individually – 74% of estimates with >99% confidence, 99.9% accurate

– Expected accuracy improves slightly from 0.927 to 0.931

Absolute estimates with judgments

2. Estimate absolute effectiveness scores

3. While confidence is low (expected error >±0.05) 1. Select a document and judge it

2. Update relevance estimates with Mjud when possible

3. Update estimates of absolute effectiveness scores

• What documents should we judge? – Those that reduce variance the most

– Measure-dependent

• The stopping condition is overly confident – Virtually no judgments are even needed (supposedly)

• But effectiveness is highly overestimated – Especially with nDCGl@5 and RBPl@5 – Mjud, and especially Mout, tend to overestimate relevance

• Practical fix: correct variance

• Estimates are better, but at the cost of judging

– Need between 15% and 35% of judgments

Summary

• Estimate ranking of systems with no judgments

– 92% accuracy on average, trustworthy individually

– Statistically significant differences are always correct

• If we want more confidence, judge documents

– As few as 2% needed to reach 95% confidence

– 74% of estimates have >99% confidence and accuracy

• Estimate absolute scores, judging as necessary

– Around 25% needed to ensure error <0.05

Outline

• Introduction

• Validity

• Reliability

• Efficiency

Outline

• Introduction

• Validity

• Reliability

• Efficiency

– Conclusions

– Future Work

Validity

• Cranfield tells us about systems, not about users

• Provide empirical mapping from system effectiveness onto user satisfaction

• Room for personalization quantified in 20%

• Need large differences for users to note them

• Consider full distributions, not just averages

• Conclusions based on effectiveness tend to contradict conclusions based on user satisfaction

Reliability

• Different significance tests for different needs

– Bootstrap test is the most powerful

– Wilcoxon and t-test are the safest

– Wilcoxon and bootstrap test are the most exact

• Practical interpretation of p-values

• MIREX collections generally larger than needed

• Spend resources on queries, not on assessors

• User models with deeper cutoffs are feasible

• Employ G-Theory while building collections

Efficiency

• Probabilistic evaluation reduces cost, dramatically

• Two models to estimate document relevance

• System rankings 92% accurate without judgments

• 2% of judgments to reach 95% confidence

• 25% of judgments to reduce error to 0.05

Measures and scales

• Best measure and scale depends on situation

• But generally speaking

– CGl@5, DCGl@5 and RBPl@5

– Fine scale

– Model distributions as Beta

Outline

• Introduction

• Validity

• Reliability

• Efficiency

– Conclusions

– Future Work

Outline

• Introduction

• Validity

• Reliability

• Efficiency

– Conclusions

– Future Work

Validity

• User studies to understand user behavior

• What information to include in test collections

• Other forms of relevance judgment to better capture document utility

• Explicitly define judging guidelines

• Similar mapping for Text IR

Reliability

• Corrections for Multiple Comparisons

• Methods to reliably estimate reliability while building test collections

Efficiency

• Better models to estimate document relevance

• Correct variance when having just a few relevance judgments available

• Estimate relevance beyond k=5

• Other stopping conditions and document weights

Conduct similar studies

for the wealth of tasks in

Music Information Retrieval

Evaluation in Audio Music Similarity

PhD dissertation

Julián Urbano

Leganés, October 3rd 2013 Picture by Javier García

Evaluation in Audio Music Similarity

Technology