  This paper proposes new acoustic feature signatures based on the multiscale
fractal dimension (MFD), which are robust against the diversity of
environmental sounds, for the content-based similarity search. The diversity of
sound sources and acoustic compositions is a typical feature of environmental
sounds. Several acoustic features have been proposed for environmental sounds.
Among them is the widely-used Mel-Frequency Cepstral Coefficients (MFCCs),
which describes frequency-domain features. However, in addition to these
features in the frequency domain, environmental sounds have other important
features in the time domain with various time scales. In our previous paper, we
proposed enhanced multiscale fractal dimension signature (EMFD) for
environmental sounds. This paper extends EMFD by using the kernel density
estimation method (EMFD-KDE), which results in increased stability and
robustness against small fluctuations in the parameters of sound sources.
Furthermore, it newly proposes another acoustic feature signature based on MFD,
namely very-long-range multiscale fractal dimension signature (MFD-VL). The
MFD-VL signature describes several features of the time varying envelope for
long periods of time. The descriptiveness of EMFD-KDE and MFD-VL is evaluated
through experiments on the similarity search of environmental sounds. We define
a similarity index to evaluate the performance of the similarity search. Our
evaluation shows that EMFD-KDE and MFD-VL improve the similarity index by
17.2\%.
