Analysis of the Data Quality of Audio Features of Environmental
Dalibor Mitrovic, Matthias Zeppelzauer, Horst Eidenberger
(Vienna University of Technology, Austria
Abstract: In this paper we perform statistical data analysis
of a broad set of state-of-the-art audio features and low-level MPEG-7
audio descriptors. The investigation comprises data analysis to reveal
redundancies between state-of-the-art audio features and MPEG-7 audio descriptors.
We introduce a novel measure to evaluate the information content of a descriptor
in terms of variance. Statistical data analysis reveals the amount of variance
contained in a feature. It enables identification of independent and redundant
features. This approach assists in efficient selection of orthogonal features
for content-based retrieval. We believe that a good feature should provide
descriptions with high variance for the underlying data. Combinations of
features should consist of decorrelated features in order to increase expressiveness
of the descriptions. Although MPEG-7 is a popular and widely used standard
for multimedia description, only few investigations do exist that address
analysis of the data quality of low-level MPEG-7 descriptions.
Keywords: content-based multimedia retrieval, feature extraction, feature
analysis, statistical data analysis, low-level MPEG-7 audio descriptors.
Categories: H.3.1, H.3.3, G.3
In the last decades a huge number of features was developed for the
analysis of audio content. One of the first application domains of audio
analysis was speech recognition [Rabiner, 93]. With
upcoming novel application areas the analysis of music and general purpose
environmental sounds gained importance. Different research fields evolved,
such as audio segmentation, music information retrieval (MIR), and environmental
sound recognition (ESR). Each of these areas developed its specific description
techniques (features). Currently, features are often employed in other
domains than their original ones. A recent effort to standardize multimedia
description tools led to the MPEG-7 standard. MPEG-7 is an ISO/IEC standard
for multimedia content description [ISO/IEC, 02].
The standard defines low-level descriptions techniques (including audio)
as well as high-level tools for multimedia processing.
The huge number of existing features makes the selection of the most
appropriate feature set for a task difficult. Statistical data analysis
can help in the identification of independent features. In this paper,
we perform a quantitative analysis of the so called MPEG-7 low-level
audio descriptors (LLDs) in the domain of environmental sounds. In
the following we consider the descriptors as features and use the terms
interchangeably. We compare MPEG-7 descriptors to a set of state-of-the-art
audio features we previously analyzed in the domain of environmental sounds
We investigate different description techniques by statistical data
analysis in order to identify similarities and redundancies. Redundant
features describe similar properties of the underlying data, while statistically
independent features contain orthogonal information. The objective of feature
selection is the combination of orthogonal features in order to maximize
the amount of represented information. The method proposed in this paper
supports the identification of independent and redundant features. Furthermore,
we evaluate selected MPEG-7 high-level tools from this point of view. Additionally,
we investigate the amount of information (entropy) contained in each feature.
The information content of a feature is proportional to the variance of
the feature values for a given dataset. This assumption is independent
from the used similarity measure and classification technique. A discriminative
feature must have high variance for a set of inhomogeneous sound samples.
We derive a measure that represents the information contained in a feature
with respect to its variance in order to evaluate the expressiveness of
The remainder of the paper is organized as follows. Section
2 gives background information on the MPEG-7 descriptors. In Section
3, we describe the approach for the data analysis. We present the structure
of the experiments in Section 4. The results of the
experiments are discussed in Section 5. We present
some related work in Section 6. Finally we conclude
the results in Section 7.
The MPEG-7 Audio part specifies data structures and techniques
for the description of audio content. It contains low-level descriptors
(LLDs) as well as more sophisticated description techniques. In this section,
we discuss the MPEG-7 LLDs relevant to this study.
2.1 Low-level MPEG-7 Audio Descriptors
The MPEG-7 Audio LLDs are a collection of low-level audio features that
describe characteristic properties of sound such as harmonicity, sharpness,
pitch, and timbre [Kim, 05]. The descriptors are applied
to short frames of the signal and are either scalars or vectors per frame.
Aggregation of descriptions of several frames to a description of an entire
media object is not a normative part of the MPEG-7 standard. Some LLDs
(TimbralSpectral descriptors) support a single-valued summarization
for entire media objects. For other LLDs the standard proposes mathematical
operations, such as minimum, maximum, mean, and variance for summarization.
The MPEG-7 Audio LLDs are organized in the six groups: Basic, Basic Spectral,
Spectral Basis, Signal Parameters, Timbral Temporal, Timbral Spectral.
In the following, we describe the MPEG-7 Audio LLDs together with their
perceptual meaning and their application domain. A more detailed description
can be found in [Kim, 05].
The LLDs in the Basic group primarily enable a short
description of the shape of an audio waveform. The
AudioWaveform (AW) descriptor represents the waveform envelope
and is mainly intended for economic display of a waveform in an audio
editor. AW comprises the minimum and maximum values of a framed
signal. The AudioPower descriptor computes the average square
of the waveform samples in a frame. It describes the power of the
signal over time.
2.3 Basic Spectral
The LLDs in the BasicSpectral group describe basic properties
of the spectrum of an audio signal. The AudioSpectrumEnvelope (ASE)
descriptor represents the short-term power spectrum of a signal with a
logarithmic frequency scale in several frequency bands. The logarithmic
frequency scale aims at imitating properties of the human ear. The ASE
descriptor is the basis for the computation of the other descriptors in
the BasicSpectral group.
The AudioSpectrumCentroid (ASC) is the center of gravity of the
spectrum calculated by ASE. The ASC descriptor indicates whether high or
low frequencies dominate the spectrum of the signal. The AudioSpectrumSpread
(ASS) descriptor represents the deviation of the power spectrum from its
centroid. ASS enables separation of tonal sounds from noise-like sounds.
The fourth descriptor of the BasicSpectral group is AudioSpectrumFlatness
(ASF). ASF describes the deviation of the spectrum of an audio signal from
a flat shape. A flat spectrum indicates a noise-like or impulse-like signal.
According to the MPEG-7 standard ASF is designed to perform fingerprinting,
which requires robust matching between pairs of audio signals.
2.4 Spectral Basis
The SpectralBasis descriptors AudioSpectrumBasis (ASB)
and AudioSpectrumProjection (ASP) are techniques for general-purpose
sound recognition. ASB transforms the spectrum of a signal to a much lower-dimensional
representation under certain statistical constraints. The ASB descriptor
is based on the power spectrum, similarly to the ASE descriptor and provides
a compact representation of a spectrum, while preserving a maximum amount
The ASP is used together with the ASB descriptor. ASP takes a decibel-scaled
spectrum as input and projects it against spectral basis functions, previously
computed by ASB.
2.5 Signal Parameters
The SignalParameters group contains the AudioFundamentalFrequency
(AFF) descriptor and the AudioHarmonicity (AH) descriptor. The AFF
descriptor represents the fundamental frequency of a sound. AFF may be
applicable to sound segmentation of speech and music. AH is a measure for
the degree of harmonicity in a signal. The descriptor comprises of two
components: harmonic ratio and upper limit of harmonicity.
The harmonic ratio is the proportion of harmonic components in a signal.
A purely harmonic signal has a harmonic ratio of "1," while the
harmonic ratio of noise is "0." The upper limit of harmonicity
specifies the frequency beyond which the audio signal has no more significant
2.6 Timbral Temporal
Timbral descriptors are usually employed in MIR. Timbre is a sound property
that is independent of pitch and loudness. The LogAttackTime (LAT)
characterizes the attack of a sound. The attack time is the time from the
beginning of a sound signal to a point in time where its amplitude reaches
a maximum. LAT is the logarithm of the attack time. The attack characterizes
the beginning of a sound, which can be smooth or sudden. LAT may be employed
for classification of musical instruments. The TemporalCentroid
(TC) is the point in time where most of the signal energy is located.
2.7 Timbral Spectral
Harmonic peaks in a spectrum correspond to frequencies that are a multiple
of the fundamental frequency. They are appropriate to describe the timbre
of a signal. The TimbralSpectral descriptors rely on harmonic peak
estimation by the fundamental frequency of the audio signal. The HarmonicSpectralCentroid
(HSC) is the amplitude-weighted average of the harmonic peaks in a spectrum.
The HarmonicSpectralSpread (HSS) descriptor is the amplitude-weighted
deviation of the harmonic peaks from the HSC.
The HarmonicSpectralDeviation (HSD) is the deviation of the harmonic
peaks from the spectral envelope. The spectral envelope is the mean over
a few neighboring harmonic peaks. HarmonicSpectralVariation (HSV)
refers to the correlation of harmonic peaks in adjacent frames. The fifth
TimbralSpectral descriptor is SpectralCentroid (SC), which
is the power-weighted average of the frequencies in the power spectrum.
The timbre descriptors are usually applied to music information retrieval
in which timbre plays an important role. We investigate the applicability
of timbre descriptors in the domain of environmental sounds. The descriptors
are expected to yield average results in the experiments.
The quality of a feature may be measured by the amount of variance of
its numeric values. A good feature should provide descriptions with high
variance for the underlying data. Statistical data analysis reveals the
amount of variance of a feature.
The analysis steps are as follows: Firstly features are extracted from
the raw sound samples. Features based on audio frames are aggregated over
time in order to obtain a description for the entire sound sample. The
feature vectors of all sample files are combined into a matrix. This matrix
is the basis for the statistical data analysis of the features.
Data analysis starts with a dimension reduction via Principal Components
Analysis (PCA). The PCA decorrelates the second statistical moments (variances)
of the feature data. We select only the principal components (PCs) with
an Eigenvalue greater or equal than "1" for data analysis.
PCs with lower Eigenvalues are not considered since they explain less
variance than the original data. From the PCA we yield the factor loading
matrix that describes the influence of PCs on the particular feature
components and vice versa. The factor loading matrix has the PCs in its
columns (ordered by descending Eigenvalues) and the features form the rows
of the matrix. Each entry (factor loading) represents the influence of
a feature on a PC. The factor loadings are in the interval [-1, 1]. A high
absolute factor loading indicates a high degree of correlation between
a feature and a PC. Features that load the same PCs are correlated. A Varimax
rotation simplifies the interpretation of factor loadings by maximizing
We investigate the expressiveness of the features using entropy. Furthermore,
we derive a novel measure for expressiveness based on the factor loading
matrix, as described in the following section.
3.1 A novel measure for expressiveness of features
In order to quantify the information contained in a feature we sum the
absolute factor loadings weighted by the corresponding percentage of explained
overall variance. The result is normalized by the sum of variances of all
PCs. We call this measure Weighted Average LoaDing Indicator (WALDI).
It is computed as defined in Equation 1.
Where fj is the j-th feature from a set of
F features and the ci are the C principal
components. The variance of the i-th PC is denoted by σ2(ci).
The factor loading matrix L is a RCxF matrix with
C columns and F rows.
The WALDI of a feature is a measure of its information content in terms
of orthogonal variances in the data. This value is proportional to the
expressiveness of a feature. However, it does not contain information about
redundancies among different features. This information can be derived
from the factor loading matrix.
We build a graph based on the WALDI (see Figure 1).
Peaks in the WALDI graph indicate high expressiveness, which may have two
reasons: either the corresponding feature loads a large number of less
important PCs or it highly loads the few most important PCs.
Additionally, we compare WALDI with the information entropy, introduced
by Shannon and Weaver [Shannon, 49]. We determine
the entropy as defined in Equation 2.
We consider features as random variables and quantize the observed
values of the features into n=256 bins. Then we compute the
probability p(i) of the i-th bin for a feature
f. For n=256 the unit of entropy is bit per
byte. The entropy is a measure for the information content of a
variable. The uncertainty of a random variable is proportional to its
entropy. A powerful feature should have high entropy. We discuss the
entropy of MPEG-7 features in Section 5.1.
The investigations in this paper employ a database containing of 940
sound samples from 9 classes of environmental sounds. The sounds can be
categorized into noise-like and tonal sounds. This distinction is of interest
since several MPEG - 7 descriptors model these properties. Table
1 summarizes the 9 different classes, the respective number of sound
samples and predominant sound characteristics.
# of samples
Table 1: The class names, the respective
number of samples and the sound characteristics
The audio data in the experiments are sampled at 11025 Hz and quantized
to 8 bits. MPEG-7 descriptors are computed with the LLD extractor provided
by the TU Berlin [TUBerlin, 06]. The investigations
include 12 previously analyzed audio descriptors from different application
domains listed in Table 2 [Mitrovic,
06]. The MPEG-7 descriptors are summarized in Table
Mel-Frequency Ceptstra Coefficients
Bark-Frequency Cepstral Coefficients
Linear Predictive Coding
Perceptual Linear Prediction
Relative Spectral - Perceptual Linear Prediction
Discrete Wavelet Transform
Constant Q Transform
Zero Crossing Rate
Table 2: Non-MPEG-7 descriptors and their
MPEG 7 Low-level descriptor
Table 3: The low-level MPEG-7 audio descriptors
TCombination of MPEG-7 and non-MPEG-7 descriptors yields a 391-dimensional
feature vector. Frame-based features are summarized by statistical moments
(mean and variance) in order to obtain descriptions of entire media objects.
Quantitative data analysis discloses the data quality of numerical features.
The basis of the investigation is the factor loading matrix that shows
the mapping of features to PCs. In the first step of the analysis we investigate
the expressiveness of features.
5.1 Information content analysis
We evaluate the audio descriptors based on their expressiveness and
compute therefore the WALDI for the descriptor components (see Section
3.1 for details). We average over all WALDIs of the descriptor components
to gain a more compact representation. That is equivalent to the average
amount of information contained in each descriptor component. Figure
1 depicts the resulting graph.
The BasicSpectral descriptors ASS, ASC, and ASF yield high WALDIs,
because these descriptors have high loadings for the first few and most
important PCs. In contrast to this, the ASB descriptor has a small WALDI
value. The reason for this is that the components of ASB do not load any
of the important PCs. The factor loading matrix reveals a similar situation
for ASE. This can as well be observed in the WALDI graph (see Figure
1). The timbral descriptors yield average values for the environmental
sounds in the experiments. This is an unexpectedly good result since they
mainly represent characteristics of musical sounds.
We believe that high entropy is a necessary property of a good feature.
In the following, we compare the results obtained from the WALDI graph
with the entropy of the descriptors. Figure 2 illustrates
the average entropy of all components of a descriptor.
Figure 1: The WALDI graph of the MPEG-7 descriptors. High
values indicate high expresiveness
Figure 2: The entropy graph of the MPEG-7 descriptors
AH is the descriptor with the highest entropy (7.2 bit per byte), followed
by the BasicSpectral descriptors, TC, and SC. Generally, the entropy
of the LLDs is high with an average of 6 bit per byte. We observe that
the MPEG-7 audio LLDs have higher entropy than the visual descriptors of
the standard [Eidenberger, 04].
A comparison to WALDI and entropy shows that both measures correlate
to a certain degree. Features with high WALDI tend to have high entropy.
This is evident for the BasicSpectral descriptors (ASF, ASS, and
ASC). Analogously, AP and ASE have low values for both measures. The correlation
between WALDI and entropy shows that WALDI is a valid measure for information
content and expressiveness.
5.2 Redundancy analysis
Identification of redundancies between features is an important pre-processing
step of feature selection. The factor loading matrix is the basis for redundancy
analysis. High absolute loadings indicate a high degree of correlation
with the corresponding PC. Features that load the same PCs are dependent
and contain redundant information.
The total redundancy of MPEG-7 descriptors is low, compared to the
non-MPEG-7 features. In order to explain 85% of the overall variance
in the data the non-MPEG-7 descriptors require 40 out of 228 PCs
(~17.5%). MPEG-7 descriptors require 56 out of 163 PCs (~34.4%) to
achieve the same result. Consequently, the set of MPEG-7 descriptors
is less redundant.
In the following, we discuss the quality of the MPEG-7 Audio LLDs in
more detail. The MPEG-7 descriptors of the Basic group (AP and AW)
are highly correlated. The reason is that the computations of these two
descriptors are similar. The descriptors represent related properties of
the audio waveform. As expected, the expressiveness of these descriptors
is limited. Both descriptors do not describe independent information with
respect to the other features in the investigation. Most information explained
by AP and AW is also contained in the Sone feature that measures the loudness
of a signal.
The components of ASE are independent to a high degree. They load several
less important PCs. However, ASE correlates with some Sone components.
ASC, ASS, and ASF are highly correlated descriptors. The dependency of
ASC and ASS is difficult to interpret since the descriptors describe different
statistical moments. This may be a side effect introduced by the underlying
data. In contrast to this, the redundancy of ASS and ASF is easier to explain
since both descriptors model the same sound characteristics (noise-likeness
and tonality). ASC, ASS, and ASF are highly redundant with several non-MPEG-7
features such as PLP, MFCC, and Sone.
The components of the SpectralBasis descriptor ASB are decorrelated.
This is due to the SVD that yields orthogonal basis functions. The ASP
descriptor bases on ASB. Thus, it correlates with ASB to some degree. ASB
and ASP should be applied in conjunction according to the MPEG-7 standard.
The factor loading matrix reveals the independence of ASB and ASP from
all other features in this survey. These properties qualify ASB and ASP
for combination with other features. Due to the similar computation of
ASB and ASP, the two descriptors have comparable loadings in the factor
AH describes the harmonic structure of a signal. In the experiments
AH is highly correlated with ASC, ASS and ASF because these descriptors
depend on harmonic properties as well. Pitch, ZCR, and AD encode similar
information as AH. A group of dependent MPEG-7 descriptors are AFF, ASC,
SC, and HSC. They are correlated since they are all sensitive to the fundamental
frequency of a signal. Consequently, the ZCR, which approximates the fundamental
frequency, highly correlates with these features.
The timbral temporal descriptors LAT and TC are highly correlated with
SF for the data in the experiments, even though they are computed entirely
different. The reason for the high correlation is that these features do
not model the characteristic of the environmental sounds very well. This
is evident in the analysis of the expressiveness of these features (see
Section 5.1). According to the WALDI graph in Figure
1, TC and LAT have average and low expressiveness, respectively.
This is due to the fact that the shape of environmental sounds is generally
not structured in attack, decay, sustain and release as is the case for
musical sounds. The expressiveness of SF with a WALDI of 0.7 is low as
well because of the complexity and noise-like structure of the environmental
sounds. However, LAT, TC, and SF describe information that is not captured
by the other features in the study. That qualifies them for combination
with other features.
The timbral spectral descriptors are completely redundant for the environmental
sounds in the experiments. However, they describe unique information that
is not captured by any other feature in the experiment. Since environmental
sounds contain only little timbral characteristics the expressiveness of
the timbral features is limited. Nevertheless timbral descriptors may be
applicable to separate certain sound classes, such as bird sounds from
other environmental sounds.
The following major insights can be derived from the analysis:
- Most of the groups of MPEG-7 descriptors contain highly correlated
components (Basic, BasicSpectral, SpectralBasis, TimbralTemporal,
and TimbralSpectral). One reason may be the characteristics of the
environmental sounds, which contain only little timbre and harmonicity.
Another reason is the similarity of the computations of the descriptors
in particular groups.
- The descriptors in the SignalParameters group are decorrelated
to a higher degree than the components of the other groups. This is because
AH and AFF describe different signal properties.
- The different descriptor groups are mostly independent from each other.
The exceptions are the Basic group and the BasicSpectral
group, which are highly correlated. The reason is that the two groups describe
a signal waveform in time and frequency domain without further processing.
Since the time and frequency representations of a signal are equivalent,
the descriptions correlate. Two other correlated groups are BasicSpectral
and SignalParameters. Both have correlated components such as AH
and ASF, which describe the tonality of a sound. Further correlated components
are ASC and AFF, which both heavily depend on the fundamental frequency.
- Descriptors of the SpectralBasis and TimbralSpectral
groups are independent from all other features including the non-MPEG-7
ones. Hence, they complement all possible feature combinations and are
potential candidates for feature selection.
- Several non-MPEG-7 features correlate with MPEG-7 descriptors. The
Sone feature is redundant with the entire Basic and BasicSpectral
groups. Pitch and PLP highly correlate with the BasicSpectral group.
Further, several popular speech recognition features such as MFCC, BFCC,
RASTA-PLP, Sone, Pitch, and PLP encode the information captured by ASF.
The reason may be that all these features describe properties necessary
for audio fingerprinting.
5.4 MPEG-7 high-level tools
The MPEG-7 high-level tools contain application-specific description
schemes that build upon LLDs. These description schemes are AudioSignature,
Timbre, and SoundModel. Other high-level descriptions do
not consist of the LLDs discussed above and are therefore out of scope
for the data analysis in this study.
The AudioSignature description scheme contains statistical summarizations
of the ASF low-level descriptor. Its addressed application area is fingerprinting.
Data analysis reveals that the components, which represent the ASF in different
frequency bands, are highly redundant. Hence, a subset of ASF components
may be sufficient for retrieval applications. Furthermore, it satisfies
the requirements for fingerprinting since it contains a significant amount
of information (see the WALDI graph in Figure 1).
Timbre is the second description scheme considered. It contains
LAT, HSC, HSD, HSS, and HSV for harmonic instrument identification and
SC, TC, and LAT for percussive instrument identification. In the experiments,
we evaluate the quality of the timbral descriptors to prove whether timbre
is a discriminative characteristic of environmental sounds. Experiments
show that the descriptors for percussive instruments model environmental
sounds better. The harmonic instrument descriptors are highly redundant
for ES. However, they describe unique information with respect to the other
descriptors in the experiments. Thus, only a subset of components of the
Timbre description scheme may be feasible for ESR.
The SoundModel descriptor scheme is the third high-level tool
in this investigation. It consists of ASB and ASP and addresses environmental
sound recognition. While the components of ASB and ASP are highly decorrelated,
their expressiveness is limited because they have only mediocre influence
on the important PCs in the factor loading matrix. It may therefore be
advantageous to combine the descriptors of SoundModel with other
more expressive LLDs such as ASF.
6 Related Work
The MPEG-7 Audio standard provides a large set of low-level audio descriptors
[ISO/IEC, 02]. MPEG-7 audio descriptors are part of
many state-of-the-art audio retrieval systems [Kim, 04],
[Benetos, 05], and [Xiong, 03].
MPEG-7 audio descriptors are applicable to different types of sound, such
as music, speech and environmental sounds.
There are only few studies that address quantitative data analysis of
content-based descriptions. We investigate low-level MPEG-7 visual descriptors
from a statistical point of view in [Eidenberger, 04].
We employ statistical moments, factor analysis, and cluster analysis in
the study. The investigation reveals that the visual descriptors are highly
dependent on each other.
Most investigations apply features to a specific problem without a preceding
data analysis. Such studies evaluate the quality of features empirically
by performance measures such as recall and precision. A popular application
domain is music information retrieval. In [Benetos, 05]
the authors combine MPEG-7 descriptors with other common audio features
for musical instrument classification. The investigation comprises of MPEG-7
BasicSpectral descriptors (ASE, ASC, ASS, and ASF) and SpectralBasis
descriptors (ASB and ASP). Furthermore, SC and AH descriptors are incorporated.
Additionally, the authors employ non-MPEG-7 features such as MFCCs, ZCR,
and Spectral Rolloff Frequency [Kim, 05]. After feature
extraction different classifiers (HMM, GMM and non-negative matrix factorization)
predict the class membership in terms of six different classes of instruments.
MPEG-7 audio descriptors for instrument characterization are surveyed
in [Casey, 01] and [Peeters, 00]
as well. The authors investigate the quality of high-level descriptors
(HarmonicInstrumentType and PercussiveInstrumentType) for
the distinction of instrument sounds. They show that the combination of
the low-level descriptors contained in the high-level description schemes
enable successful similarity matching in a musical sounds database. The
authors of [Allamanche, 01] present an MPEG-7 supported
audio identification system that is robust with respect to common types
of signal alterations. A flatness measure, similar to the MPEG-7 ASF descriptor
is employed for similarity matching in a database of songs.
There are multiple investigations that deal with more general sounds
such as environmental sounds. In [Casey, 01] the author
presents the MPEG-7 sound recognition tools for general purpose sound recognition.
The system employs the ASB and ASP descriptors. The author discusses the
corresponding high-level description scheme (SoundModel) which includes
continuous HMMs for classification. The authors of [Kim,
05], and [Kim, 04] extensively investigate the
quality of the MPEG-7 sound recognition tools in different application
domains. The authors perform speaker recognition and audio segmentation
of speech and non-speech segments. Furthermore, they perform general sound
classification of selected environmental sounds such as "Dog",
"Bell", "Water", and "Baby". They compare
the performance of MPEG-7 techniques with the traditional MFCC approach,
originally developed for speech recognition. The MPEG-7 system employs
ASB and ASP descriptors to represent the audio samples. Classification
is performed by continuous HMMs. The investigations show that MPEG-7 descriptors
perform comparably to MFCCs. However, MFCCs outperform ASB and ASP in some
applications. A similar investigation is presented in [Xiong,
03]. The authors compare the widely used MFCC audio features to the
low-level MPEG-7 descriptors designed for audio retrieval. Classification
is performed with two types of HMMs (Maximum Likelihood HMM and Entropic
Prior HMM). Again, MPEG-7 descriptors perform comparably to MFCCs. In [Wang,
03] the authors present an MPEG-7-based retrieval system for environmental
sounds. The system applies ASC, ASS and ASF to the description of audio
samples. The samples are organized in classes, such as "doorbell,"
"laughing," "knock", and "dog barks". One
HMM represents one particular class.
The MPEG-7 standard defines a set of high-level description schemes
in addition to the low-level description tools. An overview is given in
[Quackenbush, 01]. The authors present MPEG-7 applications,
such as Query by Humming, Audio Editing and application specific high-level
description schemes (e.g. MusicalInstrumentTimbre, SpokenContent, and Melody).
In this paper we perform an extensive statistical data analysis of low-level
MPEG-7 descriptions in the domain of environmental sounds. We analyze correlations
of MPEG-7 descriptors with other state-of-the-art audio features. Statistical
data analysis allows us to identify features that are complementary to
MPEG-7 LLDs. We extend data analysis by introducing a novel measure for
The objective of the experiments is the analysis of the correlations
in a large set of features. For this purpose, we employ Principal Components
Analysis, which reveals low redundancy between most of the MPEG-7 descriptor
groups. However, there is high redundancy within some groups of descriptors
such as the BasicSpectral group and the TimbralSpectral group.
Redundant features capture similar properties of the media objects and
should not be used in conjunction. We discuss the quality of MPEG-7 high-level
description tools based on the results of the analysis of the LLDs. The
entropy of the LLDs is generally high (in average 6 bit per byte) which
is significantly more than for the visual descriptors we surveyed in [Eidenberger,
The authors would like to express their gratitude to Professor Christian
Breitenender for his guidance. We want to thank Professor Sikora from the
Technical University of Berlin for providing us with the MPEG-7 LLD extractor.
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