PhD Thesis: Knowledge Extraction and Representation Learning for Music Recommendation and Classification

Knowledge Extrac0on and Representa0on Learning
for Music Recommenda0on and Classiﬁca0on

Sergio Oramas Mar>n
Doctoral Thesis Defense
Departament of Informa0on and Communica0on Technologies
Thesis Director:
Dr. Xavier Serra

Wednesday, November 29th, 2017
Thesis Board:
Dr. Markus Schedl
Dr. Emilia Gómez
Dr. Brian Whitman


Popularity bias


cold-start


Cold-start problem

New releases
Old catalog inges0on


Exploita0on vs. Explora0on
cold-start


Collabora0ve Filtering


Collabora0ve Filtering Content-based


Collabora0ve Filtering Content-based
Hybrid methods


Unstructured text
-  Rich informa0on: genre, rela0ons, inﬂuences
-  Noisy
-  Some0mes used for Rec.


Audio
-  Rich informa0on: genre, 0mber, instruments
-  Seman0c gap
-  OZen used for Rec.


Images
-  Rich informa0on: genre, age, style
-  Seman0c gap
-  Rarely used for Rec.


Tags
-  Rich and curated informa0on
-  Need experts or a crowd
-  May be limited
-  OZen used for Rec.


Music Genres: Categories that share similar musical, regional,
or temporal characteris0cs

•  “MIR is a mul0disciplinary ﬁeld of research concerned with the extrac0on,
analysis, and usage of informa0on about any kind of music en0ty (e.g., a
song or a music ar0st) on any representa0on level (e.g., audio signal,
symbolic MIDI).” (Schedl, 2008)
Music Informa0on Retrieval (MIR)

•  “MIR is a mul0disciplinary ﬁeld of research concerned with the extrac0on,
analysis, and usage of informa0on about any kind of music en0ty (e.g., a
song or a music ar0st) on any representa>on level (e.g., audio signal,
symbolic MIDI).” (Schedl, 2008)

chords, onsets

genre, mood, form

word frequencies,
co-occurrence, n-grams

noun phrases,
part-of-speech tags

seman0c rela0ons,
disambiguated en00es,
syntac0c dependencies

Most MIR research

Knowledge Extrac0on | Recommenda0on | Classiﬁca0on | Conclusions

En0ty Linking
The task to discover men0ons of en00es in text and link them
to a suitable knowledge repository (Moro et al. 2014).

En0ty Linking
Elvis Presley covered Guitar Man with Reed

En0ty Linking
Ambiguity

En0ty Linking
The task to discover men0ons of en00es in text and link them
to a suitable knowledge repository (Moro et al. 2014).


En0ty Linking
hdps://en.wikipedia.org/wiki/Jerry_Reed
hdps://en.wikipedia.org/wiki/Elvis_Presley
hdps://en.wikipedia.org/wiki/Guitar_Man_(song)

En0ty Linking
•  State-of-the-art systems
–  Babelfy
•  KB: BabelNet
–  TagMe
•  KB: Wikipedia
–  DBpedia Spotlight
•  KB: DBpedia

En0ty Linking in the Music Domain
•  Ambiguity problem
–  “This was the third Weezer album that they simply named
Weezer.”


Weezer.”
–  “Debut is the ﬁrst interna0onal solo studio album by Björk.”
“Led Zeppelin released their debut album 48 years ago.”


Weezer.”

•  Scant research


Weezer.”

•  Scant research
•  Knowledge Bases are incomplete
–  Mainly popular and Western ar0sts


ELVIS (En0ty Linking Vo0ng and Integra0on System)

ELMD Dataset
•  Linguis>c resource
•  13k ar0st biographies with links from last.fm
•  4 en0ty types: Ar0st, Album, Track, Record Label
•  Disambigua0on to DBpedia with ELVIS
•  Manual evalua0on: Precision 0.97
Oramas S., Espinosa-Anke L., Sordo M., Saggion H., & Serra X. (2016). ELMD: An Automa0cally
Generated En0ty Linking Gold Standard in the Music Domain. LREC 2016.

The process of iden0fying and annota0ng relevant seman0c
rela0ons between en00es in text.
Rela0on Extrac0on

“Gorillaz are a Bri0sh virtual band formed in 1998 by Damon Albarn of
Blur, and Jaime Haweled, co-creator of the comic book Tank Girl.”
Rela0on Extrac0on

•  Knowledge Base: Repository of knowledge organized in a
taxonomic or ontologic structure.
–  HandcraZed: WordNet, DBpedia, BabelNet, Freebase
–  Fully automa0c: NELL, ReVerb
Knowledge Bases

•  Knowledge Base: Repository of knowledge organized in a
taxonomic or ontologic structure.
–  HandcraZed: WordNet, DBpedia, BabelNet, Freebase
–  Fully automa0c: NELL, ReVerb
•  Music Knowledge Bases (or databases)
–  HandcraZed: MusicBrainz, Discogs
–  Fully automa0c: -
Knowledge Bases

Automated Knowledge Base Construc0on

•  Dependency Parsing: MATE Tools (Bohnet 2010)

•  En>ty Linking: DBpedia Spotlight

•  Group en0ty nodes
•  Find shortest path between en00es

•  Shortest path is prone to errors
•  Filtering: Regular expressions
–  Lexical: Word lemmas
–  Syntac>cal: Dependency func0ons
–  Morphological: Part-of-speech tags

•  Group diﬀerent rela0ons with similar meaning
•  Simplify the knowledge base
was wriden by ar0st
was wriden by frontman
was wriden by guitarist
was wriden by singer
was wriden by

•  Conﬁdence measure of extracted rela0ons
•  Based on sta0s0cal analysis over all extracted rela0ons
•  Select a minimum score to be part of the Knowledge Base

•  Task: Automated crea0on of a Music Knowledge Base (KBSF)
•  Text corpora
–  Stories about 30k songs gathered from songfacts.com
•  Evalua0on
–  Quality evalua0on: 2 annotators in random rela0ons
–  Coverage evalua0on: Comparison between KBs
–  Applica0on evalua0on: Explaining Recommenda0ons

Experiment
Oramas S., Espinosa-Anke L., Sordo M., Saggion H. & Serra X. (2016). Informa0on Extrac0on for
Knowledge Base Construc0on in the Music Domain. Data & Knowledge Engineering, Volume
106, Pages 70-83.

Quality Evalua0on

Quality Evalua0on
Filtering

Quality Evalua0on
(Fader et al. 2011)

•  Number of rela0ons between en00es present in all KBs
Coverage Evalua0on
KBSF-th MusicBrainz DBpedia
#Rela>ons 3633 1535 1240

Norwegian Wood (The Beatles) -> Fourth Time Around (Bob Dylan)

Explaining Recommenda0ons

Norwegian Wood (The Beatles) -> Fourth Time Around (Bob Dylan)

Fourth Time Around was wriden in response to Norwegian Wood
by The Beatles, since it is similar, both melodically and lyrically.

•  User Experiment with 35 subjects
–  Recommenda0ons provided with diﬀerent types of explana0ons
–  Users rated recommenda0ons between 1 to 5
•  Results
–  Explana0ons using original sentences improve ra0ngs by 5%
with respect to recommenda0ons without explana0ons
–  Higher diﬀerences in ra0ngs on musically untrained subjects

•  Method for the crea0on of Music Knowledge Bases from
scratch with high precision and coverage

Conclusions

•  Useful for:
–  Crea0ng novel Knowledge Bases
–  Popula0on of exis0ng Knowledge Bases
–  Explaining music recommenda0ons
Conclusions

•  Useful for:
–  Crea0ng novel Knowledge Bases
–  Popula0on of exis0ng Knowledge Bases
–  Explaining music recommenda0ons
•  Novel ﬁltering, clustering, and scoring processes
Conclusions

Knowledge Graph

Seman0c Enrichment via En0ty Linking

Singers from Tennessee
American rockabilly musicians
1935 births
American country singer-songwriters
American country guitarists
Musicians from Atlanta
1967 singles
Jerry Reed songs
RPM Country Tracks number-one singles

Item
Descrip0on

Seman0c
Enrichment

Enriched
Graph
Enriched
Descrip0on

En0ty
Linking

Knowledge Base

Enriched Graph

Exploita>on vs. Explora>on
•  Exploita>on metrics: Precision@N, Recall@N, MAP

•  Explora>on metrics: Aggregated Diversity (ADiv@N)
Dis0nct items recommended across all users

Evalua0on Metrics

Ar0st Similarity
•  Recommenda0on without personaliza0on
•  Similarity between ar0st biographies
•  Knowledge Graphs vs. Text-based approach
•  Maximal Common Subgraph


Ar0st Similarity
•  Two Experiments (ar0st biographies from Last.fm):
–  MIREX: 188 ar0sts, human judgments
–  Last.fm API: 2,336 ar0sts, Last.fm similarity
MIREX
P@5
Last.fm API
P@5
Baseline: Text-based (LSA) 0.10 0.09
Rela0on Extrac0on Graph 0.06 0.06
Enriched Graph 0.14 0.16
Oramas S., Sordo M., Espinosa-Anke L., & Serra X. (2015). A Seman0c-based approach
for Ar0st Similarity. ISMIR 2015.

•  Seman>c enrichment via En0ty Linking to build an Enriched
Graph of every item
•  Embed each Enriched Graph into a feature vector
•  Recommend using a hybrid feature-combina0on approach
–  Train a linear model for each user to predict recommenda0ons
Music Recommenda0on Approach

•  En0ty-based embedding
•  Path-based embedding
Graph Embedding
ϕi = (w1e1 , w2e2 , ..., wtet )

Item Graph Feature vector
Weights (wi):
•  Distance to the root
•  Number of in-links
•  Frequency and inverse frequency

Hybrid feature-combina0on approach
Train a linear model for each user-i to predict feedback on unseen items
Graph features Collabora0ve features

•  Task: Music Recommenda0on
•  Datasets
–  Last.fm: song tags and stories (Songfacts)
•  Last.fm listening habits
•  8k items and 5k users
–  Freesound.org: sound tags and descrip0ons
•  Freesound downloads
•  21k items and 20k users
•  Support Vector Regression
•  Splits: 80% train - 20% test
Experiments
Oramas S., Ostuni V. C., Di Noia T., Serra, X., & Di Sciascio E. (2016). Music and Sound
Recommenda0on with Knowledge Graphs. ACM Transac0ons on Intelligent Systems and
Technology, Volume 8, Issue 2, Ar0cle 21.

Knowledge Graph

Results: Last.fm dataset
Approach P@10 ADiv@10
Collab 0.31 0.24
Collab + Tags 0.32 0.34
Collab + Enriched Graph 0.32 0.39
Enriched Graph only 0.11 0.70

Results: Freesound dataset
Approach P@10 ADiv@10
Collab 0.11 0.18
Collab + Tags 0.12 0.31
Collab + Enriched Graph 0.12 0.39
Enriched Graph only 0.05 0.67

Results
Approach Exploita>on Explora>on
Collab
Collab + Tags
Collab + Enriched Graph
Enriched Graph only

Results: Last.fm dataset
En0ty embedding
Path embedding

Results: Freesound dataset
En0ty embedding
Path embedding

Conclusions
•  Seman0c Enrichment via En0ty Linking promotes the
explora0on of long tail items

Conclusions
•  Collabora0ve features are fundamental to obtain good
ranking precision

Conclusions
ranking precision
•  The proposed hybrid feature-combina0on approach
promotes less popular items with high precision

Conclusions
ranking precision
•  The proposed hybrid feature-combina0on approach
promotes less popular items with high precision
•  The proposed approach outperforms state-of-the-art hybrid
and collabora0ve ﬁltering algorithms

Representa0on Learning with Deep Learning

Representa0on Learning with Deep Learning
Internal Representa0on

•  Recommenda0on of songs by novel ar0sts
•  Mul0modal: Audio + Text (ar0st biographies)
•  Representa0on Learning using Deep Neural Networks
•  Hybrid recommenda0on approach using Matrix Factoriza0on
Cold-start Music Recommenda0on

Divide & Conquer
The Beatles Let it be
The Beatles A day in the life
The Beatles Love me do
Song features

Divide & Conquer
The Beatles Let it be
The Beatles A day in the life
The Beatles Love me do
The Beatles
Song features
Ar0st features
Track features
Let it be
A day in the life
Love me do

1.  Aggregate feedback data by ar0st
Recommenda0on Approach

2.  Obtain latent factors through Matrix Factoriza0on

3.  Learn ar0st representa0ons from text

4.  Learn song representa0ons from audio


4.  Learn song representa0ons from audio
5.  Fusion of mul0modal representa0ons

Matrix Factoriza0on (WMF)
M =
Song Factors Users
Songs
User Factors
Songs
Users
d
d

Matrix Factoriza0on (WMF)
M =
Song Factors Users
Songs
User Factors
Songs
Users
d
d
R =
Users
Ar0sts
ru,a = Σ mu,t

Ar0st
Factors
User Factors
Users
d Ar0sts
d

Learning Approach

Text-based Approach: Enriched Descrip0on
Singers_from_Tennessee,American_ro
c k a b i l l y _ m u s i c i a n s ,
1935_births,American_country_singer
songwriters,American_country_guitari
sts,Musicians_from_Atlanta,20th-
century_American_male_actors,Gram
my_Award_winners
Item Descrip0on
Seman0c data
+
VSM
z-idf
Singer, songwriter and cer0ﬁed guitar
player Jerry Reed found his musical
calling as a child. It's interes0ng, albeit
a bit disconcer0ng, to hear Reed
singing so far outside his earthier
country sound, and the folk- and pop-
ﬂavored cuts haven't the swagger of
his blues. Elvis Presley covered Guitar
Man, with Reed reproducing the guitar
break from this recording.


Text-based Learning Approach

•  Constant-Q audio spectrograms with 96 frequency bins
•  4 convolu0onal and max pooling layers
•  Time domain ﬁlters (Van Den Oord et al. 2013)
•  No dense layers

Audio-based Learning Approach

•  l2-norm
•  Concatenate representa0ons
•  Linear model
Mul0modal Fusion

•  MSD-A
–  330k tracks
–  24k ar0sts
–  1M users
Dataset
Ar0sts biographies and tags
+
Audio and user feedback

•  Ar0st Recommenda0on
–  Text-based approaches
•  Song Recommenda0on
–  Audio, text, and mul0modal approaches
•  Splits: 80 train - 10 valida0on – 10 test
•  Diﬀerent ar>sts in each subset
•  Evalua0on metric: MAP@500
Experiments
Oramas, S., Nieto O., Sordo M., & Serra X. (2017). A Deep Mul0modal Approach for Cold-start
Music Recommenda0on. DLRS-RecSys 2017.

Results: Ar0st Recomenda0on
Input Approach MAP
Text VSM-FF 0.016
Enriched Text VSM-FF 0.020

Results: Ar0st Recommenda0on
Input Approach MAP
Text VSM-FF 0.016
Text (Kim 2014) w2v-CNN 0.015
Baseline: Text Random Forest 0.009

Input Approach MAP
Text VSM-FF 0.016
Tags VSM-FF 0.031
Baseline: Tags itemAdributeKnn 0.016

Input Approach MAP
Text VSM-FF 0.016
Tags VSM-FF 0.031
Baseline: Tags itemKnn 0.016
Random - 0.001
Upper-bound - 0.553

Results: Song Recommenda0on
Input Approach MAP
Audio CNN 0.0015
Ar>st Representa>on MLP 0.0034

Input Approach MAP
Audio CNN 0.0015
Ar0st Representa0on MLP 0.0034
Audio + Enriched Text CNN + VSM-FF 0.0014
Song Repr. + Ar>st Repr. MLP 0.0036

Input Approach MAP
Audio CNN 0.0015
Ar0st Representa0on MLP 0.0034
Audio + Enriched Text CNN + VSM-FF 0.0014
Song Repr. + Ar>st Repr. MLP 0.0036
Random - 0.0002
Upper-bound - 0.1649

•  Aggrega0on of ar0st data led to improved ar0st
representa0ons
Conclusions

representa0ons
•  Seman0c enrichment via En0ty Linking improves performance
of text-based recommenda0on
Conclusions

representa0ons
•  Seman0c enrichment via En0ty Linking improves performance
of text-based recommenda0on
•  Mul0modal fusion of data representa0ons improves single
modali0es in isola0on and fully mul0modal networks
Conclusions

Music Genre Classiﬁca0on

Music Genre Classiﬁca0on is a widely studied problem*

Music Genre Classiﬁca0on is a widely studied problem
but only with these characteris0cs:
-  Audio-based
-  HandcraZed features
-  Single-label
-  Few broad genres

Mul0modal
Learned features
Mul0-label
Hundreds of genres

Music Genre Classiﬁca0on is a widely studied problem
but only with these characteris0cs:
-  Audio-based
-  Single-label

•  MARD dataset
–  1300 albums
–  13 genres
–  Amazon reviews + Acous0cBrainz audio features
•  Features:
–  Textual: VSM of uni-grams and bi-grams
–  Seman>c: Enriched descrip0on (Wikipedia categories)
–  Acous>c: Low-level audio features
•  Classifier
–  SVM 5-fold cross valida0on
Single-label Music Genre Classifica0on
Oramas S., Espinosa-Anke L., Lawlor A., Serra X., & Saggion H. (2016). Exploring Music
Reviews for Music Genre Classifica0on and Evolu0onary Studies. ISMIR 2016.

Accuracy
Baseline: Text-based 62.9
Enriched Descrip>on 69.1
Text-based (Hu 2006) 55.0

Accuracy
Baseline: Text-based 62.9
Enriched Descrip>on 69.1
Text-based (Hu 2006) 55.0
Audio features 38.7

Audio Text

Logis0c output
Mul0-label Classiﬁca0on with Deep Learning
Output layer:
-  1 neuron per label
-  Sigmoid ac0va0on
-  Cross entropy loss
GENRE LABELS

Logis0c output
GENRE LABELS
Output layer:
-  1 neuron per label
-  Sigmoid ac0va0on
-  Cross entropy loss
Cons:
-  Assump0on of mutual
independence of labels
-  High dimensionality

PMI Factoriza0on (Chollet 2016)
Dimensionality reduc0on

Regression output
LATENT FACTORS
Output layer:
-  1 neuron per latent dim
-  Linear ac0va0on
-  Cosine proximity loss

•  MuMu Dataset
–  31k albums with
•  Cover art images
•  150k audio tracks
•  450k album reviews
–  Mul0-label genre annota0ons of 250 genres
Dataset
Oramas, S., Nieto O., Barbieri F., & Serra X. (2017). Mul0-label Music Genre Classiﬁca0on
from Audio, Text and Images Using Deep Features. ISMIR 2017.

•  Area under the ROC curve for every genre (AUC)
•  Aggregated diversity (ADiv@N) of genre predic0ons
Diﬀerent genres in Top-N lists / Total number of genres
Evalua0on metrics

•  Constant-Q spectrograms with 96 frequency bins
•  4 convolu0onal layers
•  No dense layers

Audio-based approach

•  Filter sizes
–  3x3 (Choi 2016)
–  4x96 (Van Den Oord 2013)
–  4x70 (Pons 2016)
•  Number of ﬁlters
–  HIGH: 256/512/1024/1024
–  LOW: 64/128/128/64
•  Output layer
–  GENRE LABELS
–  LATENT FACTORS
Audio-based approach

Audio-based classiﬁca0on results
Target Input AUC ADiv@1
GENRE LABELS Baseline: Audio features 0.792 0.04
GENRE LABELS CQT + CNN 0.871 0.05

Audio-based classiﬁca0on results
GENRE LABELS Baseline: Audio features 0.792 0.04
GENRE LABELS CQT + CNN 0.871 0.05
LATENT FACTORS CQT + CNN 0.888 0.35

Qualita0ve Analysis
LATENT FACTORS LABELS

Text-based Approach

Text-based classiﬁca0on results
GENRE LABELS Text 0.905 0.08
GENRE LABELS Enriched Text 0.916 0.10

Text-based classiﬁca0on results
GENRE LABELS Text 0.905 0.08
GENRE LABELS Enriched Text 0.916 0.10
LATENT FACTORS Enriched Text 0.917 0.42

Informa0on Gain
Rock Pop Metal Hip Hop Country
band song metal hip country
rock songs death hop Nashville
punk euro band rap her
bands trade black rhymes Waylon

•  Deep Residual Networks (ResNets)
•  101 layers
•  Pretrained on ImageNet
•  Fine-tuning in our task
Image-based approach

Mul0modal classiﬁca0on results
Modality AUC
Audio 0.888
Text 0.917
Images 0.743

Modality AUC
Audio 0.888
Text 0.917
Images 0.743
A + T 0.930
A + I 0.900
T + I 0.921

Modality AUC
Audio 0.888
Text 0.917
Images 0.743
A + T 0.930
A + I 0.900
T + I 0.921
A + T + I 0.936

t-SNE of visual features
Qualita0ve analysis

t-SNE of visual features
Qualita0ve analysis
Features:

Color faces
Hair
Background
Clothes
Instruments
Typographies

Qualita0ve analysis: Aden0on heatmaps
RnB Pop RnB Electronic
Country Country Pop Folk
Jazz Jazz Blues Reggae

•  Representa0on learning beder than handcraZed features
Conclusions

•  Seman0c enrichment improves text classiﬁca0on
Conclusions

•  Text achieves best single modality results
Conclusions

•  Audio is nearer Text performance thanks to deep learning
Conclusions

•  Fusion of learned data representa0ons improves results
Conclusions

•  Fusion of learned data representa0ons improves results
•  Dimensionality reduc0on yields beder accuracy and diversity
Conclusions


•  Knowledge from seman0c repositories incorporated via En0ty
Linking improves item proﬁles -> higher diversity (long tail)



•  Learning and combining data representa0ons from
mul0modal data -> accurate cold-start recommenda0ons



mul0modal data -> accurate cold-start recommenda0ons
Hybrid approaches
+
Seman0c Enrichment
+
Representa0on learning

beder
Long tail

Cold-start


Linking improves item descrip0ons -> higher accuracy


mul0modal data -> higher accuracy and diversity


-  Audio-based
-  Single-label
Mul0modal
Learned features
Mul0-label
Hundreds of genres

mul0modal data -> higher accuracy and diversity

•  Approach for the automated crea0on of Music KBs
Contribu0ons

•  Methodology for the seman0c enrichment of text
Contribu0ons

•  Hybrid and knowledge-based recommenda0on approach that
promotes long tail recommenda0ons
Contribu0ons

•  Mul0modal deep learning approach for cold-start
recommenda0ons
Contribu0ons

•  Mul0modal deep learning approach for cold-start
recommenda0ons
•  Mul0modal deep learning approach for mul0-label music
genre classiﬁca0on
Contribu0ons

•  Development of data-driven methodologies that have the
poten0al to help musicologists to discover new hypothesis
from text
–  Relevance of ar0sts
–  Diachronic studies of tendencies and evolu0on of genres
Computa0onal Musicology

•  Development of data-driven methodologies that have the
poten0al to help musicologists to discover new hypothesis
from text
–  Relevance of ar0sts
–  Diachronic studies of tendencies and evolu0on of genres
•  Crea0on of a ﬂamenco music Knowledge Base
Computa0onal Musicology

•  Crea0on of the “Music meets NLP” research project
–  Promo0on of intersec0on of MIR and NLP communi0es
–  High number of publica0ons
–  Release of several linguis0c resources
–  Organiza0on of tutorials and a challenges
Outcomes

Outcomes

Outcomes
hdp://musicbrainz.org/release-group/2536a41d-fde9-35d5-a6c6-cd4d94ﬀd916
hdp://musicbrainz.org/ar0st/9472e6e4-3e13-430a-900d-6f075720a5c6

•  6 Datasets
–  ELMD
•  Task: Music En0ty Linking
•  Content: Annotated documents
–  MARD
•  Task: Music Genre Classiﬁca0on
•  Content: Customer reviews and acous0c features
–  SAS
•  Task: Ar0st Similarity
•  Content: Ar0st biographies and similarity ground truth
–  KG-Rec
•  Task: Sound and Music Recommenda0on
•  Content: User feedback, item descrip0ons and tags
Reproducibility

–  MSD-A
•  Task: Music Recommenda0on
•  Content: User feedback, ar0st biographies and tags, audio tracks
–  MuMu
•  Task: Mul0-label Music Genre Classiﬁca0on
•  Content: Customer reviews, audio tracks, album cover art
•  Sogware
–  ELVIS
•  En0ty Linking Integra0on System
–  TARTARUS
•  Mul0modal deep learning framework for recommenda0on and classiﬁca0on
Reproducibility

•  5 Peer-reviewed journals
–  Oramas S., Espinosa-Anke L., & Serra X. (Submided). Knowledge Extrac0on for
Musicology. Journal of New Musical Research.
–  Oramas S., Barbieri F., Nieto O., Serra X. (Submided). Learning and Combining
Mul0modal Data Representa0ons for Music Genre Classiﬁca0on. Transac0ons of the
Interna0onal Society for Music Informa0on Retrieval.
–  Oramas S., Espinosa-Anke L., Sordo M., Saggion H. & Serra X. (2016). Informa0on
Extrac0on for Knowledge Base Construc0on in the Music Domain. Data & Knowledge
Engineering, Volume 106, Pages 70-83.
–  Oramas S., Ostuni V. C., Di Noia T., Serra, X., & Di Sciascio E. (2016). Music and Sound
Recommenda0on with Knowledge Graphs. ACM Transac0ons on Intelligent Systems and
Technology, Volume 8, Issue 2, Ar0cle 21.
–  Oramas S., & Sordo M. (2016). Knowledge is Out There: A New Step in the Evolu0on of
Music Digital Libraries. Fontes Ar0s Musicae, Vol 63, no. 4.

Publica0ons

•  14 Peer-reviewed conference papers
–  Oramas, S., Nieto O., Barbieri F., & Serra X. (2017). Mul0-label Music Genre
Classiﬁca0on from Audio, Text and Images Using Deep Features. ISMIR 2017.
–  Oramas, S., Nieto O., Sordo M., & Serra X. (2017). A Deep Mul0modal Approach for
Cold-start Music Recommenda0on. DLRS-RecSys 2017.
–  Espinosa-Anke, L., Oramas S., Saggion H., & Serra X. (2017). ELMDist: A vector space
model with words and MusicBrainz en00es. ESWC 2017.
–  Oramas S., Espinosa-Anke L., Lawlor A., Serra X., & Saggion H. (2016). Exploring Music
Reviews for Music Genre Classiﬁca0on and Evolu0onary Studies. ISMIR 2016.
–  Oramas S., Espinosa-Anke L., Sordo M., Saggion H., & Serra X. (2016). ELMD: An
Automa0cally Generated En0ty Linking Gold Standard in the Music Domain. LREC 2016.
–  Espinosa-Anke, L., Oramas S., Camacho-Collados J., & Saggion H. (2016). Finding and
Expanding Hypernymic Rela0ons in the Music Domain. CCIA 2016.
Publica0ons

–  Oramas S., Sordo M., Espinosa-Anke L., & Serra X. (2015). A Seman0c-based approach
for Ar0st Similarity. ISMIR 2015.
–  Oramas S., Gómez F., Gómez E., & Mora J. (2015). FlaBase: Towards the crea0on of a
Flamenco Music Knowledge Base. ISMIR 2015.
–  Ostuni V. C., Oramas S., Di Noia T., Serra, X., & Di Sciascio E. (2015). A Seman0c Hybrid
Approach for Sound Recommenda0on. WWW 2015.
–  Oramas S., Sordo M., & Espinosa-Anke L. (2015). A Rule-based Approach to Extrac0ng
Rela0ons from Music Tidbits. KET-WWW 2015.
–  Sordo, M., Oramas S., & Espinosa-Anke L. (2015). Extrac0ng Rela0ons from
Unstructured Text Sources for Music Recommenda0on. NLDB 2015.
–  Oramas S., Sordo M., & Serra X. (2014). Automa0c Crea0on of Knowledge Graphs from
Digital Musical Document Libraries. CIM 2014.
–  Oramas S. (2014). Harves0ng and Structuring Social Data in Music Informa0on Retrieval.
ESWC 2014.
–  Font, F., Oramas, S., Fazekas, G., & Serra, X. (2014). Extending Tagging Ontologies with
Domain Speciﬁc Knowledge. ISWC 2014.
Publica0ons

•  Other conference presenta>ons
–  Oramas, S. (2017). Knowledge Extrac0on and Feature Learning for Music
Recommenda0on in the Long Tail. 5th Large Scale Recommenda0on Systems Workshop,
co-located with RecSys 2017, Como, Italy.
–  Oramas, S. (2017). Discovering Similari0es and Relevance Ranking of Renaissance
Composers. The 63rd Annual Mee0ng of the Renaissance Society of America (RSA),
Chicago.
–  Oramas S. (2015). Informa0on Extrac0on for the Music Domain. The 2nd Interna0onal
Workshop on Human History Project: Natural Language Processing and Big Data,
CIRMMT, Montreal.
–  Oramas, S., & Sordo M. (2015). Knowledge Acquisi0on from Music Digital Libraries. The
Interna0onal Associa0on of Music Libraries and Interna0onal Musicological Society
Conference (IAML/IMS 2015), New York.
Publica0ons

•  Tutorials and Challenges
–  Camacho-Collados J., Delli Bovi C., Espinosa-Anke L., Oramas S., Pasini T., Shwartz V.,
Santus E., Saggion H., Navigli R. (In press) Task 9: Hypernym Discovery. SemEval 2018.
–  Speck R., Röder M., Oramas S., Espinosa-Anke L., & Ngomo A. C. N. (2017). Open
Knowledge Extrac0on Challenge 2017. ESWC 2017.
–  Oramas S., Espinosa-Anke L., Zhang S., Saggion H., & Serra X. (2016). Natural Language
Processing for Music Informa0on Retrieval. ISMIR 2016.
•  Prizes
–  Best oral presenta0on award at ISMIR 2017
–  Best paper award at SemDeep-ESWC 2017
–  Best poster award at CCIA 2016
–  Maria de Maeztu Research reproducibility award 2016
Publica0ons

Acknowledgements

PhD Thesis: Knowledge Extraction and Representation Learning for Music Recommendation and Classification

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PhD Thesis: Knowledge Extraction and Representation Learning for Music Recommendation and Classification