KNOWLEDGE AND CONTENT-BASED AUDIO RETRIEVAL USING
WORDNET
Pedro Cano and Markus Koppenberger and Sylvain Le Groux
and Perfecto Herrera and Julien Ricard and Nicolas Wack
Institut de l’Audiovisual, Universitat Pompeu Fabra
c/Ocata 3, 08003 Barcelona, Spain
Keywords:
MPEG7, content-based audio, sound effects, ontology management, WordNet, audio classification, audio asset
management
Abstract:
Sound producers create the sound that goes along the image in cinema and video productions, as well as
spots and documentaries. Some sounds are recorded for the occasion. Many occasions, however, require
the engineer to have access to massive libraries of music and sound effects. Of the three major facets of
audio in post-production: music, speech and sound effects, this document focuses on sound effects (Sound
FX or SFX). Main professional on-line sound-fx providers offer their collections using standard text-retrieval
technologies. Library construction is an error-prone and labor consuming task. Moreover, the ambiguity and
informality of natural languages affects the quality of the search. The use of ontologies alleviates some of
the ambiguity problems inherent to natural languages, yet it is very complicated to devise and maintain an
ontology that account for the level of detail needed in a production-size sound effect management system.
To address this problem we use WordNet, an ontology that organizes over 100.000 concepts of real world
knowledge: e.g: it relates doors to locks, to wood and to the actions of opening, closing or knocking. However
a fundamental issue remains: sounds without caption are invisible to the users. Content-based audio tools offer
perceptual ways of navigating the audio collections, like “find similar sound”, even if unlabeled, or query-by-
example, possibly restricting the search to a semantic subspace, such as “vehicles”. The proposed content-
based technologies also allow semi-automatic sound annotation. We describe the integration of semantically-
enhanced management of metadata using WordNet together with content-based methods in a commercial
sound effect management system.
1 INTRODUCTION
The audio component is a fundamental aspect in an
audiovisual production. Around 75% of the sound ef-
fects (SFX) of a movie are added at post-production.
Many sounds are useless due to the noise in the
recording session and some are simply not picked up
by the production microphones. Sometimes sounds
are replaced in order to improve the dramatic impact,
e.g.: arrow sounds of the “Lord of the Rings” are re-
placed by “whooshes”. There are also artistic rea-
sons, for example, in the movie “All the President’s
Men”, in order to strengthen the message that the pen
is mightier that the sword, the typewriter keys sounds
were mixed with the sound of gunfire(Weis, 1995).
Many occasions, not only movies but also com-
puter games, audio-visual presentations, web-sites re-
quire sounds. These sounds can be recorded as well
as recreated using foley techniques—for the sound of
the knife entering the body in Psycho’ shower scene,
Hitchcock used a melon (Weis, 1995). Another pos-
sibility is the use of already compiled SFX libraries.
Accessing library sounds can be an interesting alter-
native to sending a team to record sounds or to recre-
ate them in a studio because one needs then a foley pit
and the rest of the recording equipment.
A number of SFX providers, for example:
www.sounddogs.com, www.sonomic.com or
www.sound-effects-library.com, offer SFX on-
line. The technology behind these services is
standard text-search. Librarians tag sounds with de-
scriptive keywords that the users may search. Some
companies also keep categories—such as “automo-
biles”, “horror” or “crashes”—to ease the interaction
with the collections. This approach presents several
limitations. The work of the librarian is error-prone
and a very time-consuming task. Solutions have
been proposed to manage media assets from a audio
301
Cano P., Koppenberger M., Le Groux S., Herrera P., Ricard J. and Wack N. (2004).
KNOWLEDGE AND CONTENT-BASED AUDIO RETRIEVAL USING WORDNET.
In Proceedings of the First International Conference on E-Business and Telecommunication Networks, pages 301-308
DOI: 10.5220/0001397503010308
Copyright
c
 SciTePress

content-based audio perspective, both from the
academia and the industry (see Section 2). However
none seems to have impacted in professional sound
effects management systems. Another source of
problems is due to the imprecision and ambiguity
of natural languages. Natural languages present
polysemy—“bike” can mean both “bicycle” and
“motorcycle”—and synonymy—both “elevator” and
“lift” refer to the same concept. This, together with
the difficulty associated to describing sounds with
words, affects the quality of the search. The user has
to guess how the librarian has labeled the sounds and
either too many or too few results are returned.
In this context we present a SFX retrieval system
that incorporates content-based audio techniques and
semantic knowledge tools implemented on top of one
of the biggest sound effects providers database. The
rest of the paper is organized as follows: in Section 2
we review what what existing literature proposes to
improve sound effect management. From Section 3
to 5 we describe the implemented enhancements of
the system.
2 RELATED WORK
Related work to sound effect management falls into
three categories: Content-based audio technologies,
approaches to describe sound events and taxonomy
management.
2.1 Content-based audio
classification and retrieval
Content-based functionalities aim at finding new
ways of querying and browsing audio documents as
well as automatic generating of metadata, mainly
via classification. Query-by-example and simi-
larity measures that allow perceptual browsing of
an audio collection is addressed in the literature
and exist in commercial products, see for instance:
www.findsounds.com, www.soundfisher.com.
Existing classification methods normally concen-
trate on small domains, such as musical instrument
classification or very simplified sound effects tax-
onomies. Classification methods cannot currently of-
fer the detail needed in commercial sound effects
management, e.g: “female steps on wood, fast”. In
audio classification, researchers normally assume the
existence or define a well defined hierarchical classifi-
cation scheme of a few categories (less than a hundred
at the leaves of the tree). On-line sound effects and
music sample providers have several thousand cate-
gories. For further discussion on classification of gen-
eral sound, we refer to (Cano et al., 2004a).
2.2 Description of Audio
Sounds are multifaceted, multirepresentional and usu-
ally difficult to describe in words. MPEG-7 offers
a framework for the description of multimedia doc-
uments, see (Manjunath et al., 2002). MPEG-7 con-
tent semantic description tools describe the actions,
objects and context of a scene. In sound effects, this
correlates to the physical production of the sound in
the real world, “moo cow solo”, or the context, “Air-
port atmos announcer”.
MPEG-7 content structure tools concentrate on the
spatial, temporal and media source structure of mul-
timedia content. Indeed, important descriptors are
those that describe the perceptual qualities indepen-
dently of the source and how they are structured on
a mix. Since they refer to the properties of sound,
e.g: Loudness, brightness. Other important search-
able metadata are post-production specific descrip-
tions, e.g.: horror, comic or science-fiction. Creation
metadata describe how the sound was recorded. For
example, to record a car door closing one can place
the microphone in the interior or in the exterior. Some
examples of such descriptors are: interior, exterior,
close-up, live recording, programmed sound, studio
sound, treated sound. For a more complete review on
SFX description, we refer to (Cano et al., 2004b).
Figure 1: Snapshot of the vehicle taxonomy in WordNet
2.0. Only the hypernym type of relation is displayed.
2.3 Taxonomy Management
The use of taxonomies or classification schemes al-
leviates some of the ambiguity problems inherent
to natural languages, yet they pose others. It is
very complicated to devise and maintain classification
schemes that account for the level of detail needed in
a production-size sound effect management system.
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The MPEG-7 standard provides description mecha-
nisms and ontology management tools for multime-
dia documents (Manjunath et al., 2002). (Celma and
Mieza, 2004) show an opera information system that
exploits taxonomies built with classification schemes
using MPEG-7 framework. However, it is very com-
plicated to extend them to the level of detail needed in
a production-size sound effect management system.
We have found that it is much faster to start develop-
ing ontologies on top on a semantic network such as
WordNet rather than starting from scratch.
WordNet (Miller,
1995)/(http://www.cogsci.princeton.edu/ wn/) is
a lexical network designed following psycholin-
guistic theories of human lexical memory. Standard
dictionaries organize words alphabetically. WordNet
organizes concepts in synonym sets, synsets, with
links between the concepts like: broader sense,
narrower sense, part of, made of and so on. It knows
for instance that the word “piano” has two senses,
the musical attribute that refers to “low loudness”
and the “musical instrument”. It also encodes the
information that a grand piano is a type of piano,
and that it has parts such us a keyboard, a loud
pedal and so on. Such a knowledge system is
useful for retrieval. It can for instance display the
results of a query “car” in types of cars, parts of
car, actions of a car (approaching, departing, turning
off). Figure 1 displays a subset of the “vehicle”
graph from WordNet 2.0. The usefulness of such
knowledge systems has been justified for image
retrieval in (Aslandogan et al., 1997) and in general
multimedia asset management (Flank, 2002).
3 SYSTEM OVERVIEW
Text-based and content-based methods alone do not
seem to suffice for a complete interaction with vast
sound effects repositories. In the implemented sys-
tem we aim to combine the best of two worlds to offer
tools for the users to refine and explore a huge collec-
tion of audio. Similar work on integrating perceptual
and semantic information in a more general multi-
media framework is MediaNet (Benitez et al., 2000).
The system we present is specialized for SFX. The
current prototype uses 80.000 sounds from a major
on-line sound effects provider: www.sound-effects-
library.com. Sounds come with a textual description
which has been disambiguated with the augmented
WordNet ontology.
3.1 Functional blocks
The system has been designed to ease the use of dif-
ferent tools to interact with the audio collection and
with speed as a major design issue. On top of these
premises we have implemented the blocks of Fig. 2.
The sound analysis, audio retrieval and metadata gen-
eration blocks are described in Section 4. The text re-
trieval, text processor and knowledge manager blocks
are described in Section 5.
Figure 2: Functional Block Diagram of the System.
3.2 System architecture
The audio processing engines use the CLAM Frame-
work, a C++ Library for Audio and Music, devel-
oped at the MTG and distributed under GNU/GPL
License (http://www.iua.upf.es/mtg/clam). The on-
tology management and integration of different parts
is done with Perl and a standard relational database
management system. The functionality is avail-
able via a web interface and exported via SOAP
(http://www.w3.org/TR/soap). The SOAP interface
provides some exclusive functionality—such as inter-
action with special applications, e.g.: Sound editors
and annotators— which is not available via the web
interface. See Figure 3 for a diagram of the architec-
ture.
4 CONTENT-BASED AUDIO
TOOLS
Content-based audio tools ease the work of the librar-
ian and enhance the search possibilities for the user.
It simplifies the labeling of new sounds because many
keywords are automatically presented to the librar-
ian. To achieve it, the new sound is compared to
the collection with Nearest Neighbor search and the
text associated with the similar matches is presented
to the librarian. The sound analysis module (see Fig-
ure 2), besides extracting sound descriptors used for
the similarity search, generates searchable descriptors
KNOWLEDGE AND CONTENT-BASED AUDIO RETRIEVAL USING WORDNET
303

Data Storage Application Server
Web Clients
SOAP
Interface
Web
Server
SOAP Clients
(e.g. Audacity, ProTools-Plugin, ...)
Knowledge
Manager
XML / XSLT
Transformation
HTTP Request
+ Access to audio and
meta data
+ Browsing
+ Search tools
(similarity, query-by-
example, ...)
+ Audio converter
+ Meta data extractors
+ Algorithms
+ Data Management
(adding of audio & meta-
data, modifications)
+ Ontology management,
+ User management
and access rights
+ Usage statistic (views,
searches, downloads, ...)
Functionalities
Objects repre-
senting Audio
and Meta Data
Relational DBMS
(Meta data)
User Clients
File System
Audio data and
additional meta data
(e.g. XML-files)
Data
Manager
Researcher /
Developer
Librarian
Web Client /
other GUIs
(i.e. Mozilla, Internet Explorer)
User
Manager
(examples)
Figure 3: System Architecture
as those detailed in Subsection 4.2 (crescendo, noisy,
etc.). Content-based tools offer the user functionali-
ties such as:
Virtual Foley Mode: Find perceptually similar
sounds. A user may be interested in a glass crash
sound. If none of the retrieved sounds suits him, he
can still browse the collection for similar sounds
even if produced by different sources, even if unla-
beled.
Clustering of sounds: Typically a query like
“whoosh” may retrieve several hundred results.
These results are clustered and only one represen-
tative of each class is displayed to the user. The
user can then refine the search more easily.
Morphological Descriptors: Another option when
the list of results is too large to listen to is filter-
ing the results using morphological descriptors (see
Section 4.2).
Query by example: The user can provide an example
sound or utter himself one as a query to the system,
possibly restricting the search to a semantic sub-
space, such as “mammals”.
4.1 Similarity Distance
The similarity measure is a normalized Manhat-
tan distance of features belonging to three different
groups: a first group gathering spectral as well as tem-
poral descriptors included in the MPEG-7 standard; a
second one built on Bark Bands perceptual division
of the acoustic spectrum and which outputs the mean
and variance of relative energies for each band; and,
finally a third one, composed of Mel-Frequency Cep-
stral Coefficients and their corresponding variances
(see (Cano et al., 2004a) for details):
d (x, y) =
N
X
k=1
|x
k
− y
k
|
(max
k
− min
k
)
where x and y are the vectors of features, N the
dimensionality of the feature space, and max
k
and
min
k
the maximum and minimum values of the kth
feature.
The similarity measure is used for metadata genera-
tion: a sound sample will be labeled with the descrip-
tions from the similar sounding examples of the anno-
tated database. This type of classification is known as
one-nearest neighbor decision rule (1-NN)(Jain et al.,
2000). The choice of a memory-based nearest neigh-
bor classifier avoids the design and training of every
possible class of sound which is of the order of sev-
eral thousands. Besides, it does not need redesign or
training whenever a new class of sounds is added to
the system. The NN classifier needs a database of la-
beled instances and a similarity distance to compare
them. An unknown sample will borrow the metadata
associated with the most similar registered sample.
The similarity measure is also used for the query-
by-example and to browse through “perceptually”
generated hyperlinks.
4.2 Morphological Sound
Description
The morphological sounds descriptor module extracts
a set of descriptors that focused on intrinsic percep-
tual qualities of sound based on Schaeffer’s research
on sound objects (Schaeffer, 1966). The extractor of
morphological descriptors (Ricard and Herrera, 2004)
currently generates the following metadata:
Pitchness: (Organization within the spectral dimen-
sions) Pitch, Complex and Noisy.
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Dynamic Profile: (Intensity description) Unvarying,
Crescendo, Decrescendo, Delta, Impulsive, Itera-
tive, Other.
Pitchness Profile: (Temporal evolution of the inter-
nal spectral components) Varying and Unvarying
Pitch Profile: (Temporal evolution of the global
spectrum) Undefined, Unvarying, Varying Contin-
uous, Varying Stepped.
Figure 4: Morphological descriptor filtering. The iterative
dynamic profile allows to discriminate between snare sam-
ples and loops
Figure 5: Morphological description filtering. The impul-
sive dynamic profile allows to discriminate violin pizzicati.
These descriptors can be used to retrieve abstract
sounds as well as refine other types of searches. Be-
sides applying to all types of sounds, the use of an
automatic extractor avoids expensive human labeling
while it assures consistency. For details on the con-
struction and usability evaluation of the morphologi-
cal sound description we refer to (Ricard and Herrera,
2004).
4.3 Clustering and Visualization
Tools
Usually, systems for content-based retrieval of sim-
ilar sounds output a list of similar sounds ordered
by increasing similarity distance. The list of re-
trieved sounds can rapidly grow and the search of
the appropriate sound becomes tedious. There is a
need for a user-friendly type of interface for brows-
ing through similar sounds. One possibility for avoid-
ing having to go over, say 400 gunshots, is via clus-
tering sounds into perceptually meaningful subsets,
so that the user can choose what perceptual cate-
gory of sound he or she wishes to explore. We
used a hierarchical tree clustering with average link-
age algorithm and the above mentioned similarity dis-
tance (Jain et al., 2000). Another possibility of in-
teraction with the sounds is using visualization tech-
niques, specifically Multidimensional scaling (MDS),
self-organizing maps (SOM) or FastMap (Cano et al.,
2002), to map the audio samples into points of an Eu-
clidean space. Figure 6 displays a mapping of the au-
dio samples to a 2D space. In the example it is possi-
ble to distinguish different classes of cat sounds, e.g.:
“purring”, “hissing” and “miaow” sounds.
5 NATURAL LANGUAGE
PROCESSING AND
KNOWLEDGE MANAGER
This module enhances existing text-search engines
used in sound effects retrieval systems. It eases the li-
brarian work and it simplifies the management of the
categories.
• There is a lemmatizer, say “bikes” becomes “bike”,
an inflecter that allows to expand it to “bike, bikes
and biking”, and a named entity recognition mod-
ule, that is able to identify “Grand piano” as a spe-
cific type of piano.
• Module for the phonetic matching, e.g:
“whoooassh” retrieves “whoosh”. Phonetic
matching is used in information retrieval to
account for the typo errors in a query.
• Higher control on the precision and recall of the re-
sults using WordNet concepts. The query “bike”
returns both “bicycle” and “motorcycle” sounds
and the user is given the option to refine the search.
• Proposal of related terms. It is generally accepted
that recognition is stronger than recall. A user may
not know how the librarian tagged a sound. Word-
Net can be used to propose alternative search terms.
• Proposal of higher level related term not included
in the lexical network. WordNet does not have
all possible relations. For instance, “footsteps in
mud”, “tractor”, “cow bells” and “hens” may seem
related in our minds when we think of farm sounds
but do not have direct links within WordNet. It
is possible to recover this type of relations be-
cause there are many sounds that have been la-
beled with the concept “farm”. Studying the co-
occurrence of synsets allows the system to infer re-
lated terms (Banerjee and Pedersen, 2003).
For further details on the implementation and evalua-
tion of WordNet as backbone for sound effect ontol-
ogy management, we refer to (Cano et al., 2004b).
KNOWLEDGE AND CONTENT-BASED AUDIO RETRIEVAL USING WORDNET
305

Figure 6: FastMap visualization screenshot. The points of the 2D map refer to different audio samples. The distances on
the euclidean space try to preserve distances in the hyper-dimensional perceptual space defined by the similarity distance of
subsection 4.1
6 EXPERIMENTS
We have used 40.000 sounds from the Sound-Effects-
Library (http://www.sound-effects-library.com) for
the experiments. These sounds have been unambigu-
ously tagged with concepts of an enhanced Word-
Net. Thus a violin sound with the following cap-
tion:“Concert Grand Piano - piano” may have the fol-
lowing synsets (the numbers on the left are the unique
WordNet synset identifiers):
• 02974665%n concert grand, concert piano – (a
grand piano suitable for concert performances)
• 04729552%n piano, pianissimo – ((music) low
loudness)
6.1 Experimental setup
The evaluation of similarity distances is a tricky sub-
ject. Perceptual listening tests are expensive. Another
possibility is to evaluate the goodness of the similar-
ity measure examining the performance in a Nearest
Neighbor (NN) classification task. As we will see in
6.3, the overlap between semantic and perceptual tax-
onomies complicates the evaluation. In musical in-
struments, the semantic taxonomy more or less fol-
lows an acoustic classification scheme, basically due
to the physical construction, and so instruments are
wind (wood and brass), string (plucked or bowed) and
so on (Herrera et al., 2003).
We have tried three ways of assessing the percep-
tual similarity between sounds:
• Perceptual listening experiments
• Classification or metadata generation performance
• Consistency on the ranking and robustness to dis-
tortions such as resampling, transcoding (convert-
ing to MP3 format at different compression rates
and back). The harmonic instruments have been
transcoded and resampled into WAV PCM format
and Ogg format (www.vorbis.com). The classifica-
tion accuracy dropped from 92% to 71.5% in the
worst case.
6.2 Perceptual listening tests
In order to test the relevance of our similarity mea-
sures, we asked users of our system to give a per-
sonal perceptual evaluation on the retrieval of sounds
by similarity. This experiment was accomplished on
20 users who chose 41 different queries, and pro-
duced 568 evaluations on the relevance of the simi-
lar sound retrieved. During the evaluation, the users
were presented with a grading scale from 1—not sim-
ilar at all—to 5—closely similar. The average grade
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Table 1: The classifier assigns the metadata of the sounds of the second column to the sounds of the first.
Query Sound Caption Nearest-neighbor Caption
1275cc Mini Cooper Door Closes Interior Perspective Trabant Car Door Close
Waterfall Medium Constant Extremely Heavy Rain Storm Short Loop
M-domestic Cat- Harsh Meow A1v:Solo violin (looped)
Auto Pull Up Shut Off Oldsmobile Cutlass Ferrari - Hard Take Off Away - Fast
was 2.6. We have at our disposal the semantic con-
cepts associated with the 40.000 sounds used in the
experiment. It turned out that the semantic class of
a sound is crucial in the user’s sensation of similar-
ity. The conclusion of our experiment is that the users
gave better grades to retrieved sounds that are from
the same semantic class as the query sound (40%
of the best graded sounds belonged to the same se-
mantic class). In the prototype, in addition to the
purely content-based retrieval tools, the use of the
knowledge-based tools allows searches for similar
sounds inside a specific semantic family.
6.3 Metadata annotation
performance
The first experiment on metadata annotation perfor-
mance consisted in finding a best-match for all the
sounds in the database. Table 1 shows some exam-
ples: on the left column the original caption of the
query sound and on the right the caption of the nearest
neighbor. The caption on the right would be assigned
to the query sound in an automatic annotation system.
As can be inferred from table 1 it is difficult to
quantitatively evaluate the performance of the system.
An intersection on the terms of the captions would not
yield a reasonable evaluation metric. The WordNet
based ontology can inform us that both “Trabant” and
“Mini Cooper” are narrow terms for the concept“car,
automobile”. Thus, the comparison of the number of
common synsets on both query and nearest-neighbor
could be used as a better evaluation. The number
of concepts (synsets) that the sound in the database
and their best match have in common was bigger than
one—at least one synset—half of the time. Yet there
are many cases when this metric is not appropriate
for similarity evaluation. The intersection of source
descriptions can be zero for very similar sounding
sounds. The closest-match for a “paper bag” turns out
to be a “eating toast”. These sounds are semantically
different but perceptually equivalent. The ambiguity
is a disadvantage when designing and assessing per-
ceptual similarity distances.
In a second experiment we have tested the gen-
eral approach in reduced domain classification regime
mode: percussive instruments, harmonic instruments
and we achieve acceptable performances. The as-
sumption is that there is a parallelism between se-
mantic and perceptual taxonomies in musical instru-
ments. The psychoacoustic studies of (Lakatos, 2000)
revealed groupings based on the similarities in the
physical structure of instruments. We have therefore
evaluated the similarity with classification on the mu-
sical instruments space, a subspace of the universe of
sounds.
In the 6 class percussive instrument classification
we achieve a 85% recognition (955 audio files) using
10 fold validation. The results for a 8 class classifi-
cation of harmonic instruments is a 77.3% (261 audio
files). We refer to (Cano et al., 2004a) for further dis-
cussion on general sound similarity and classification.
7 SUMMARY
We have introduced the difficulties inherent in inter-
acting with sound effect repositories, both for the li-
brarian who designs such content repositories and for
potential users who access this content. We have
presented several technologies that enhance and fit
smoothly into professional sound effects providers
working processes. Several content-based audio tools
have been integrated providing possibilities of access-
ing sounds which are unrelated from the text caption
but sound the same—even if they are unlabeled. Sev-
eral natural language processing tools have also been
described. WordNet, previously proposed for multi-
media retrieval has been extended for sound effects
retrieval.
The system can be accessed and evaluated at
http://www.audioclas.org.
ACKNOWLEDGEMENTS
We thank the staff from the Tape Gallery for all the
support, discussion and feedback. This work is de-
veloped under a EU E! 2668 Eureka AudioClas. We
thank Oscar Celma and Fabien Gouyon for the review.
KNOWLEDGE AND CONTENT-BASED AUDIO RETRIEVAL USING WORDNET
307

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