A Collection of Tools to Search, Analyze and Collect Audio Files in a LAN
and in the Internet
Sergio Cavaliere, Carmine Colucci
Università di Napoli “Federico II”
Dipartimento di Scienze Fisiche
Via Cinthia 80129 Napoly (Italy)
Keywords: Multimedia Databases, Audio Browsing.
Abstract: In this paper we present a toolbox aimed to search for audio files on the Internet, in a Local Area Network
or in a single computer. Search is finalized both to analyze the collected files and to populate a multimedia
archive for further use or analysis. The related tools to interface to a multimedia Data Base and analyze
files is also provided. The toolbox is intended to be open in the sense that any user may customize it at will
adding proprietary tools and methods. It is freely distributed and also open to contributions. The goal has
been achieved building a Matlab Toolbox; this, as is well known, results in an open environment that
anybody may customize at will. Research in the field of music and sound browsing analysis and
classification is a large and open field in which a large amount of different solutions have been proposed in
the literature. Deciding which sound parameters are suited to a kind of search or classification is still an
open problem: we are therefore providing an open environment where anybody may customize at will tools
and methods, an environment which, as a plus respect to other tools in the literature, starts from the very
first stage of the process, searching and browsing directly from the Internet. Our work goes in this direction
and proposes an open environment made of open tools for the purpose. The language used allows also, as a
further benefit, the advantage of straightforward prototyping of new tools. Interested researchers are kindly
invited to email the authors for the distribution of the toolbox.
An open and very interesting field of research is the
search for tools and methods to implement automatic
identification, indexing and segmentation
mechanisms suitable for musical audio and sounds
(Tzanetakis & Cook, 2000, Zhang & Kuo, 2001,
Wold , 1996, Foote, 1999, Peeters , 2002). This is a
new concept in network and database navigation: the
navigation in audio environment, signals and
messages. This innovative type of navigation will
also allow, in automated mode and at the several
levels of the acoustical message content, to explore
the large mass of sound and musical information that
may be found in the Internet, by means of powerful
audio search, interpretation and classification tools.
The indexing of these multimedia objects poses
completely new problems, consisting in the
identification and the analysis of the audio signal, by
means of large set of parameters which may describe
the audio content and allow both classification and
search of sonic material for different purposes,
including just listening, or using audio data for
music composition or collect audio material for
other purposes.
The starting idea is to provide an instrument
which easily allows browsing in the Internet or in a
personal computer or in Local Area Network, just as
directory explorers allow listing directories,
searching for particular files based on names, length,
format or even content, and displaying file
information and features. The same simple paradigm
of file browsers should allow searching for audio
file based on file name, format, length, but, most
important, on content, sound parameters and others.
This is the idea of our Personal Sound Browser.
After the search, the files just found may be added to
a chosen Multimedia Data Base, in the form of links
to their position in the Personal Computer or LAN or
their Internet URL.
Cavaliere S. and Colucci C. (2006).
PERSONAL SOUND BROWSER - A Collection of Tools to Search, Analyze and Collect Audio Files in a LAN and in the Internet.
In Proceedings of the International Conference on Signal Processing and Multimedia Applications, pages 335-338
DOI: 10.5220/0001571403350338
Figure 1: The top level interface of the application. Menù :
add by WEB search - add by file search- Query DB.
The toolbox allows searching in the Internet, in a
Local Area Network (LAN) or in any storage device
of a Personal Computer .
The toolbox provides the user with an interface
to a Data Base Access (but any different DB is
allowed), using which he may populate an existing
archive; finally the archive may be visited,
annotating sound files and a search in it may be
performed both by example, that is providing a
source file, or specifying a range of parameters for
similarity comparison.
The whole toolbox is organized as a collection of
files and routines which at the top level allow the
following operations (see figure 1):
Add by file Search
Add by Web Search
Query DB
2.1 Add by File Search and Web
The search, as already stated, may be performed in
the computer (any storage device in it), just
providing the starting directory and the number of
levels to be analyzed in the directory tree. In the
actual realization parameters for search operation
and listing are grouped along the following
file features: name-extension
sound features: number. of bits, number of
channels, sampling rate
options for the download: download with
preview-max dimension
In the same GUI, whose functionality is
straightforward, the user may select the destination
archive to populate, if for some files the user decides
to store them in the archive, with predefined tables
and structure.
A second interface GUI is accessed choosing the
adding by web search menu entry.
This GUI is quite similar to the Add by File
search interface and is used in order to state the
modality of search; the search is then performed just
reading the source page, looking for links to the
chosen kind of files and adding these URLs to a list;
then, in the same page, links to other pages are
searched for, going down in the resulting tree by
using a recursive depth first search (DFS) algorithm.
The search is performed using regular grammars
and stops as far as a chosen number of levels is
traversed or a predetermined number of pages is
visited. The search may be delayed in time, pre-
programming it to be performed at definite time, and
at the end of the search a shut-down may be
programmed. In this case, at the end of the search,
the results, that is an html file containing the
addresses of the files found in the search, is stored as
a log file, for further processing.
Here also functions are grouped by category as
shown in the following list:
Source for the search
search options include: name, extension,
number of pages, number of levels for depth
search, optional visualization of web pages
during the search
sound features such as: number of bits -
number of channels - sampling rate
Download options e.g maximum length, etc
General options: delay the search, save the
search, shutdown PC when search has finished.
While most of the items are readily understood
from the name, we will point out some relevant
features; first of all search in the Internet may be
started from a specific URL; browsing at this
address will mean that we are just looking for
sounds, but also we are analyzing the content of the
specific URL: in fact we may visualize the starting
page and also the linked pages that will be visited
during the search, if we decide to practice this kind
of navigation; this will thus benefit also of
information on the context of the sounds.
A second possible source for the search may be
an html file saved by the user on the local disk, as a
result of a search performed by any search engine.
Our browser, in this case, stands on top of
professional and efficient commercial search engines
whose work we are enabled to refine just entering
the name of the performed and stored search; from
this our browser will start looping on the signalled
URLs for further analysis of the content.
Finally a third case may be that of a search
already performed by us by means of the Personal
Sound Browser, whose results have been stored in
our PC for this further analysis, as a log file.
Search is performed by means of depth first
traversing algorithm, allowing exhaustive search of
pages in the tree, up to a programmed number of
The second stage in both GUIs, the add by file
search and add by web search, is that of showing the
results of the search, if any, and the analysis of
individual files. The user may then ask for
downloading which is performed by means of a call
to the WGET free executable, from the GNU
( ).
For format different than wave the use of
executables from the LAME project
( allows audio format
The screen GUI has the following sections:
list of files: where you may choose the file to
download and analyze
signal plot
o spectrum: linear scale / db scale / mel scale
for frequency axis
o specgram linear frequency scale / mel scale
o start time - final time of signal to display -
o view web page: option to view the source
web page from which the file was
Data Base operations
o play-list: this is a token to be stored in the
Data Base, subjectively chosen by the user:
it may be a stile, or a user defined
collection or other
o insert selected sound file
o insert all sound files found
2.2 Browse the Data Base
The common interface to the DB, is provided by this
functionality, which allows selecting files in the DB
in order to fulfil some chosen features defined as
their mean value and dispersion. The parameters
have been computed on request, one by one, so that
the user, displaying the features along time may
carry on a thorough analysis of the signal.
Most of the features, are stored in form of time
varying features, on the basis of a chosen time
window, and their statistical distribution, including
mean and standard deviation, for later comparison
(Burred & Lerch, 2004).
The parameters chosen so far include, as suggested
by the current literature:
ZCR zero crossing rate
RMS root mean square (see Figure 2)
CENTROID weighted mean frequency
ROLLOFF (related to energy distribution)
FLUX (related to energy distribution)
MFCC Mel frequency cepstral coefficients
These parameters may be easily extended at will
by any user and are actually a starting point to be
improved along lines easily found in the large
literature in the field (Wold 1996, Scheirer 1997,
Rossignol 1998, Brown 1999, Lu 2001, Zhang 2001,
Zölzer 2002, Tzanetakis 2002, Burred Lerch 2004) .
Figure 2: The screenshot for a time varying parameter:
RMS value and histogram.
Search in the database is actually performed by
means of a similarity criterion: the parameters of a
chosen file are displayed in the form of their mean
and standard deviation; the user may modify these
parameters by hand or apply a multiplicative
coefficient to the standard deviation; the search is
then performed just looking for files whose mean
value of the parameters fall in the programmed
range; increasing the multiplicative coefficient of the
standard deviation broadens the range of files
collected, while further parameter refinement allows
reducing the selected class at will, up to a desired
class of sounds.
PERSONAL SOUND BROWSER - A Collection of Tools to Search, Analyze and Collect Audio Files in a LAN and in the
Many improvement are programmed in our project,
in all phases of its operation.
An improvement is that of using eventual XML
content information for the search (Bellini Nesi
2001, Haus Longari 2002) and that of using text
information from the URL by means of techniques
from Natural Language Processing, to be added to
the content information obtained by the signal: the
context of the sound file, description, annotations
and similar may in fact add useful information on it.
Other features to be used as means for
classification and search will be added, from the
large number identified by the literature (Peeters,
Rodet 2002); an example is the kind of thumbnails
recently introduced by one of the authors
(Evangelista & Cavaliere 2005).
A second modality of search will also be
implemented, based on histogram similarity using
the Kullback-Leibler divergence or other measure.
In this case the user will provide an example file or
an entire class of files for the search; files are then
searched for, which provide best fit to the statistical
distribution of the parameters in the example file.
We are also working to an improvement of the
program, consisting in a parallel version of it;
parallelism will be achieved by a master computer
which will divide the burden of annotation in chunks
and will send tasks to slave computers (these mostly
are in the LAN, but also might reside in any position
in the network); these slaves, as soon as the user in
them decides to open to parallel processing, will
signal its presence in the net and will be waiting for
the completion of the task. The maarester in fact will
receive the address of the slaves which are ready
and will send to it a specific task. The granularity of
these tasks is easily identified in the analysis of the
different sound files: the master just sends the
address of the files in the Internet: the slave will
download the sound file and, in turn, send back the
computed sound parameters to be stored in the
archive for further search.
The practice of our project has collected its first
encouraging results, showing that it has configured a
complete set of tools, which, installed in a Local
Area Network, in a studio or also classroom or
Research Laboratory, allows easily the efficient
paradigm of a parallel archive with distributed
storage and also distributed processing.
Also we realized that in spite of the use of high
level interpreted languages the efficiency of the
program is quite satisfying, while easiness of
prototyping lets experiment easily new solutions: on
the other end a compiled version of the Sound
Browser speeds up both search and classification.
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