Content-Based
Classification,
Search, and
Retrieval of Audio
Many audio and
multimedia
applications would
benefit from the
ability to classify and
search for audio
based on its
characteristics. The
audio analysis,
search, and
classification engine
described here
reduces sounds to
perceptual and
acoustical features.
This lets users search
or retrieve sounds by
any one feature or a
combination of
them, by specifying
previously learned
classes based on
these features, or by
selecting or entering
reference sounds and
asking the engine to
retrieve similar or
dissimilar sounds.
Erling Wold, Thorn Blum, Douglas Keislar,
and James Wheaton
Muscle Fish
he
T
rapid increase in speed and capacity
of computers and networks has
allowed the inclusion of audio as a data
type in many modern computer appli-
cations. However, the audio is usually treated as an
opaque collection of bytes with only the most prim-
itive fields attached: name, file format, sampling
rate, and so on. Users accustomed to searching,
scanning, and retrieving text data can be frustrated
by the inability to look inside the audio objects.
Multimedia databases or file systems, for exam-
ple, can easily have thousands of audio record-
ings. These could be anything from a library of
sound effects to the soundtrack portion of a news
footage archive. Such libraries are often poorly
indexed or named to begin with. Even if a previ-
ous user has assigned keywords or indices to the
data, these are often highly subjective and may be
useless to another person. Searching for a partic-
ular sound or class of sound (such as applause,
music, or the speech of a particular speaker) can
be a daunting task.
How might people want to access sounds? We
believe there are several useful methods, all of
which we have attempted to incorporate into our
system.
I Simile: saying one sound is like another sound
or a group of sounds in terms of some charac-
teristics. For example, “like the sound of a herd
of elephants.” A simpler example would be to
say that it belongs to the class
of speech
sounds
or the class of applause sounds, where the sys-
tem has previously been trained on other
sounds in this class.
I Acoustical/perceptual features: describing the
sounds in terms of commonly understood
physical characteristics such as brightness,
pitch, and loudness.
I Subjective features: describing the sounds using
personal descriptive language. This requires
training the system (in our case, by example)
to understand the meaning of these descriptive
terms. For example, a user might be looking for
a “shimmering” sound.
I Onomatopoeia: making a sound similar in some
quality to the sound you are looking for. For
example, the user could making a buzzing
sound to find bees or electrical hum.
In a retrieval application, all of the above could
be used in combination with traditional keyword
and text queries.
To accomplish any of the above methods, we
first reduce the sound to a small set of parameters
using various analysis techniques. Second, we use
statistical techniques over the parameter space to
accomplish the classification and retrieval.
Previous research
Sounds are traditionally described by their
pitch, loudness, duration, and timbre. The first
three of these psychological percepts are well
understood and can be accurately modeled by
measurable acoustic features. Timbre, on the other
hand, is an ill-defined attribute that encompasses
all the distinctive qualities of a sound other than
its pitch, loudness, and duration. The effort to dis-
cover the components of timbre underlies much
of the previous psychoacoustic research that is rel-
evant to content-based audio retrieval.*
Salient components of timbre include the
amplitude envelope, harmonicity, and spectral
envelope. The attack portions of a tone are often
essential for identifying the timbre. Timbres with
similar spectral energy distributions (as measured
by the centroid of the spectrum) tend to be judged
as perceptually similar. However, research has
shown that the time-varying spectrum of a single
musical instrument tone cannot generally be
treated as a “fingerprint” identifying the instru-
ment, because there is too much variation across
1070-986X/96/$5.00 6 1996 IEEE
the instrument’s range of pitches and across its
range of dynamic levels.
Various researchers have discussed or proto-
typed algorithms capable of extracting audio
structure from a sound.z The goal was to allow
queries such as “find the first occurrence of the
note G-sharp.” These algorithms were tuned to
specific musical constructs and were not appro-
priate for all sounds.
Other researchers have focused on indexing
audio databases using neural nets.3 Although they
have had some success with their method, there
are several problems from our point of view. For
example, while the neural nets report similarities
between sounds, it is very hard to “look inside” a
net after it is trained or while it is in operation to
determine how well the training worked or what
aspects of the sounds are similar to each other.
This makes it difficult for the user to specify which
features of the sound are important and which to
ignore.
Analysis and retrieval engine
Here we present a general paradigm and spe-
cific techniques for analyzing audio signals in a
way that facilitates content-based retrieval.
Content-based retrieval of audio can mean a vari-
ety of things. At the lowest level, a user could
retrieve a sound by specifying the exact numbers
in an excerpt of the sound’s sampled data. This is
analogous to an exact text search and is just as
simple to implement in the audio domain.
At the next higher level of abstraction, the
retrieval would match any sound containing the
given excerpt, regardless of the data’s sample rate,
quantization, compression, and so on. This is
analogous to a fuzzy text search and can be imple-
mented using correlation techniques. At the next
level, the query might involve acoustic features
that can be directly measured and perceptual (sub-
jective) properties of the sound.+5 Above this, one
can ask for speech content .or musical content.
It is the “sound” level-acoustic and perceptu-
al properties-with which we are most concerned
here. Some of the aural (perceptual) properties of
a sound, such as pitch, loudness, and brightness,
correspond closely to measurable features of the
audio signal, making it logical to provide fields for
these properties in the audio database record.
However, other aural properties (“scratchiness,”
for instance) are more indirectly related to easily
measured acoustical features of the sound. Some
of these properties may even have different mean-
ings for different users.
We first measure a variety of acoustical features
of each sound. This set of N features is represented
as an N-vector. In text databases, the resolution of
queries typically requires matching and compar-
ing strings. In an audio database, we would like to
match and compare the aural properties as
described above. For example, we would like to
ask for all the sounds similar to a given sound or
that have more or less of a given property. To
guarantee that this is possible, sounds that differ
in the aural property should map to different
regions of the N-space. If this were not satisfied,
the database could not distinguish between
sounds with different values for this property.
Note that this approach is similar to the “feature-
vector” approach currently used in content-based
retrieval of images, although the actual features
used are very different.6
Since we cannot know the complete list of
aural properties that users might wish to specify,
it is impossible to guarantee that our choice of
acoustical features will meet these constraints.
However, we can make sure that we meet these
constraints for many useful aural properties.
Acoustical features
We can currently analyze the following aspects
of sound: loudness, pitch, brightness, bandwidth,
and harmonicity.
Loudness is approximated by the signal’s root-
mean-square (RMS) level in decibels, which is cal-
culated by taking a series of windowed frames of
the sound and computing the square root of the
sum of the squares of the windowed sample val-
ues. (This method does not account for the fre-
quency response of the human ear; if desired, the
necessary equalization can be added by applying
the Fletcher-Munson equal-loudness contours.)
The human ear can hear over a 120-decibel range.
Our software produces estimates over a lOO-
decibel range from 16-bit audio recordings.
Pitch is estimated by taking a series of short-
time Fourier spectra. For each of these frames, the
frequencies and amplitudes of the peaks are mea-
sured and an approximate greatest common divi-
sor algorithm is used to calculate an estimate of
the pitch. We store the pitch as a log frequency.
The pitch algorithm also returns a pitch confi-
dence value that can be used to weight the pitch
in later calculations. A perfect young human ear
can hear frequencies in the ZO-Hz to ZO-kHz
range. Our software can measure pitches in the
range of 50 Hz to about 10 kHz.
Brightness is computed as the centroid of the
short-time Fourier magnitude spec-
tra, again stored as a log frequency.
It is a measure of the higher fre-
quency content of the signal. As an
example, putting your hand over
your mouth as you speak reduces the
brightness of the speech sound as
well as the loudness. This feature
varies over the same range as the
pitch, although it can’t be less than the pitch esti-
mate at any given instant.
Bandwidth is computed as the magnitude-
weighted average of the differences between the
spectral components and the centroid. As exam-
ples, a single sine wave has a bandwidth of zero
and ideal white noise has an infinite bandwidth.
Harmonicity distinguishes between harmonic
spectra (such as vowels and most musical sounds),
inharmonic spectra (such as metallic sounds), and
noise (spectra that vary randomly in frequency
and time). It is computed by measuring the devl-
ation of the sound’s line spectrum from a perfect-
ly harmonic spectrum. This is currently an
optional feature and is not used in the examples
that follow. It is normalized to lie in a range from
zero to one.
All of these aspects of sound vary over time.
The trajectory in time is computed during the
analysis but not stored as such in the database.
However, for each of these trajectories, several fea-
tures are computed and stored. These include the
average value, the variance of the value over the
trajectory, and the autocorrelation of the trajec-
tory at a small lag. Autocorrelation is a measure of
the smoothness of the trajectory. It can distin-
guish between a pitch glissando and a wildly vary-
ing pitch (for example), which the simple variance
measure cannot.
The average, variance, and autocorrelation
computations are weighted by the amplitude tra-
jectory to emphasize the perceptually important
sections of the sound. In addition to the above
features, the duration of the sound is stored. The
feature vector thus consists of the duration plus
the parameters just mentioned (average, variance,
and autocorrelation) for each of the aspects of
sound given above. Figure 1 shows a plot of the
raw trajectories of loudness, brightness, band-
width, and pitch for a recording of male laughter.
After the statistical analyses, the resulting
analysis record (shown in Table 1) contains the
computed values. These numbers are the only
information used in the content-based classifica-
tion and retrieval of these sounds. It is possible to
see some of the essential characteris-
tics of the sound. Most notably, we
see the rapidly time-varying nature
of the laughter.
Training the system
It is possible to specify a sound
directly by submitting constraints on
the values of the N-vector described
above directly to the system. For
example, the user can ask for sounds
in a certain range of pitch or bright-
ness, However, it is also possible to
train the system by example. In this
case, the user selects examples of
sounds that demonstrate the proper-
ty the user wishes to train, such as
“scratchiness.”
For each sound entered into the
database, the N-vector, which we
represent as a, is computed. When
the user supplies a set of example
sounds for training, the mean vector
,U and the covariance matrix R for
the a vectors in each class are calcu-
lated. The mean and covariance are
given by
0.00 0.50
1.00 0.00 1 so
X
--... LaughterYoungMale.bright
Figure 1. Male laughter.
where A4 is the number of sounds in the summa-
tion. In practice, one can ignore the off-diagonal
elements of R if the feature vector elements are
reasonably independent of each other. This sim-
plification can yield significant savings in com-
putation time. The mean and covariance together
become the system’s model of the perceptual
property being trained by the user.
Classifying sounds
When a new sound needs to be classified, a dis-
tance measure is calculated from the new sound’s
a vector and the model above. We use a weighted
D = ((a - p)T R1 (a - p))”
Again, the off-diagonal elements of R can be
ignored for faster computation. Also, simpler mea-
sures such as an L, or Manhattan distance can be
used. The distance is compared to a threshold to
determine whether the sound is “in” or “out” of
the class. If there are several mutually exclusive
classes, the sound is placed in the class to which
it is closest, that is, for which it has the smallest
value of D.
If it is known a priori that some acoustic fea-
tures .are unimportant for the class, these can be
ignored or given a lower weight in the computa-
tion of D. For example, if the class models some
timbral aspect of the sounds, the duration and
average pitch of the sounds can usually be
ignored.
We also define a likelihood value L based on
the normal distribution and given by
L = exp(-D2/2)
This value can be interpreted as “how much” of the
defining property for the class the new sound has.
Retrieving sounds
It is now possible to select, sort, or classify
sounds from the database using the distance mea-
sure. Some example queries are
I Retrieve the “scratchy” sounds. That is, retrieve
all the sounds that have a high likelihood of
being in the “scratchy” class.
I Retrieve the top 20 “scratchy” sounds.
I Retrieve all the sounds that are less “scratchy”
than a given sound.
I Sort the given set of sounds by how “scratchy”
they are.
I Classify a given set of sounds into the follow-
’ ing set of classes.
any desired hyper-rectangle of sounds in the data-
base by requesting all sounds whose feature val-
ues fall in a set of desired ranges. Requesting such
hyper-rectangles allows a much more efficient
search. This technique has the advantage that it
can be implemented on top of the very efficient
index-based search algorithms in existing com-
mercial databases.
As an example, consider a query to retrieve the
top M sounds in a class. If the database has MO
sounds total, we first ask for all the sounds in a
hyper-rectangle centered around the mean ,U with
volume V such that
V/v, = M/n/i,
For small databases, it is easiest to compute the
distance measure(s) for all the sounds in the data-
base and then to choose the sounds that match
the desired result. For large databases, this can be
too expensive. To speed up the search, we index
(sort) the sounds in the database by all the
and try again.
Note that the above discussion is a simplifica-
tion of our current algorithm, which asks for big-
ger volumes to begin with to correct for two
factors. First, for, our distance measure, we really
want a hypersphere of volume V, which means we
want the hyper-rectangle that circumscribes this
sphere. Second, the distribution of sounds in the
feature space is not perfectly regular. If we assume
some reasonable distribution of the sounds in the
database, we can easily compute how much larger
V has to be to achieve some desired confidence
level that the search will succeed.
Quality measures
The magnitude of the covariance matrix R is a
measure of the compactness of the class. This can
be reported to the user as a quality measure of the
classification. For example, if the dimensions of R
are similar to the dimensions of the database, this
class would not be useful as a discriminator, since
all the sounds would fall into it. Similarly, the sys-
tem can detect other irregularities in the training
set, such as outliers or bimodality.
The size of the covariance matrix in each
dimension is a measure of the particular dimen-
sion’s importance to the class. From this, the user
can see if a particular feature is too important or
not important enough. For example, if all the
sounds in the training set happen to have a very
similar duration, the classification process will
rank this feature highly, even though it may be
irrelevant. If this is the case, the user can tell the
system to ignore duration or weight it differently,
or the user can try to improve the training set.
Similarly, the system can report to the user the
components of the computed distance measure.
Again, this is an indication to the user of possible
problems in the class description.
Note that all of these measures would be diffi-
cult to derive from a non-statistical model such as
a neural network.
Segmentation
The discussion above deals with the case where
each sound is a single gestalt. Some examples of
this would be single short sounds, such as a door
slam, or longer sounds of uniform texture, such as
a recording of rain on cement. Recordings that
contain many different events need to be seg-
mented before using the features above.
Segmentation is accomplished by applying the
acoustic analyses discussed to the signal and look-
ing for transitions (sudden changes in the mea-
sured features). The transitions define segments of
the signal, which can then be treated like individ-
ual sounds. For example, a recording of a concert
could be scanned automatically for applause
sounds to determine the boundaries between
musical pieces. Similarly, after training the system
to recognize a certain speaker, a recording could
be segmented and scanned for all the sections
where that speaker was talking.
Performance
We have used the above algorithms at Muscle
Fish on a test sound database that contains about
400 sound files. These sound files were culled from
various sound effects and musical instrument sam-
ple libraries. A wide variety of sounds are represent-
ed from animals, machines, musical instruments,
speech, and nature. The sounds vary in duration
from less than a second to about 1.5 seconds.
A number of classes were made by running the
classification algorithm on some perceptually sim-
ilar sets of sounds. These classes were then used to
reorder the sounds in the database by their likeli-
hood of membership in the class. The following
discussion shows the results of this process for sev-
eral sound sets. These examples illustrate the char-
acter of the process and the fuzzy
nature of the retrieval. (For more
information, and to duplicate these
examples, see the “Interactive Web
Demo” sidebar.)
Example 1: Laughter. For this
example, all the recordings of laugh-
ter except two were used in creating
the class. Figure 2 shows a plot of the
class membership likelihood values
(the Y-axis) for all of the sound files
in the test database. Each vertical
strip along the X-axis is a user-
defined category (the directory in
which the sound resides). See the
“Class Model” sidebar on p. 32 for
details on how our system comput-
ed this model.
The highest returned likelihoods
are for the laughing sounds, includ-
ing the two that were not included
in the original training set, as well as
one of the animal recordings. This animal record-
ing is of a chicken coop and has strong similari-
ties in sound to the laughter recordings,
consisting of a number of strong sound bursts.
Example 2: Female speech. Our test database
contains a number of very short recordings of a
- Laughter.order
not in training set
m . . . . . Animals
+---..-.. Bells
*----
Crowds
*..
k2000
s_-- Laughter
---
Telephone
--....-
Water
- Mcgill/altotrombone
o.. . . . .
Mcgill/cellobowed
c I - - - - - Mcgill/oboe
-----
Mcgill/percussion
.- .
Mcgill/tubularbells
I--
Mcgill/violinbowed
---
Mcgill/violinpizz
--
Speech/female
- Speech/male
Figure 2. Laughter
classification.
