30,502 research outputs found
Acoustic Scene Classification
This work was supported by the Centre for Digital Music Platform (grant EP/K009559/1) and a Leadership Fellowship
(EP/G007144/1) both from the United Kingdom Engineering and Physical Sciences Research Council
A Compact and Discriminative Feature Based on Auditory Summary Statistics for Acoustic Scene Classification
One of the biggest challenges of acoustic scene classification (ASC) is to
find proper features to better represent and characterize environmental sounds.
Environmental sounds generally involve more sound sources while exhibiting less
structure in temporal spectral representations. However, the background of an
acoustic scene exhibits temporal homogeneity in acoustic properties, suggesting
it could be characterized by distribution statistics rather than temporal
details. In this work, we investigated using auditory summary statistics as the
feature for ASC tasks. The inspiration comes from a recent neuroscience study,
which shows the human auditory system tends to perceive sound textures through
time-averaged statistics. Based on these statistics, we further proposed to use
linear discriminant analysis to eliminate redundancies among these statistics
while keeping the discriminative information, providing an extreme com-pact
representation for acoustic scenes. Experimental results show the outstanding
performance of the proposed feature over the conventional handcrafted features.Comment: Accepted as a conference paper of Interspeech 201
Learning sound representations using trainable COPE feature extractors
Sound analysis research has mainly been focused on speech and music
processing. The deployed methodologies are not suitable for analysis of sounds
with varying background noise, in many cases with very low signal-to-noise
ratio (SNR). In this paper, we present a method for the detection of patterns
of interest in audio signals. We propose novel trainable feature extractors,
which we call COPE (Combination of Peaks of Energy). The structure of a COPE
feature extractor is determined using a single prototype sound pattern in an
automatic configuration process, which is a type of representation learning. We
construct a set of COPE feature extractors, configured on a number of training
patterns. Then we take their responses to build feature vectors that we use in
combination with a classifier to detect and classify patterns of interest in
audio signals. We carried out experiments on four public data sets: MIVIA audio
events, MIVIA road events, ESC-10 and TU Dortmund data sets. The results that
we achieved (recognition rate equal to 91.71% on the MIVIA audio events, 94% on
the MIVIA road events, 81.25% on the ESC-10 and 94.27% on the TU Dortmund)
demonstrate the effectiveness of the proposed method and are higher than the
ones obtained by other existing approaches. The COPE feature extractors have
high robustness to variations of SNR. Real-time performance is achieved even
when the value of a large number of features is computed.Comment: Accepted for publication in Pattern Recognitio
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
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